27-02-2011, 09:14 PM
HEY PLZ SND ME SUM MORE INF ON DIS TOPIC
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BRAIN COMPUTER INTERFACE A SEMINAR REPORT
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01-03-2011, 04:42 PM
[attachment=9308]
Abstract:
Brain computer interface research has targeted repairing damaged sight providing functionality to paralysed people .Researchers have built devices to interface with neural cells and entire neural network in cultures outside animals.One of the most exciting areas of BCI research is the development of devices that can be controlled by thoughts.Once the basic mechanism of converting thoughts to computerized action is perfected, the potential uses for the technology are almost limitless.Instead of a robotic hand disabled users could have robotic braces attached to their limbs allowing them to move directly and interact with environment.
Introduction:
A brain-computer interface (BCI), sometimes called a direct neural interface or a brain-machine interface, is a direct communication pathway between a human or animal brain (or brain cell culture ) and an external device. In one-way BCIs, computers either accept commands from the brain or send signals to it (for example, to restore vision) but not both. Two-way BCI s would allow brains and external devices to exchange information in both directions but have yet to be successfully implanted in animals or humans.
In this definition, the word brain means the brain or nervous system of an organic life form rather than the mind. Computer means any processing or computational device, from simple circuits to silicon chips (including hypothetical future technologies such as quantum computing).
Neuroprosthetics is an area of neuroscience concerned with neural prostheses—using artificial devices to replace the function of impaired nervous systems or sensory organs. The most widely used neuroprosthetic device is the cochlear implant, which was implanted in approximately 100,000 people worldwide as of 2006. There are also several neuroprosthetic devices that aim to restore vision, including retinal implants, although this article only discusses implants directly into the brain.
Neuroprosthetics and BCI seek to achieve the same aims, such as restoring sight, hearing, movement, ability to communicate, and even cognitive function. Both use similar experimental methods and surgical techniques.
Types of BCI :
Invasive BCI :Invasive BCI research has targeted repairing damaged sight and providing new functionality to paralysed people. Invasive BCIs are implanted directly into the grey matter of the brain during neurosurgery. As they rest in the grey matter, invasive devices produce the highest quality signals of BCI devices but are prone to scar-tissue build-up, causing the signal to become weaker or even lost as the body reacts to a foreign object in the brain. In vision science, direct brain implants have been used to treat non-congential (acquired) blindness.
prototype was implanted into "Jerry," a man blinded in adulthood, in 1978. A single-array BCI containing 68 electrodes was implanted onto Jerry’s visual cortex and succeeded in producing phosphenes, the sensation of seeing light. The system included cameras mounted on glasses to send signals to the implant. Initially, the implant allowed Jerry to see shades of grey in a limited field of vision at a low frame-rate. This also required him to be hooked up to a two-ton mainframe, but shrinking electronics and faster computers made his artificial eye more portable and now enable him to perform simple tasks unassisted. Partially-invasive BCIs : Partially invasive BCI devices are implanted inside the skull but rest outside the brain rather than amidst the grey matter. They produce better resolution signals than non-invasive
BCIs where the bone tissue of the cranium deflects and deforms signals and have a lower risk of forming scar-tissue in the brain than fully-invasive BCIs.
ECoG is a very promising intermediate BCI modality because it has higher spatial resolution, better signal-to-noise ratio, wider frequency range, and lesser training requirements than scalp-recorded EEG, and at the same time has lower technical difficulty, lower clinical risk, and probably superior long-term stability than intracortical single-neuron recording. This feature profile and recent evidence of the high level of control with minimal training requirements shows potential for real world application for people with motor disabilities.
Non-invasive BCIs :As well as invasive experiments, there have also been experiments in humans using non-invasive neuroimaging technologies as interfaces. Signals recorded in this way have been used to power muscle implants and restore partial movement in an experimental volunteer. Although they are easy to wear, non-invasive implants produce poor signal resolution because the skull dampens signals, dispersing and blurring the electromagnetic waves created by the neurons. Although the waves can still be detected it is more difficult to determine the area of the brain that created them or the actions of individual neurons.
Abstract:
Brain computer interface research has targeted repairing damaged sight providing functionality to paralysed people .Researchers have built devices to interface with neural cells and entire neural network in cultures outside animals.One of the most exciting areas of BCI research is the development of devices that can be controlled by thoughts.Once the basic mechanism of converting thoughts to computerized action is perfected, the potential uses for the technology are almost limitless.Instead of a robotic hand disabled users could have robotic braces attached to their limbs allowing them to move directly and interact with environment.
Introduction:
A brain-computer interface (BCI), sometimes called a direct neural interface or a brain-machine interface, is a direct communication pathway between a human or animal brain (or brain cell culture ) and an external device. In one-way BCIs, computers either accept commands from the brain or send signals to it (for example, to restore vision) but not both. Two-way BCI s would allow brains and external devices to exchange information in both directions but have yet to be successfully implanted in animals or humans.
In this definition, the word brain means the brain or nervous system of an organic life form rather than the mind. Computer means any processing or computational device, from simple circuits to silicon chips (including hypothetical future technologies such as quantum computing).
Neuroprosthetics is an area of neuroscience concerned with neural prostheses—using artificial devices to replace the function of impaired nervous systems or sensory organs. The most widely used neuroprosthetic device is the cochlear implant, which was implanted in approximately 100,000 people worldwide as of 2006. There are also several neuroprosthetic devices that aim to restore vision, including retinal implants, although this article only discusses implants directly into the brain.
Neuroprosthetics and BCI seek to achieve the same aims, such as restoring sight, hearing, movement, ability to communicate, and even cognitive function. Both use similar experimental methods and surgical techniques.
Types of BCI :
Invasive BCI :Invasive BCI research has targeted repairing damaged sight and providing new functionality to paralysed people. Invasive BCIs are implanted directly into the grey matter of the brain during neurosurgery. As they rest in the grey matter, invasive devices produce the highest quality signals of BCI devices but are prone to scar-tissue build-up, causing the signal to become weaker or even lost as the body reacts to a foreign object in the brain. In vision science, direct brain implants have been used to treat non-congential (acquired) blindness.
prototype was implanted into "Jerry," a man blinded in adulthood, in 1978. A single-array BCI containing 68 electrodes was implanted onto Jerry’s visual cortex and succeeded in producing phosphenes, the sensation of seeing light. The system included cameras mounted on glasses to send signals to the implant. Initially, the implant allowed Jerry to see shades of grey in a limited field of vision at a low frame-rate. This also required him to be hooked up to a two-ton mainframe, but shrinking electronics and faster computers made his artificial eye more portable and now enable him to perform simple tasks unassisted. Partially-invasive BCIs : Partially invasive BCI devices are implanted inside the skull but rest outside the brain rather than amidst the grey matter. They produce better resolution signals than non-invasive
BCIs where the bone tissue of the cranium deflects and deforms signals and have a lower risk of forming scar-tissue in the brain than fully-invasive BCIs.
ECoG is a very promising intermediate BCI modality because it has higher spatial resolution, better signal-to-noise ratio, wider frequency range, and lesser training requirements than scalp-recorded EEG, and at the same time has lower technical difficulty, lower clinical risk, and probably superior long-term stability than intracortical single-neuron recording. This feature profile and recent evidence of the high level of control with minimal training requirements shows potential for real world application for people with motor disabilities.
Non-invasive BCIs :As well as invasive experiments, there have also been experiments in humans using non-invasive neuroimaging technologies as interfaces. Signals recorded in this way have been used to power muscle implants and restore partial movement in an experimental volunteer. Although they are easy to wear, non-invasive implants produce poor signal resolution because the skull dampens signals, dispersing and blurring the electromagnetic waves created by the neurons. Although the waves can still be detected it is more difficult to determine the area of the brain that created them or the actions of individual neurons.
02-03-2011, 02:52 PM
[attachment=9366]
What is an EEG?
An electroencephalogram is a measure of the brain's voltage fluctuations as detected from scalp electrodes.
It is an approximation of the cumulative electrical activity of neurons.
What is it good for?
Neurofeedback
treating ADHD
guiding meditation
Brain Computer Interfaces
People with little muscle control (i.e. not enough control for EMG or gaze tracking)
People with ALS, spinal injuries
High Precision
Low bandwidth (bit rate)
EEG Background
1875 - Richard Caton discovered electrical properties of exposed cerebral hemispheres of rabbits and monkeys.
1924 - German Psychiatrist Hans Berger discovered alpha waves in humans and invented the term “electroencephalogram”
1950s - Walter Grey Walter developed “EEG topography” - mapping electrical activity of the brain.
Physical Mechanisms
EEGs require electrodes attached to the scalp with sticky gel
Require physical connection to the machine
Electrode Placement
Standard “10-20 System”
Spaced apart 10-20%
Letter for region
F - Frontal Lobe
T - Temporal Lobe
C - Center
O - Occipital Lobe
Number for exact position
Odd numbers - left
Even numbers - right
Electrode Placement
A more detailed view:
Brain “Features”
User must be able to control the output:
use a feature of the continuous EEG output that the user can reliably modify (waves), or
evoke an EEG response with an external stimulus (evoked potential)
Continuous Brain Waves
Generally grouped by frequency: (amplitudes are about 100µV max)
Brain Waves Transformations
wave-form averaging over several trials
auto-adjustment with a known signal
Fourier transforms to detect relative amplitude at different frequencies
Alpha and Beta Waves
Studied since 1920s
Found in Parietal and Frontal Cortex
Relaxed - Alpha has high amplitude
Excited - Beta has high amplitude
So, Relaxed -> Excited
means Alpha -> Beta
Mu Waves
Studied since 1930s
Found in Motor Cortex
Amplitude suppressed by Physical Movements, or intent to move physically
(Wolpaw, et al 1991) trained subjects to control the mu rhythm by visualizing motor tasks to move a cursor up and down (1D)
Mu Waves
Mu and Beta Waves
(Wolpaw and McFarland 2004) used a linear combination of Mu and Beta waves to control a 2D cursor.
Weights were learned from the users in real time.
Cursor moved every 50ms (20 Hz)
92% “hit rate” in average 1.9 sec
Mu and Beta Waves
Movie!
Mu and Beta Waves
How do you handle more complex tasks?
Finite Automata, such as this from (Millán et al, 2004)
P300 (Evoked Potentials)
occurs in response to a significant but low-probability event
300 milliseconds after the onset of the target stimulus
found in 1965 by (Sutton et al., 1965; Walter, 1965)
focus specific
P300 Experiments
(Farwell and Donchin 1988)
95% accuracy at 1 character per 26s
P300 (Evoked Potentials)
(Polikoff, et al 1995) allowed users to control a cursor by flashing control points in 4 different directions
Each sample took 4 seconds
Threw out samples masked by muscle movements (such as blinks)
(Polikoff, et al 1995) Results
50% accuracy at ~1/4 Hz
80% accuracy at ~1/30 Hz
VEP - Visual Evoked Potential
Detects changes in the visual cortex
Similar in use to P300
Close to the scalp
Model Generalization (time)
EEG models so far haven’t adjusted to fit the changing nature of the user.
(Curran et al 2004) have proposed using Adaptive Filtering algorithms to deal with this.
Model Generalization (users)
Many manual adjustments still must be made for each person (such as EEG placement)
Currently, users have to adapt to the system rather than the system adapting to the users.
Current techniques learn a separate model for each user.
Model Generalization (users)
(Müller 2004) applied typical machine learning techniques to reduce the need for training data.
Support Vector Machines (SVM) and Regularized Linear Discriminant Analysis (RLDA)
This is only the beginning of applying machine learning to BCIs!
BCI Examples - Communication
Farwell and Donchin (1988) allowed the user to select a command by looking for P300 signals when the desired command flashed
BCI Examples - Prostheses
(Wolpaw and McFarland 2004) allowed a user to move a cursor around a 2 dimensional screen
(Millán, et al. 2004) allowed a user to move a robot around the room.
BCI Examples - Music
1987 - Lusted and Knapp demonstrated an EEG controlling a music synthesizer in real time.
Atau Tanaka (Stanford Center for Computer Research in Music and Acoustics) uses it in performances to switch synthesizer functions while generating sound using EMG.
In Review…
Brain Computer Interfaces
Allow those with poor muscle control to communicate and control physical devices
High Precision (can be used reliably)
Requires somewhat invasive sensors
Requires extensive training (poor generalization)
Low bandwidth (today 24 bits/minute, or at most 5 characters/minute)
Future Work
Improving physical methods for gathering EEGs
Improving generalization
Improving knowledge of how to interpret waves (not just the “new phrenology”)
References
http://cs.man.ac.uk/aig/staff/toby/resea...erface.txt
http://icadwebsiteV2.0/Conferences/ICAD2...t_call.htm
http://faculty.washington.edu/chudler/1020.html
http://biocontroleeg.html
http://asel.udel.edu/speech/Spch_proc/eeg.html
Toward a P300-based Computer Interface
James B. Polikoff, H. Timothy Bunnell, & Winslow J. Borkowski Jr. Applied Science and Engineering Laboratories Alfred I. Dupont Institute
Various papers from PASCAL 2004
Original Paper on Evoked Potential:
Invasive BCIs
Have traditionally provided much finer control than non-invasive EEGs (no longer true?)
May have ethical/practical issues
(Chapin et al. 1999) trained rats to control a “robot arm” to fetch water
(Wessberg et al. 2000) allowed primates to accurately control a robot arm in 3 dimensions in real time.
What is an EEG?
An electroencephalogram is a measure of the brain's voltage fluctuations as detected from scalp electrodes.
It is an approximation of the cumulative electrical activity of neurons.
What is it good for?
Neurofeedback
treating ADHD
guiding meditation
Brain Computer Interfaces
People with little muscle control (i.e. not enough control for EMG or gaze tracking)
People with ALS, spinal injuries
High Precision
Low bandwidth (bit rate)
EEG Background
1875 - Richard Caton discovered electrical properties of exposed cerebral hemispheres of rabbits and monkeys.
1924 - German Psychiatrist Hans Berger discovered alpha waves in humans and invented the term “electroencephalogram”
1950s - Walter Grey Walter developed “EEG topography” - mapping electrical activity of the brain.
Physical Mechanisms
EEGs require electrodes attached to the scalp with sticky gel
Require physical connection to the machine
Electrode Placement
Standard “10-20 System”
Spaced apart 10-20%
Letter for region
F - Frontal Lobe
T - Temporal Lobe
C - Center
O - Occipital Lobe
Number for exact position
Odd numbers - left
Even numbers - right
Electrode Placement
A more detailed view:
Brain “Features”
User must be able to control the output:
use a feature of the continuous EEG output that the user can reliably modify (waves), or
evoke an EEG response with an external stimulus (evoked potential)
Continuous Brain Waves
Generally grouped by frequency: (amplitudes are about 100µV max)
Brain Waves Transformations
wave-form averaging over several trials
auto-adjustment with a known signal
Fourier transforms to detect relative amplitude at different frequencies
Alpha and Beta Waves
Studied since 1920s
Found in Parietal and Frontal Cortex
Relaxed - Alpha has high amplitude
Excited - Beta has high amplitude
So, Relaxed -> Excited
means Alpha -> Beta
Mu Waves
Studied since 1930s
Found in Motor Cortex
Amplitude suppressed by Physical Movements, or intent to move physically
(Wolpaw, et al 1991) trained subjects to control the mu rhythm by visualizing motor tasks to move a cursor up and down (1D)
Mu Waves
Mu and Beta Waves
(Wolpaw and McFarland 2004) used a linear combination of Mu and Beta waves to control a 2D cursor.
Weights were learned from the users in real time.
Cursor moved every 50ms (20 Hz)
92% “hit rate” in average 1.9 sec
Mu and Beta Waves
Movie!
Mu and Beta Waves
How do you handle more complex tasks?
Finite Automata, such as this from (Millán et al, 2004)
P300 (Evoked Potentials)
occurs in response to a significant but low-probability event
300 milliseconds after the onset of the target stimulus
found in 1965 by (Sutton et al., 1965; Walter, 1965)
focus specific
P300 Experiments
(Farwell and Donchin 1988)
95% accuracy at 1 character per 26s
P300 (Evoked Potentials)
(Polikoff, et al 1995) allowed users to control a cursor by flashing control points in 4 different directions
Each sample took 4 seconds
Threw out samples masked by muscle movements (such as blinks)
(Polikoff, et al 1995) Results
50% accuracy at ~1/4 Hz
80% accuracy at ~1/30 Hz
VEP - Visual Evoked Potential
Detects changes in the visual cortex
Similar in use to P300
Close to the scalp
Model Generalization (time)
EEG models so far haven’t adjusted to fit the changing nature of the user.
(Curran et al 2004) have proposed using Adaptive Filtering algorithms to deal with this.
Model Generalization (users)
Many manual adjustments still must be made for each person (such as EEG placement)
Currently, users have to adapt to the system rather than the system adapting to the users.
Current techniques learn a separate model for each user.
Model Generalization (users)
(Müller 2004) applied typical machine learning techniques to reduce the need for training data.
Support Vector Machines (SVM) and Regularized Linear Discriminant Analysis (RLDA)
This is only the beginning of applying machine learning to BCIs!
BCI Examples - Communication
Farwell and Donchin (1988) allowed the user to select a command by looking for P300 signals when the desired command flashed
BCI Examples - Prostheses
(Wolpaw and McFarland 2004) allowed a user to move a cursor around a 2 dimensional screen
(Millán, et al. 2004) allowed a user to move a robot around the room.
BCI Examples - Music
1987 - Lusted and Knapp demonstrated an EEG controlling a music synthesizer in real time.
Atau Tanaka (Stanford Center for Computer Research in Music and Acoustics) uses it in performances to switch synthesizer functions while generating sound using EMG.
In Review…
Brain Computer Interfaces
Allow those with poor muscle control to communicate and control physical devices
High Precision (can be used reliably)
Requires somewhat invasive sensors
Requires extensive training (poor generalization)
Low bandwidth (today 24 bits/minute, or at most 5 characters/minute)
Future Work
Improving physical methods for gathering EEGs
Improving generalization
Improving knowledge of how to interpret waves (not just the “new phrenology”)
References
http://cs.man.ac.uk/aig/staff/toby/resea...erface.txt
http://icadwebsiteV2.0/Conferences/ICAD2...t_call.htm
http://faculty.washington.edu/chudler/1020.html
http://biocontroleeg.html
http://asel.udel.edu/speech/Spch_proc/eeg.html
Toward a P300-based Computer Interface
James B. Polikoff, H. Timothy Bunnell, & Winslow J. Borkowski Jr. Applied Science and Engineering Laboratories Alfred I. Dupont Institute
Various papers from PASCAL 2004
Original Paper on Evoked Potential:
Invasive BCIs
Have traditionally provided much finer control than non-invasive EEGs (no longer true?)
May have ethical/practical issues
(Chapin et al. 1999) trained rats to control a “robot arm” to fetch water
(Wessberg et al. 2000) allowed primates to accurately control a robot arm in 3 dimensions in real time.
09-03-2011, 09:23 AM
[attachment=9820]
Brain Computer Interface
What is BCI?
Brain -Computer Interface
-Direct Neural Interface or Brain-Machine Interface
Direct communication pathway between a brain and an external device.
Research on BCIs began in the 1970s at the University of California Los Angeles
How does it work?
Signal Acquisition
Signal Processing
Devices
Brain Wave Control
Active
◦ α (8 – 12 Hz): relaxed/reflecting
◦ β (12 – 30 Hz): alert/working
- Training
- Misjudgment
Passive
◦ Evoked potentials
Passive
◦ Evoked potentials
Passive
◦ Evoked potentials
Simple introduction of the brain
Focus on cortex
Structure of the cortex
Cortex
Primary Somatosensory Cortex
Primary Motor Cortex
Motor Association Cortex
Sensory Association Area
Visual Association Area
Visual Cortex
Wernicke's Area
Prefrontal Cortex
Speech Center (Broca‘s Area)
Auditory Cortex
Auditory Association Area
Data Acquisition
Invasive BCIs
Non-Invasive BCIs
Partially-Invasive BCIs
Wireless BCIs
Invasive BCIs
Implanted: grey matter
Signals: highest quality
Scar-tissue build-up
Target:
◦ repairing damaged sight
◦ providing new functionality to persons with paralysis
Artificial Vision System
Electrode Arrays
App.- Artificial Vision
Non-Invasive BCIs
poor signal resolution
power muscle implants and restore partial movement
Interfaces
◦ EEG
◦ MEG
◦ MRI
MEG
Magnetoencephalography
Magnetic Field: 10-15T ~10-13T (Weak!!)
◦ S.Q.U.I.D. Sensors
◦ Shielded Room
Magnetic Field
Partially-Invasive BCIs
Implanted: skull
lower risk of forming scar-tissue in the brain
Signal quality between invasive BCIs & non-invasive BCIs
Wireless BCIs
More practical
Embedding multiple chips
◦ More complicated thoughts
Transmission with RF
key requirement: keep the heat down
Examples of BCI
Rats implanted with BCIs in Theodore Berger’s experiments
Monkey operating a robotic arm with BCIs
arrangments
Disadvantages
Headache
Exhausting
Laziness Degenerate
Future
Integrate with different territory
From lab to factory
Nursing and medical treatment
Brain Computer Interface
What is BCI?
Brain -Computer Interface
-Direct Neural Interface or Brain-Machine Interface
Direct communication pathway between a brain and an external device.
Research on BCIs began in the 1970s at the University of California Los Angeles
How does it work?
Signal Acquisition
Signal Processing
Devices
Brain Wave Control
Active
◦ α (8 – 12 Hz): relaxed/reflecting
◦ β (12 – 30 Hz): alert/working
- Training
- Misjudgment
Passive
◦ Evoked potentials
Passive
◦ Evoked potentials
Passive
◦ Evoked potentials
Simple introduction of the brain
Focus on cortex
Structure of the cortex
Cortex
Primary Somatosensory Cortex
Primary Motor Cortex
Motor Association Cortex
Sensory Association Area
Visual Association Area
Visual Cortex
Wernicke's Area
Prefrontal Cortex
Speech Center (Broca‘s Area)
Auditory Cortex
Auditory Association Area
Data Acquisition
Invasive BCIs
Non-Invasive BCIs
Partially-Invasive BCIs
Wireless BCIs
Invasive BCIs
Implanted: grey matter
Signals: highest quality
Scar-tissue build-up
Target:
◦ repairing damaged sight
◦ providing new functionality to persons with paralysis
Artificial Vision System
Electrode Arrays
App.- Artificial Vision
Non-Invasive BCIs
poor signal resolution
power muscle implants and restore partial movement
Interfaces
◦ EEG
◦ MEG
◦ MRI
MEG
Magnetoencephalography
Magnetic Field: 10-15T ~10-13T (Weak!!)
◦ S.Q.U.I.D. Sensors
◦ Shielded Room
Magnetic Field
Partially-Invasive BCIs
Implanted: skull
lower risk of forming scar-tissue in the brain
Signal quality between invasive BCIs & non-invasive BCIs
Wireless BCIs
More practical
Embedding multiple chips
◦ More complicated thoughts
Transmission with RF
key requirement: keep the heat down
Examples of BCI
Rats implanted with BCIs in Theodore Berger’s experiments
Monkey operating a robotic arm with BCIs
arrangments
Disadvantages
Headache
Exhausting
Laziness Degenerate
Future
Integrate with different territory
From lab to factory
Nursing and medical treatment
12-03-2011, 10:31 AM
[attachment=10052]
Brain Computer Interfaces
What is an EEG?
An electroencephalogram is a measure of the brain's voltage fluctuations as detected from scalp electrodes.
It is an approximation of the cumulative electrical activity of neurons.
What is it good for?
Neurofeedback
treating ADHD
guiding meditation
Brain Computer Interfaces
People with little muscle control (i.e. not enough control for EMG or gaze tracking)
People with ALS, spinal injuries
High Precision
Low bandwidth (bit rate)
EEG Background
1875 - Richard Caton discovered electrical properties of exposed cerebral hemispheres of rabbits and monkeys.
1924 - German Psychiatrist Hans Berger discovered alpha waves in humans and invented the term “electroencephalogram”
1950s - Walter Grey Walter developed “EEG topography” - mapping electrical activity of the brain.
Physical Mechanisms
EEGs require electrodes attached to the scalp with sticky gel
Require physical connection to the machine
Electrode Placement
Standard “10-20 System”
Spaced apart 10-20%
Letter for region
F - Frontal Lobe
T - Temporal Lobe
C - Center
O - Occipital Lobe
Number for exact position
Odd numbers - left
Even numbers - right
Electrode Placement
A more detailed view:
Brain “Features”
User must be able to control the output:
use a feature of the continuous EEG output that the user can reliably modify (waves), or
evoke an EEG response with an external stimulus (evoked potential)
Continuous Brain Waves
Generally grouped by frequency: (amplitudes are about 100µV max)
Brain Waves Transformations
wave-form averaging over several trials
auto-adjustment with a known signal
Fourier transforms to detect relative amplitude at different frequencies
Alpha and Beta Waves
Studied since 1920s
Found in Parietal and Frontal Cortex
Relaxed - Alpha has high amplitude
Excited - Beta has high amplitude
So, Relaxed -> Excited
means Alpha -> Beta
Mu Waves
Studied since 1930s
Found in Motor Cortex
Amplitude suppressed by Physical Movements, or intent to move physically
(Wolpaw, et al 1991) trained subjects to control the mu rhythm by visualizing motor tasks to move a cursor up and down (1D)
Mu Waves
Mu and Beta Waves
(Wolpaw and McFarland 2004) used a linear combination of Mu and Beta waves to control a 2D cursor.
Weights were learned from the users in real time.
Cursor moved every 50ms (20 Hz)
92% “hit rate” in average 1.9 sec
Mu and Beta Waves
Movie!
Mu and Beta Waves
How do you handle more complex tasks?
Finite Automata, such as this from (Millán et al, 2004)
P300 (Evoked Potentials)
occurs in response to a significant but low-probability event
300 milliseconds after the onset of the target stimulus
found in 1965 by (Sutton et al., 1965; Walter, 1965)
focus specific
P300 Experiments
(Farwell and Donchin 1988)
95% accuracy at 1 character per 26s
P300 (Evoked Potentials)
(Polikoff, et al 1995) allowed users to control a cursor by flashing control points in 4 different directions
Each sample took 4 seconds
Threw out samples masked by muscle movements (such as blinks)
(Polikoff, et al 1995) Results
50% accuracy at ~1/4 Hz
80% accuracy at ~1/30 Hz
VEP - Visual Evoked Potential
Detects changes in the visual cortex
Similar in use to P300
Close to the scalp
Model Generalization (time)
EEG models so far haven’t adjusted to fit the changing nature of the user.
(Curran et al 2004) have proposed using Adaptive Filtering algorithms to deal with this.
Model Generalization (users)
Many manual adjustments still must be made for each person (such as EEG placement)
Currently, users have to adapt to the system rather than the system adapting to the users.
Current techniques learn a separate model for each user.
(Müller 2004) applied typical machine learning techniques to reduce the need for training data.
Support Vector Machines (SVM) and Regularized Linear Discriminant Analysis (RLDA)
This is only the beginning of applying machine learning to BCIs!
BCI Examples - Communication
Farwell and Donchin (1988) allowed the user to select a command by looking for P300 signals when the desired command flashed
BCI Examples - Prostheses
(Wolpaw and McFarland 2004) allowed a user to move a cursor around a 2 dimensional screen
(Millán, et al. 2004) allowed a user to move a robot around the room.
BCI Examples - Music
1987 - Lusted and Knapp demonstrated an EEG controlling a music synthesizer in real time.
Atau Tanaka (Stanford Center for Computer Research in Music and Acoustics) uses it in performances to switch synthesizer functions while generating sound using EMG.
In Review…
Brain Computer Interfaces
Allow those with poor muscle control to communicate and control physical devices
High Precision (can be used reliably)
Requires somewhat invasive sensors
Requires extensive training (poor generalization)
Low bandwidth (today 24 bits/minute, or at most 5 characters/minute)
Future Work
Improving physical methods for gathering EEGs
Improving generalization
Improving knowledge of how to interpret waves (not just the “new phrenology”)
Invasive BCIs
Have traditionally provided much finer control than non-invasive EEGs (no longer true?)
May have ethical/practical issues
(Chapin et al. 1999) trained rats to control a “robot arm” to fetch water
(Wessberg et al. 2000) allowed primates to accurately control a robot arm in 3 dimensions in real time.
Brain Computer Interfaces
What is an EEG?
An electroencephalogram is a measure of the brain's voltage fluctuations as detected from scalp electrodes.
It is an approximation of the cumulative electrical activity of neurons.
What is it good for?
Neurofeedback
treating ADHD
guiding meditation
Brain Computer Interfaces
People with little muscle control (i.e. not enough control for EMG or gaze tracking)
People with ALS, spinal injuries
High Precision
Low bandwidth (bit rate)
EEG Background
1875 - Richard Caton discovered electrical properties of exposed cerebral hemispheres of rabbits and monkeys.
1924 - German Psychiatrist Hans Berger discovered alpha waves in humans and invented the term “electroencephalogram”
1950s - Walter Grey Walter developed “EEG topography” - mapping electrical activity of the brain.
Physical Mechanisms
EEGs require electrodes attached to the scalp with sticky gel
Require physical connection to the machine
Electrode Placement
Standard “10-20 System”
Spaced apart 10-20%
Letter for region
F - Frontal Lobe
T - Temporal Lobe
C - Center
O - Occipital Lobe
Number for exact position
Odd numbers - left
Even numbers - right
Electrode Placement
A more detailed view:
Brain “Features”
User must be able to control the output:
use a feature of the continuous EEG output that the user can reliably modify (waves), or
evoke an EEG response with an external stimulus (evoked potential)
Continuous Brain Waves
Generally grouped by frequency: (amplitudes are about 100µV max)
Brain Waves Transformations
wave-form averaging over several trials
auto-adjustment with a known signal
Fourier transforms to detect relative amplitude at different frequencies
Alpha and Beta Waves
Studied since 1920s
Found in Parietal and Frontal Cortex
Relaxed - Alpha has high amplitude
Excited - Beta has high amplitude
So, Relaxed -> Excited
means Alpha -> Beta
Mu Waves
Studied since 1930s
Found in Motor Cortex
Amplitude suppressed by Physical Movements, or intent to move physically
(Wolpaw, et al 1991) trained subjects to control the mu rhythm by visualizing motor tasks to move a cursor up and down (1D)
Mu Waves
Mu and Beta Waves
(Wolpaw and McFarland 2004) used a linear combination of Mu and Beta waves to control a 2D cursor.
Weights were learned from the users in real time.
Cursor moved every 50ms (20 Hz)
92% “hit rate” in average 1.9 sec
Mu and Beta Waves
Movie!
Mu and Beta Waves
How do you handle more complex tasks?
Finite Automata, such as this from (Millán et al, 2004)
P300 (Evoked Potentials)
occurs in response to a significant but low-probability event
300 milliseconds after the onset of the target stimulus
found in 1965 by (Sutton et al., 1965; Walter, 1965)
focus specific
P300 Experiments
(Farwell and Donchin 1988)
95% accuracy at 1 character per 26s
P300 (Evoked Potentials)
(Polikoff, et al 1995) allowed users to control a cursor by flashing control points in 4 different directions
Each sample took 4 seconds
Threw out samples masked by muscle movements (such as blinks)
(Polikoff, et al 1995) Results
50% accuracy at ~1/4 Hz
80% accuracy at ~1/30 Hz
VEP - Visual Evoked Potential
Detects changes in the visual cortex
Similar in use to P300
Close to the scalp
Model Generalization (time)
EEG models so far haven’t adjusted to fit the changing nature of the user.
(Curran et al 2004) have proposed using Adaptive Filtering algorithms to deal with this.
Model Generalization (users)
Many manual adjustments still must be made for each person (such as EEG placement)
Currently, users have to adapt to the system rather than the system adapting to the users.
Current techniques learn a separate model for each user.
(Müller 2004) applied typical machine learning techniques to reduce the need for training data.
Support Vector Machines (SVM) and Regularized Linear Discriminant Analysis (RLDA)
This is only the beginning of applying machine learning to BCIs!
BCI Examples - Communication
Farwell and Donchin (1988) allowed the user to select a command by looking for P300 signals when the desired command flashed
BCI Examples - Prostheses
(Wolpaw and McFarland 2004) allowed a user to move a cursor around a 2 dimensional screen
(Millán, et al. 2004) allowed a user to move a robot around the room.
BCI Examples - Music
1987 - Lusted and Knapp demonstrated an EEG controlling a music synthesizer in real time.
Atau Tanaka (Stanford Center for Computer Research in Music and Acoustics) uses it in performances to switch synthesizer functions while generating sound using EMG.
In Review…
Brain Computer Interfaces
Allow those with poor muscle control to communicate and control physical devices
High Precision (can be used reliably)
Requires somewhat invasive sensors
Requires extensive training (poor generalization)
Low bandwidth (today 24 bits/minute, or at most 5 characters/minute)
Future Work
Improving physical methods for gathering EEGs
Improving generalization
Improving knowledge of how to interpret waves (not just the “new phrenology”)
Invasive BCIs
Have traditionally provided much finer control than non-invasive EEGs (no longer true?)
May have ethical/practical issues
(Chapin et al. 1999) trained rats to control a “robot arm” to fetch water
(Wessberg et al. 2000) allowed primates to accurately control a robot arm in 3 dimensions in real time.
18-03-2011, 10:36 AM
presented by:
P . Rajeswari
[attachment=10461]
BRAIN –COMPUTER INTERFACE
Introduction to BCI
A brain–computer interface (BCI) is a direct communication pathway between a brain and an external device sometimes called a direct neural interface or a brain–machine interface.
BCIs are often aimed at: assisting, augmenting or repairing
human cognitive or sensory-motor functions
Signal Acquisition
Brain signals can be collected in different ways, one of these methods is EEG (Electroencephalography)
Mu Rhythms
In awake people, even when they are notproducing motor output, motor cortical areas
often display 8–12 Hz EEG activity (Mu Rhythm)
Movement or preparation for movement typicallycauses a decrease in mu rhythms .
• Output Device
• Any controllable machines
• For answering yes/no questions
• For word processing at slow
• Wheelchair
• Virtual Reality
• Usually, Computer screen and the output is the selection of targets or cursor movement
• Need for BCI
Developing technologies for people with disabilities:
• Assist paralyzed people to operate external devices
• Assist blind people to visualize external images without physical movement
• Decode information stored on human brain (as memory)
• Decode information from brain to display human thinking or dream on a screen
• Human BCI
Human BCI Types:
Invasive
Partially invasive
Non Invasive
• Invasive BCI
• Implanted directly into the grey matter of the brain during neurosurgery
• Targeted repairing damaged sight
• Providing new functionality to persons with paralysis
• Produce the highest quality signals
Disadvantage:
• Prone to Scalar tissue build up
• Causes signal to become weaker or even lost as the body reacts to a foreign object
• Partial Invasive BCI
• Implanted inside the skull but rest outside the brain
• Produce better resolution signals than non-invasive BCIs having a lower risk of forming scar-tissue in the brain than fully-invasive BCIs
Examples:
1.Electrocorticography (ECoG)
2.Light Reactive Imaging BCI
• Partial Invasive BCI (Continues)
Electrocorticography (ECoG)
• Measures the electrical activity of the brain taken from beneath the skull
• Electrodes are embedded in a thin plastic pad that is placed above the cortex
• First trialed in humans in 2004 by Eric Leuthardt and Daniel Moran
• Enabled a teenage boy to play Space Invaders using ECoG implant
• Controls are rapid, and requires minimal training
• Partial Invasive BCI (Continues)
Light Reactive Imaging BCI
• Involve implanting a laser inside the skull
• Laser is trained on a single neuron and the neuron's
reflectance measured by a separate sensor
• When the neuron fires, the laser light pattern and wavelengths would change
Advantages of Partial Invasive BCI
• Better signal to noise ratio
• Higher spatial ratio
• Better Frequency Range
• Non-Invasive BCI
• Recorded signal have been used to power muscle implants and
restore partial movement
• Signals are weaken as skull dampens the signal
• Although the waves are still detectable, it is hard to determine the area of the brain or the neuron that created the signal
Examples:
Electroencephalography (EEG)
Magneto encephalography (MEG)
Magnetic resonance imaging (MRI)
• Non-Invasive BCI (Continues)
Electroencephalography (EEG)
Most studied potential non-invasive interface
Fine temporal resolution
EEG in Mid1990s:
• EEG signal was used as a binary signal to
control a computer cursor
• Patients can use computer cursors by
controlling their brainwaves
• Slow – required an hour to write 100
characters
• Non-Invasive BCI (Continues)
This procedure is the first non-invasive neuroimaging technique discovered. It measures the electrical activity of the brain. Due to its ease of use, cost and high temporal resolution this method is the most widely used one in BCIs nowadays
• Non-Invasive BCI (Continues)
Magneto encephalography (MEG)
MEG is a technique for mapping brain activity byrecording magnetic fields produced by electricalcurrents occurring naturally in the brain
• By using Arrays of SQUIDs (superconducting quantum interference devices)
Application :
• Localizing the regions affected by pathology, before surgical removal
• Determining the function of various parts of the brain
• Non-Invasive BCI (Continues)
Magnetic resonance imaging (MRI)
MRI is a technique used in radiology to visualize detailed internal structures.
Functional MRI or FMRI is a type of MRI scan that measures the hemodynamic response (change in blood flow) related to neural activity in the brain or spinal cord.
Recent research in ATR (Advanced Telecommunications Research, in Kyoto, Japan) on FMRI allowed the scientists to reconstruct images directly from the brain and display them on a computer.
Advantages of BCI
1. Induced disability
2. Ease of use in hardware &software
3. Speed
4. Novelty
5. Potentially high impact technology
Conclusions
BCI field is out of the demonstrations phase and is ready for clinical applications .Any new BCI technology should be focused on improving the quality of life of the end user.
This technology enables a disabled person to carry on his work overcoming his inability.
P . Rajeswari
[attachment=10461]
BRAIN –COMPUTER INTERFACE
Introduction to BCI
A brain–computer interface (BCI) is a direct communication pathway between a brain and an external device sometimes called a direct neural interface or a brain–machine interface.
BCIs are often aimed at: assisting, augmenting or repairing
human cognitive or sensory-motor functions
Signal Acquisition
Brain signals can be collected in different ways, one of these methods is EEG (Electroencephalography)
Mu Rhythms
In awake people, even when they are notproducing motor output, motor cortical areas
often display 8–12 Hz EEG activity (Mu Rhythm)
Movement or preparation for movement typicallycauses a decrease in mu rhythms .
• Output Device
• Any controllable machines
• For answering yes/no questions
• For word processing at slow
• Wheelchair
• Virtual Reality
• Usually, Computer screen and the output is the selection of targets or cursor movement
• Need for BCI
Developing technologies for people with disabilities:
• Assist paralyzed people to operate external devices
• Assist blind people to visualize external images without physical movement
• Decode information stored on human brain (as memory)
• Decode information from brain to display human thinking or dream on a screen
• Human BCI
Human BCI Types:
Invasive
Partially invasive
Non Invasive
• Invasive BCI
• Implanted directly into the grey matter of the brain during neurosurgery
• Targeted repairing damaged sight
• Providing new functionality to persons with paralysis
• Produce the highest quality signals
Disadvantage:
• Prone to Scalar tissue build up
• Causes signal to become weaker or even lost as the body reacts to a foreign object
• Partial Invasive BCI
• Implanted inside the skull but rest outside the brain
• Produce better resolution signals than non-invasive BCIs having a lower risk of forming scar-tissue in the brain than fully-invasive BCIs
Examples:
1.Electrocorticography (ECoG)
2.Light Reactive Imaging BCI
• Partial Invasive BCI (Continues)
Electrocorticography (ECoG)
• Measures the electrical activity of the brain taken from beneath the skull
• Electrodes are embedded in a thin plastic pad that is placed above the cortex
• First trialed in humans in 2004 by Eric Leuthardt and Daniel Moran
• Enabled a teenage boy to play Space Invaders using ECoG implant
• Controls are rapid, and requires minimal training
• Partial Invasive BCI (Continues)
Light Reactive Imaging BCI
• Involve implanting a laser inside the skull
• Laser is trained on a single neuron and the neuron's
reflectance measured by a separate sensor
• When the neuron fires, the laser light pattern and wavelengths would change
Advantages of Partial Invasive BCI
• Better signal to noise ratio
• Higher spatial ratio
• Better Frequency Range
• Non-Invasive BCI
• Recorded signal have been used to power muscle implants and
restore partial movement
• Signals are weaken as skull dampens the signal
• Although the waves are still detectable, it is hard to determine the area of the brain or the neuron that created the signal
Examples:
Electroencephalography (EEG)
Magneto encephalography (MEG)
Magnetic resonance imaging (MRI)
• Non-Invasive BCI (Continues)
Electroencephalography (EEG)
Most studied potential non-invasive interface
Fine temporal resolution
EEG in Mid1990s:
• EEG signal was used as a binary signal to
control a computer cursor
• Patients can use computer cursors by
controlling their brainwaves
• Slow – required an hour to write 100
characters
• Non-Invasive BCI (Continues)
This procedure is the first non-invasive neuroimaging technique discovered. It measures the electrical activity of the brain. Due to its ease of use, cost and high temporal resolution this method is the most widely used one in BCIs nowadays
• Non-Invasive BCI (Continues)
Magneto encephalography (MEG)
MEG is a technique for mapping brain activity byrecording magnetic fields produced by electricalcurrents occurring naturally in the brain
• By using Arrays of SQUIDs (superconducting quantum interference devices)
Application :
• Localizing the regions affected by pathology, before surgical removal
• Determining the function of various parts of the brain
• Non-Invasive BCI (Continues)
Magnetic resonance imaging (MRI)
MRI is a technique used in radiology to visualize detailed internal structures.
Functional MRI or FMRI is a type of MRI scan that measures the hemodynamic response (change in blood flow) related to neural activity in the brain or spinal cord.
Recent research in ATR (Advanced Telecommunications Research, in Kyoto, Japan) on FMRI allowed the scientists to reconstruct images directly from the brain and display them on a computer.
Advantages of BCI
1. Induced disability
2. Ease of use in hardware &software
3. Speed
4. Novelty
5. Potentially high impact technology
Conclusions
BCI field is out of the demonstrations phase and is ready for clinical applications .Any new BCI technology should be focused on improving the quality of life of the end user.
This technology enables a disabled person to carry on his work overcoming his inability.
22-03-2011, 02:14 PM
does anyone know about these so called work from home jobs? Are they real, I mean can you really make money online?
23-03-2011, 03:18 PM
Submitted By:
Swati Wadhwa & Kriti Sachdeva
[attachment=10850]
INTRODUCTION
Muscles of patients paralyzed due to various
neurological diseases making them feel locked in.
But the brain is unimpaired.
Faulty control over muscles causes existing
computer communication aids to fail.
Alternative method of electrical brain activity
measurement leads to BCI.
WHAT IS BCI?
Called Brain-Computer Interface or Direct Neural Interface.
Direct communication pathway between brain and external device.
One-way BCIs
Two-way BCIs
Communication without involving brain’s normal pathways.
Achieved by Electroencephalography.
ELECTROENCEPHALOGRAPHY
Neuropsychological measurement of electrical activity
of brain from electrodes to it.
Traces called Electroencephalogram or
brainwaves.
Rather than currents, voltage differences between
different parts of brain are measured.
Individually wired electrodes or imbedded caps used.
BCI vs NEUROPROSTHESIS
Neuroprosthesis = Neural + Prosthesis
Examples-
Cochlear implant-Implanted in approx.
1,00,000 people up to 2006.
Retinal implant
Robotic Arm
Controller has inbuilt functionality to grip objects intelligently.
Neuromuscular Pathway Neuroprosthetic Pathway
1) Brain sends movement Non-invasive control
intention command to hand scheme sends intention
via spinal cord movement. to prosthetic controller.
2) Hand moves and returns Prosthetic returns force
feedback. sensory information to
controller.
3) Brain subconsciously Controller processes
processes feedback and feedback and adjusts grip.
dynamically adjusts grip.
4) Loops continuously until Loops until BCI input to
movement intent change. controller changes.
ANIMAL BCI RESEARCH
In 1999, researchers at Harvard University decoded neural firings to reproduce images seen by cats.
Used array of electrodes in thalamus of sharp-eyed cats.
Cats shown short movies.
Monkey Mouse Experiment
Monkeys trained to move cursor.
Then BrainGate attached to monkey’s neurons & only thoughts drove cursor.
HUMAN BCI RESEARCH
1.) Invasive BCIs:- Devices implanted directly into the grey matter of the brain. Used for acquired blindness.
Advantage:- High quality signals.
Disadvantage:- Prone to scar tissue build up & poor signal.
2.) Partially Invasive BCIs:- Implanted inside the skull but outside the grey matter.
Use ECoG where electrodes are embedded in thin plastic pad above the cortex.
Advantages:-
o Better resolution signals than non-invasive BCIs.
o Lower risk of scar tissue.
Disadvantage:-
Lower reslution than invasive BCIs.
3.) Non-invasive BCIs:- Caps or nets are worn outside the skull. Most popular device is EEG.
Advantage:- Easy to use, relatively cheap and portable.
Disadvantage:- Poor signals and difficult to determine waves creating area of brain.
TRAINING PROCESS
Neils Birbaumer, a neuropsychologist in Germany has implemented a BCI system called TTD.
After gaining accuracy in the game(Figure 1), patients are trained on a word processor software called Language Support System(LSP).
EQUIPMENT USED FOR BCI
BRAIN SIGNALS MEASUREMENT AND PROCESSING
Brain patterns form wave shapes that are commonly sinusoidal.
Their measurement consists of-
1. Electrodes
- Disposable (gel-less & pre-gelled type )
- Reusable ( Gold, silver, tin etc. )
- Headbands & electrode caps
2. Amplifiers and Filters
- Amplify physiological signals & reject noise
- Guarantee protection from voltage & current surges
3. A/D Converter
APPLICATIONS
Neuroprosthetics
Image Processing:- Brain can be converted into automatic
image-identifying machines.
Processing power of brain + Computer Vision = C3
Vision developed by researchers at Columbia Univ.
Simulated Reality:- Reality computer simulated to a
degree indistinguishable from the ‘True” reality.
Virtual Reality is easily distinguishable from true reality.
Participant may receive adjustment to temporarily forget
being in virtual world.
Military Use:-
Machinery operation evaluation
Fighter pilots
Gaming:-
Brain Pong
Car Race
Swati Wadhwa & Kriti Sachdeva
[attachment=10850]
INTRODUCTION
Muscles of patients paralyzed due to various
neurological diseases making them feel locked in.
But the brain is unimpaired.
Faulty control over muscles causes existing
computer communication aids to fail.
Alternative method of electrical brain activity
measurement leads to BCI.
WHAT IS BCI?
Called Brain-Computer Interface or Direct Neural Interface.
Direct communication pathway between brain and external device.
One-way BCIs
Two-way BCIs
Communication without involving brain’s normal pathways.
Achieved by Electroencephalography.
ELECTROENCEPHALOGRAPHY
Neuropsychological measurement of electrical activity
of brain from electrodes to it.
Traces called Electroencephalogram or
brainwaves.
Rather than currents, voltage differences between
different parts of brain are measured.
Individually wired electrodes or imbedded caps used.
BCI vs NEUROPROSTHESIS
Neuroprosthesis = Neural + Prosthesis
Examples-
Cochlear implant-Implanted in approx.
1,00,000 people up to 2006.
Retinal implant
Robotic Arm
Controller has inbuilt functionality to grip objects intelligently.
Neuromuscular Pathway Neuroprosthetic Pathway
1) Brain sends movement Non-invasive control
intention command to hand scheme sends intention
via spinal cord movement. to prosthetic controller.
2) Hand moves and returns Prosthetic returns force
feedback. sensory information to
controller.
3) Brain subconsciously Controller processes
processes feedback and feedback and adjusts grip.
dynamically adjusts grip.
4) Loops continuously until Loops until BCI input to
movement intent change. controller changes.
ANIMAL BCI RESEARCH
In 1999, researchers at Harvard University decoded neural firings to reproduce images seen by cats.
Used array of electrodes in thalamus of sharp-eyed cats.
Cats shown short movies.
Monkey Mouse Experiment
Monkeys trained to move cursor.
Then BrainGate attached to monkey’s neurons & only thoughts drove cursor.
HUMAN BCI RESEARCH
1.) Invasive BCIs:- Devices implanted directly into the grey matter of the brain. Used for acquired blindness.
Advantage:- High quality signals.
Disadvantage:- Prone to scar tissue build up & poor signal.
2.) Partially Invasive BCIs:- Implanted inside the skull but outside the grey matter.
Use ECoG where electrodes are embedded in thin plastic pad above the cortex.
Advantages:-
o Better resolution signals than non-invasive BCIs.
o Lower risk of scar tissue.
Disadvantage:-
Lower reslution than invasive BCIs.
3.) Non-invasive BCIs:- Caps or nets are worn outside the skull. Most popular device is EEG.
Advantage:- Easy to use, relatively cheap and portable.
Disadvantage:- Poor signals and difficult to determine waves creating area of brain.
TRAINING PROCESS
Neils Birbaumer, a neuropsychologist in Germany has implemented a BCI system called TTD.
After gaining accuracy in the game(Figure 1), patients are trained on a word processor software called Language Support System(LSP).
EQUIPMENT USED FOR BCI
BRAIN SIGNALS MEASUREMENT AND PROCESSING
Brain patterns form wave shapes that are commonly sinusoidal.
Their measurement consists of-
1. Electrodes
- Disposable (gel-less & pre-gelled type )
- Reusable ( Gold, silver, tin etc. )
- Headbands & electrode caps
2. Amplifiers and Filters
- Amplify physiological signals & reject noise
- Guarantee protection from voltage & current surges
3. A/D Converter
APPLICATIONS
Neuroprosthetics
Image Processing:- Brain can be converted into automatic
image-identifying machines.
Processing power of brain + Computer Vision = C3
Vision developed by researchers at Columbia Univ.
Simulated Reality:- Reality computer simulated to a
degree indistinguishable from the ‘True” reality.
Virtual Reality is easily distinguishable from true reality.
Participant may receive adjustment to temporarily forget
being in virtual world.
Military Use:-
Machinery operation evaluation
Fighter pilots
Gaming:-
Brain Pong
Car Race
26-03-2011, 02:07 PM
work from home jobs seem almost impossible to make a living from.is there anyone in here who's made enough from internet marketing to actually quit their day-job?
09-04-2011, 09:48 AM
[attachment=11891]
Brain Computer Interfaces
What is an EEG?
An electroencephalogram is a measure of the brain's voltage fluctuations as detected from scalp electrodes.
It is an approximation of the cumulative electrical activity of neurons.
What is it good for?
Neurofeedback
treating ADHD
guiding meditation
Brain Computer Interfaces
People with little muscle control (i.e. not enough control for EMG or gaze tracking)
People with ALS, spinal injuries
High Precision
Low bandwidth (bit rate)
EEG Background
1875 - Richard Caton discovered electrical properties of exposed cerebral hemispheres of rabbits and monkeys.
1924 - German Psychiatrist Hans Berger discovered alpha waves in humans and invented the term “electroencephalogram”
1950s - Walter Grey Walter developed “EEG topography” - mapping electrical activity of the brain.
Physical Mechanisms
EEGs require electrodes attached to the scalp with sticky gel
Require physical connection to the machine
Electrode Placement
Standard “10-20 System”
Spaced apart 10-20%
Letter for region
F - Frontal Lobe
T - Temporal Lobe
C - Center
O - Occipital Lobe
Number for exact position
Odd numbers - left
Even numbers - right
Electrode Placement
A more detailed view:
Brain “Features”
User must be able to control the output:
use a feature of the continuous EEG output that the user can reliably modify (waves), or
evoke an EEG response with an external stimulus (evoked potential)
Continuous Brain Waves
Generally grouped by frequency: (amplitudes are about 100µV max)
Brain Waves Transformations
wave-form averaging over several trials
auto-adjustment with a known signal
Fourier transforms to detect relative amplitude at different frequencies
Alpha and Beta Waves
Studied since 1920s
Found in Parietal and Frontal Cortex
Relaxed - Alpha has high amplitude
Excited - Beta has high amplitude
So, Relaxed -> Excited
means Alpha -> Beta
Mu Waves
Studied since 1930s
Found in Motor Cortex
Amplitude suppressed by Physical Movements, or intent to move physically
(Wolpaw, et al 1991) trained subjects to control the mu rhythm by visualizing motor tasks to move a cursor up and down (1D)
Mu Waves
Mu and Beta Waves
(Wolpaw and McFarland 2004) used a linear combination of Mu and Beta waves to control a 2D cursor.
Weights were learned from the users in real time.
Cursor moved every 50ms (20 Hz)
92% “hit rate” in average 1.9 sec
Mu and Beta Waves
Movie!
Mu and Beta Waves
How do you handle more complex tasks?
Finite Automata, such as this from (Millán et al, 2004)
P300 (Evoked Potentials)
occurs in response to a significant but low-probability event
300 milliseconds after the onset of the target stimulus
found in 1965 by (Sutton et al., 1965; Walter, 1965)
focus specific
P300 Experiments
(Farwell and Donchin 1988)
95% accuracy at 1 character per 26s
P300 (Evoked Potentials)
(Polikoff, et al 1995) allowed users to control a cursor by flashing control points in 4 different directions
Each sample took 4 seconds
Threw out samples masked by muscle movements (such as blinks)
(Polikoff, et al 1995) Results
50% accuracy at ~1/4 Hz
80% accuracy at ~1/30 Hz
VEP - Visual Evoked Potential
Detects changes in the visual cortex
Similar in use to P300
Close to the scalp
Model Generalization (time)
EEG models so far haven’t adjusted to fit the changing nature of the user.
(Curran et al 2004) have proposed using Adaptive Filtering algorithms to deal with this.
Model Generalization (users)
Many manual adjustments still must be made for each person (such as EEG placement)
Currently, users have to adapt to the system rather than the system adapting to the users.
Current techniques learn a separate model for each user.
Model Generalization (users)
(Müller 2004) applied typical machine learning techniques to reduce the need for training data.
Support Vector Machines (SVM) and Regularized Linear Discriminant Analysis (RLDA)
This is only the beginning of applying machine learning to BCIs!
BCI Examples - Communication
Farwell and Donchin (1988) allowed the user to select a command by looking for P300 signals when the desired command flashed
BCI Examples - Prostheses
(Wolpaw and McFarland 2004) allowed a user to move a cursor around a 2 dimensional screen
(Millán, et al. 2004) allowed a user to move a robot around the room.
BCI Examples - Music
1987 - Lusted and Knapp demonstrated an EEG controlling a music synthesizer in real time.
Atau Tanaka (Stanford Center for Computer Research in Music and Acoustics) uses it in performances to switch synthesizer functions while generating sound using EMG.
In Review…
Brain Computer Interfaces
Allow those with poor muscle control to communicate and control physical devices
High Precision (can be used reliably)
Requires somewhat invasive sensors
Requires extensive training (poor generalization)
Low bandwidth (today 24 bits/minute, or at most 5 characters/minute)
Future Work
Improving physical methods for gathering EEGs
Improving generalization
Improving knowledge of how to interpret waves (not just the “new phrenology”)
Brain Computer Interfaces
What is an EEG?
An electroencephalogram is a measure of the brain's voltage fluctuations as detected from scalp electrodes.
It is an approximation of the cumulative electrical activity of neurons.
What is it good for?
Neurofeedback
treating ADHD
guiding meditation
Brain Computer Interfaces
People with little muscle control (i.e. not enough control for EMG or gaze tracking)
People with ALS, spinal injuries
High Precision
Low bandwidth (bit rate)
EEG Background
1875 - Richard Caton discovered electrical properties of exposed cerebral hemispheres of rabbits and monkeys.
1924 - German Psychiatrist Hans Berger discovered alpha waves in humans and invented the term “electroencephalogram”
1950s - Walter Grey Walter developed “EEG topography” - mapping electrical activity of the brain.
Physical Mechanisms
EEGs require electrodes attached to the scalp with sticky gel
Require physical connection to the machine
Electrode Placement
Standard “10-20 System”
Spaced apart 10-20%
Letter for region
F - Frontal Lobe
T - Temporal Lobe
C - Center
O - Occipital Lobe
Number for exact position
Odd numbers - left
Even numbers - right
Electrode Placement
A more detailed view:
Brain “Features”
User must be able to control the output:
use a feature of the continuous EEG output that the user can reliably modify (waves), or
evoke an EEG response with an external stimulus (evoked potential)
Continuous Brain Waves
Generally grouped by frequency: (amplitudes are about 100µV max)
Brain Waves Transformations
wave-form averaging over several trials
auto-adjustment with a known signal
Fourier transforms to detect relative amplitude at different frequencies
Alpha and Beta Waves
Studied since 1920s
Found in Parietal and Frontal Cortex
Relaxed - Alpha has high amplitude
Excited - Beta has high amplitude
So, Relaxed -> Excited
means Alpha -> Beta
Mu Waves
Studied since 1930s
Found in Motor Cortex
Amplitude suppressed by Physical Movements, or intent to move physically
(Wolpaw, et al 1991) trained subjects to control the mu rhythm by visualizing motor tasks to move a cursor up and down (1D)
Mu Waves
Mu and Beta Waves
(Wolpaw and McFarland 2004) used a linear combination of Mu and Beta waves to control a 2D cursor.
Weights were learned from the users in real time.
Cursor moved every 50ms (20 Hz)
92% “hit rate” in average 1.9 sec
Mu and Beta Waves
Movie!
Mu and Beta Waves
How do you handle more complex tasks?
Finite Automata, such as this from (Millán et al, 2004)
P300 (Evoked Potentials)
occurs in response to a significant but low-probability event
300 milliseconds after the onset of the target stimulus
found in 1965 by (Sutton et al., 1965; Walter, 1965)
focus specific
P300 Experiments
(Farwell and Donchin 1988)
95% accuracy at 1 character per 26s
P300 (Evoked Potentials)
(Polikoff, et al 1995) allowed users to control a cursor by flashing control points in 4 different directions
Each sample took 4 seconds
Threw out samples masked by muscle movements (such as blinks)
(Polikoff, et al 1995) Results
50% accuracy at ~1/4 Hz
80% accuracy at ~1/30 Hz
VEP - Visual Evoked Potential
Detects changes in the visual cortex
Similar in use to P300
Close to the scalp
Model Generalization (time)
EEG models so far haven’t adjusted to fit the changing nature of the user.
(Curran et al 2004) have proposed using Adaptive Filtering algorithms to deal with this.
Model Generalization (users)
Many manual adjustments still must be made for each person (such as EEG placement)
Currently, users have to adapt to the system rather than the system adapting to the users.
Current techniques learn a separate model for each user.
Model Generalization (users)
(Müller 2004) applied typical machine learning techniques to reduce the need for training data.
Support Vector Machines (SVM) and Regularized Linear Discriminant Analysis (RLDA)
This is only the beginning of applying machine learning to BCIs!
BCI Examples - Communication
Farwell and Donchin (1988) allowed the user to select a command by looking for P300 signals when the desired command flashed
BCI Examples - Prostheses
(Wolpaw and McFarland 2004) allowed a user to move a cursor around a 2 dimensional screen
(Millán, et al. 2004) allowed a user to move a robot around the room.
BCI Examples - Music
1987 - Lusted and Knapp demonstrated an EEG controlling a music synthesizer in real time.
Atau Tanaka (Stanford Center for Computer Research in Music and Acoustics) uses it in performances to switch synthesizer functions while generating sound using EMG.
In Review…
Brain Computer Interfaces
Allow those with poor muscle control to communicate and control physical devices
High Precision (can be used reliably)
Requires somewhat invasive sensors
Requires extensive training (poor generalization)
Low bandwidth (today 24 bits/minute, or at most 5 characters/minute)
Future Work
Improving physical methods for gathering EEGs
Improving generalization
Improving knowledge of how to interpret waves (not just the “new phrenology”)
18-04-2011, 04:21 PM
PRESENTED BY
VENU
[attachment=12322]
HISTORY
Started in 1970s
Mainly for Pilots to control their planes
Since 1980s research in this field has been restricted to medicine
COGNITIVE ENGINEERING
The process of receiving signals from the thoughts of a person through different methods is known as 'COGNITIVE ENGINEERING'.
Two basic methods
Invasive and
Non Invasive
INVASIVE METHOD
In this method an electrode called the neurotrophic electrode is implanted in the brain through a brain surgery
It is implanted in the cerebral cortex just above the ear
The implant consists of a thin glass cone of the size of a ball point tip
NON-INVASIVE METHOD
This method doesn’t require a brain implant
The user wears a cap studded with electrodes These electrodes help in transmitting the signals
BRAIN COMPUTER INTERFACE
The system which is used to understand the received signals and translate them to the computer is known as Brain-Computer Interface
This system allows a person to communicate through the electrophysiological signals from his brain
VENU
[attachment=12322]
HISTORY
Started in 1970s
Mainly for Pilots to control their planes
Since 1980s research in this field has been restricted to medicine
COGNITIVE ENGINEERING
The process of receiving signals from the thoughts of a person through different methods is known as 'COGNITIVE ENGINEERING'.
Two basic methods
Invasive and
Non Invasive
INVASIVE METHOD
In this method an electrode called the neurotrophic electrode is implanted in the brain through a brain surgery
It is implanted in the cerebral cortex just above the ear
The implant consists of a thin glass cone of the size of a ball point tip
NON-INVASIVE METHOD
This method doesn’t require a brain implant
The user wears a cap studded with electrodes These electrodes help in transmitting the signals
BRAIN COMPUTER INTERFACE
The system which is used to understand the received signals and translate them to the computer is known as Brain-Computer Interface
This system allows a person to communicate through the electrophysiological signals from his brain
19-04-2011, 03:49 PM
[attachment=12409]
1. INTRODUCTION
A brain-computer interface uses electrophysiological signals to control remote devices. Most current BCIs are not invasive. They consist of electrodes applied to the scalp of an individual or worn in an electrode cap. These electrodes pick up the brain’s electrical activity (at the microvolt level) and carry it into amplifiers. These amplifiers amplify the signal approximately ten thousand times and then pass the signal via an analog to digital converter to a computer for processing. The computer processes the EEG signal and uses it in order to accomplish tasks such as communication and environmental control. BCIs are slow in comparison with normal human actions, because of the complexity and noisiness of the signals used, as well as the time necessary to complete recognition and signal processing.
The phrase brain-computer interface (BCI) when taken literally means to interface an individual’s electrophysiological signals with a computer. A true BCI only uses signals from the brain and as such must treat eye and muscle movements as artifacts or noise. On the other hand, a system that uses eye, muscle, or other body potentials mixed with EEG signals, is a brain-body actuated system.
Figure 1.1: Scheme of an EEG-based Brain Computer Interface with on-line feedback.
The EEG is recorded from the head surface, signal processing techniques are used to extract features. These features are classified, the output is displayed on a computer screen. This feedback should help the subject to control its EEG patterns.
The BCI system uses oscillatory electroencephalogram (EEG) signals, recorded during specific mental activity, as input and provides a control option by its output. The obtained output signals are presently evaluated for different purposes, such as cursor control, selection of letters or words, or control of prosthesis. People who are paralyzed or have other severe movement disorders need alternative methods for communication and control. Currently available augmentative communication methods require some muscle control. Whether they use one muscle group to supply the function normally provided by another (e.g., use extraocular muscles to drive a speech synthesizer) .Thus, they may not be useful for those who are totally paralyzed (e.g. brainstem stroke) or have other severe motor disabilities. These individuals need an alternative communication channel that does not depend on muscle control. The current and the most important application of a BCI is the restoration of communication channel for patients with locked-in-syndrome.
2. What is BCI
A brain-computer interface (BCI), sometimes called a direct neural interface or a brain-machine interface, a research deals with a direct communication pathway between a either accept commands from the brain or send signals to it (for example, to human or animal brain and an external device. In one-way BCIs, computers restore vision) but not both. Throughout the world, such research is surprisingly extensive and expanding. BCIs are often aimed at assisting, augmenting or repairing human cognitive or sensory-motor functions. BCIs read electrical signals or other manifestations of brain activity and translate them into a digital form that computers can understand, process, and convert into actions of same kind, such as moving a cursor or turning on a TV.BCI can help people with inabilities to control computers, wheelchairs, televisions or other devices with brain activity.
BCI research is rapidly approaching a level of first-generation medical practice for use by individuals whose neural pathways are damaged, and use of BCI technologies is accelerating rapidly in nonmedical arenas of commerce as well, particularly in the gaming, automotive, and robotics industries. The technologies used for BCI purposes are cutting-edge, enabling, and synergistic in many interrelated arenas, including signal processing, neural tissue engineering, multiscale modeling, systems integration, and robotics.
Among the possible brain monitoring methods, the scalp recorded electroencephalogram (EEG) constitutes an adequate alternative because of its good time resolution, relative simplicity and noninvasiveness. The EEG signals are analyzed and mapped into actions inside the computer rendered environment. A BCI allows a person to communicate with or control the external world without using the brain's normal output pathways of peripheral nerves and muscles. Messages and commands are expressed not by muscle contractions, but rather by electrophysiological signals from the brain. BCls provide an alternative communication and control option for the severely disabled.
3. HISTORY
3.1 Discovering the basics
The history of Brain-Computer-Interfaces (BCI) starts with Hans Berger's discovery of the electrical activity of human brain and the development of electroencephalograpy (EEG).Berger studied medicine at the University of Jena and received his doctorate in 1897. He became a professor in 1906 and the director of the University's psychiatry and neurology clinic in 1919. In 1924 Berger was the first one who recorded an EEG from a human brain. By analyzing EEGs Berger was able to identify different waves or rhythms which are present in a brain, as the Alpha Wave (8 – 12 Hz), also known as Berger's Wave.
Berger analyzed the interrelation of alternations in his EEG wave diagrams with brain diseases. EEGs permitted completely new possibilities for the research of human brain activities. However, it took until 1970 before the first development steps were taken to use brain activities for simple communication systems. The Advanced Research Project Agency (ARPA) of the government of the United State of America became interested in this field of research. They had the vision of increasing the performance of mental high load tasks by enhancing human abilities with artificial computer power. However these ambitious goals were never fulfilled but the first steps into the right direction were taken.
3.1.1 Monkey follows…
As almost all experiments which include a certain risk for human lives, the first experiments were conducted with animals more precisely on primates. The first wireless intracortial brain-computer interface was built by Philip Kennedy and his colleagues by implanting neurotrophic cone electrodes into monkey brains.
Miguel Nicolelis, a Brazilian physicist and scientist became the most popular proponent of using multiarea recordings from neural ensembles as input for BCI applications. In the 90s Nicolelis team did inital studies on rats, followed by the development of a BCI system that was able to decode monkeys' brain activities. This data was used to translate the monkeys' movement to rudimentary robot action. By the year 2000, Nicolelis' group implanted electrode arrays into multiple brain areas of owl monkeys. They built a BCI system that was capable of reproducing a monkey's movement, while reaching for food or using a joystick in real time. However, the system has to be considered as an open-loop BCI, as the monkeys got no feedback from their actions by the BCI. They proceeded with their research and conducted experiments in rhesus monkeys. The monkeys were trained to reach and grasp for objects on a screen by manipulating a joystick. Using velocity and grasping action prediction their BCI system was able to control a robot. The robot was hidden from the monkey but the monkey was provided with feedback of the robot's performance by the visual display.
3.1.2 Humans follows
However, not only monkeys were objects to BCI research but also humans participated in
experiments with both invasive (which means direct contact to the neurons by whatever means) and non-invasive approaches. There have been many experiments using various techniques for “reading the brain” such as the EEG, MEG, fMRI or similar methods.
One of the first persons who benefit from all the years of BCI research is Matt Nagle. In 2004 an electrode array was implanted into his brain to restore functionalities he had lost due to paralysis.
The system required some training but finally he was able to control the TV, check emails and do basically everything that can be achieved by using a mouse.
Today many researchers at a lot of universities and laboratories are continuing BCI research. However, the present-days achievments are very impressive but there is still a lot of research and studying to be done until the whole potential of Brain-Computer-Interfaces can be tapped.
1. INTRODUCTION
A brain-computer interface uses electrophysiological signals to control remote devices. Most current BCIs are not invasive. They consist of electrodes applied to the scalp of an individual or worn in an electrode cap. These electrodes pick up the brain’s electrical activity (at the microvolt level) and carry it into amplifiers. These amplifiers amplify the signal approximately ten thousand times and then pass the signal via an analog to digital converter to a computer for processing. The computer processes the EEG signal and uses it in order to accomplish tasks such as communication and environmental control. BCIs are slow in comparison with normal human actions, because of the complexity and noisiness of the signals used, as well as the time necessary to complete recognition and signal processing.
The phrase brain-computer interface (BCI) when taken literally means to interface an individual’s electrophysiological signals with a computer. A true BCI only uses signals from the brain and as such must treat eye and muscle movements as artifacts or noise. On the other hand, a system that uses eye, muscle, or other body potentials mixed with EEG signals, is a brain-body actuated system.
Figure 1.1: Scheme of an EEG-based Brain Computer Interface with on-line feedback.
The EEG is recorded from the head surface, signal processing techniques are used to extract features. These features are classified, the output is displayed on a computer screen. This feedback should help the subject to control its EEG patterns.
The BCI system uses oscillatory electroencephalogram (EEG) signals, recorded during specific mental activity, as input and provides a control option by its output. The obtained output signals are presently evaluated for different purposes, such as cursor control, selection of letters or words, or control of prosthesis. People who are paralyzed or have other severe movement disorders need alternative methods for communication and control. Currently available augmentative communication methods require some muscle control. Whether they use one muscle group to supply the function normally provided by another (e.g., use extraocular muscles to drive a speech synthesizer) .Thus, they may not be useful for those who are totally paralyzed (e.g. brainstem stroke) or have other severe motor disabilities. These individuals need an alternative communication channel that does not depend on muscle control. The current and the most important application of a BCI is the restoration of communication channel for patients with locked-in-syndrome.
2. What is BCI
A brain-computer interface (BCI), sometimes called a direct neural interface or a brain-machine interface, a research deals with a direct communication pathway between a either accept commands from the brain or send signals to it (for example, to human or animal brain and an external device. In one-way BCIs, computers restore vision) but not both. Throughout the world, such research is surprisingly extensive and expanding. BCIs are often aimed at assisting, augmenting or repairing human cognitive or sensory-motor functions. BCIs read electrical signals or other manifestations of brain activity and translate them into a digital form that computers can understand, process, and convert into actions of same kind, such as moving a cursor or turning on a TV.BCI can help people with inabilities to control computers, wheelchairs, televisions or other devices with brain activity.
BCI research is rapidly approaching a level of first-generation medical practice for use by individuals whose neural pathways are damaged, and use of BCI technologies is accelerating rapidly in nonmedical arenas of commerce as well, particularly in the gaming, automotive, and robotics industries. The technologies used for BCI purposes are cutting-edge, enabling, and synergistic in many interrelated arenas, including signal processing, neural tissue engineering, multiscale modeling, systems integration, and robotics.
Among the possible brain monitoring methods, the scalp recorded electroencephalogram (EEG) constitutes an adequate alternative because of its good time resolution, relative simplicity and noninvasiveness. The EEG signals are analyzed and mapped into actions inside the computer rendered environment. A BCI allows a person to communicate with or control the external world without using the brain's normal output pathways of peripheral nerves and muscles. Messages and commands are expressed not by muscle contractions, but rather by electrophysiological signals from the brain. BCls provide an alternative communication and control option for the severely disabled.
3. HISTORY
3.1 Discovering the basics
The history of Brain-Computer-Interfaces (BCI) starts with Hans Berger's discovery of the electrical activity of human brain and the development of electroencephalograpy (EEG).Berger studied medicine at the University of Jena and received his doctorate in 1897. He became a professor in 1906 and the director of the University's psychiatry and neurology clinic in 1919. In 1924 Berger was the first one who recorded an EEG from a human brain. By analyzing EEGs Berger was able to identify different waves or rhythms which are present in a brain, as the Alpha Wave (8 – 12 Hz), also known as Berger's Wave.
Berger analyzed the interrelation of alternations in his EEG wave diagrams with brain diseases. EEGs permitted completely new possibilities for the research of human brain activities. However, it took until 1970 before the first development steps were taken to use brain activities for simple communication systems. The Advanced Research Project Agency (ARPA) of the government of the United State of America became interested in this field of research. They had the vision of increasing the performance of mental high load tasks by enhancing human abilities with artificial computer power. However these ambitious goals were never fulfilled but the first steps into the right direction were taken.
3.1.1 Monkey follows…
As almost all experiments which include a certain risk for human lives, the first experiments were conducted with animals more precisely on primates. The first wireless intracortial brain-computer interface was built by Philip Kennedy and his colleagues by implanting neurotrophic cone electrodes into monkey brains.
Miguel Nicolelis, a Brazilian physicist and scientist became the most popular proponent of using multiarea recordings from neural ensembles as input for BCI applications. In the 90s Nicolelis team did inital studies on rats, followed by the development of a BCI system that was able to decode monkeys' brain activities. This data was used to translate the monkeys' movement to rudimentary robot action. By the year 2000, Nicolelis' group implanted electrode arrays into multiple brain areas of owl monkeys. They built a BCI system that was capable of reproducing a monkey's movement, while reaching for food or using a joystick in real time. However, the system has to be considered as an open-loop BCI, as the monkeys got no feedback from their actions by the BCI. They proceeded with their research and conducted experiments in rhesus monkeys. The monkeys were trained to reach and grasp for objects on a screen by manipulating a joystick. Using velocity and grasping action prediction their BCI system was able to control a robot. The robot was hidden from the monkey but the monkey was provided with feedback of the robot's performance by the visual display.
3.1.2 Humans follows
However, not only monkeys were objects to BCI research but also humans participated in
experiments with both invasive (which means direct contact to the neurons by whatever means) and non-invasive approaches. There have been many experiments using various techniques for “reading the brain” such as the EEG, MEG, fMRI or similar methods.
One of the first persons who benefit from all the years of BCI research is Matt Nagle. In 2004 an electrode array was implanted into his brain to restore functionalities he had lost due to paralysis.
The system required some training but finally he was able to control the TV, check emails and do basically everything that can be achieved by using a mouse.
Today many researchers at a lot of universities and laboratories are continuing BCI research. However, the present-days achievments are very impressive but there is still a lot of research and studying to be done until the whole potential of Brain-Computer-Interfaces can be tapped.
06-05-2011, 03:12 PM
Presented by::
Vipul Sparsh
[attachment=13324]
What is BCI?
Brain-Computer Interfaces (BCI)
A Brain Computer Interface (BCI) is a
collaboration in which
a brain accepts and
controls a mechanical
device as a natural part
of its representation of the body.
Examples…. (Reel life)
Example…. (real) Stephen Hawkins
General principle
In healthy subjects, primary motor area sends movement commands to muscles via spinal cord .
In paralyzed people this pathway is interrupted.
Computer based decoder translates this activity into commands for muscle control.
How bci works…….
BASIC COMPONENTs
The implant device chronic multielectrode
The signal recording and processing section
An external device the subject uses to produce & control motion
A feedback section to the subject
Output Device
Any controllable machines
For answering yes/no questions
Wheelchair
Virtual Reality
Usually, Computer screen and the output is the selection of targets or cursor movement
Our Architecture
EEG based BCI
Electroencephalography
(EEG) is a method used in
measuring the electrical
activity of the brain.
The basic frequency of the
EEG range is classified
into five bands for
purposes of EEG analysis
called brain rhythms .
The alpha rhythm is one of
the principal components
of the EEG and is an
indicator of the state of
alertness of the brain
Wearable BCI
Mobility
Communication technologies
Bluetooth
802.11
GSM/GPRS
PDA instead of stationary computer
Dry Electrode instead of wet (reducing montage time)
Making the BCI transparent
No need to change electrodes for a reasonable long time
BCI APPLICATIONS
Medical applications(restoration of a
communication channel for patients with lockedin syndrome and the control of neuroprostheses in patients affected by spinal cord injuries )
Military applications
Counter terrorism(10 times faster image search) multimedia and virtual reality applications
DRAWBACKS
EEGs measure tiny voltage potentials. The
signal is weak and prone to interference.
Each neuron is constantly sending and
receiving signals through a complex web
of connections. There are chemical
processes involved as well, which EEGs
can't pick up on.
COMPUTATIONAL CHALLENGES AND FUTURE IMPLEMENTATIONS
Minimally invasive surgical methods.
Next generation Neuroprostheses.
Vision prosthesis.
BCI for totally paralyzed.
Minimal number of calibration trials.
Development of telemetry chip to collect data without external cables.
CONCLUSION
A potential therapeutic tool.
BCI System is nominated for the
European ICT Grand Prize.
Potentially high impact technology
Vipul Sparsh
[attachment=13324]
What is BCI?
Brain-Computer Interfaces (BCI)
A Brain Computer Interface (BCI) is a
collaboration in which
a brain accepts and
controls a mechanical
device as a natural part
of its representation of the body.
Examples…. (Reel life)
Example…. (real) Stephen Hawkins
General principle
In healthy subjects, primary motor area sends movement commands to muscles via spinal cord .
In paralyzed people this pathway is interrupted.
Computer based decoder translates this activity into commands for muscle control.
How bci works…….
BASIC COMPONENTs
The implant device chronic multielectrode
The signal recording and processing section
An external device the subject uses to produce & control motion
A feedback section to the subject
Output Device
Any controllable machines
For answering yes/no questions
Wheelchair
Virtual Reality
Usually, Computer screen and the output is the selection of targets or cursor movement
Our Architecture
EEG based BCI
Electroencephalography
(EEG) is a method used in
measuring the electrical
activity of the brain.
The basic frequency of the
EEG range is classified
into five bands for
purposes of EEG analysis
called brain rhythms .
The alpha rhythm is one of
the principal components
of the EEG and is an
indicator of the state of
alertness of the brain
Wearable BCI
Mobility
Communication technologies
Bluetooth
802.11
GSM/GPRS
PDA instead of stationary computer
Dry Electrode instead of wet (reducing montage time)
Making the BCI transparent
No need to change electrodes for a reasonable long time
BCI APPLICATIONS
Medical applications(restoration of a
communication channel for patients with lockedin syndrome and the control of neuroprostheses in patients affected by spinal cord injuries )
Military applications
Counter terrorism(10 times faster image search) multimedia and virtual reality applications
DRAWBACKS
EEGs measure tiny voltage potentials. The
signal is weak and prone to interference.
Each neuron is constantly sending and
receiving signals through a complex web
of connections. There are chemical
processes involved as well, which EEGs
can't pick up on.
COMPUTATIONAL CHALLENGES AND FUTURE IMPLEMENTATIONS
Minimally invasive surgical methods.
Next generation Neuroprostheses.
Vision prosthesis.
BCI for totally paralyzed.
Minimal number of calibration trials.
Development of telemetry chip to collect data without external cables.
CONCLUSION
A potential therapeutic tool.
BCI System is nominated for the
European ICT Grand Prize.
Potentially high impact technology
09-05-2011, 03:12 PM
[attachment=13474]
BRAIN COMPUTER INTERFACE(BCI)
Brain Computer Interface
A brain-computer interface is a direct communication pathway between a human or animal brain ( or brain cell culture) and an external device.
Sometimes called a direct neural interface or a brain machine interface (BMI).
Motivation for BCI Research
In USA, more than 200,000 patients live with the motor sequelae (consequences) of serious injury. There are two ways to help them restore some motor function:
Repair the damaged nerve axons.
Build neuroprosthetic device.
Detecting neural activity
Invasive BCI
Invasive BCI are directly implanted into the grey matter of the brain during neurosurgery.
They produce the highest quality signals of BCI devices .
Prone to building up of scar-tissue
Targeted repairing damaged sight and providing new functionality to paralyzed people (neuroprosthetics).
Detecting neural activity
Non Invasive BCI
Neuroimaging technologies as interfaces are used.
Signals recorded in this way have been used to power muscle implants and restore partial movement in an experimental volunteer.
Non-invasive implants produce poor signal resolution .
EEG
Using BCI
Neuron spike based BCI
High speed real time control
Precise control of movement
Invasive
High risk for clinical application
Working of Simple BCI
BCI Methods
P300 detection
How can you type words by mind?
The P300 (P3) wave is an event related potential (ERP) which can be recorded via electroencephalography (EEG) as a positive deflection in voltage at a latency of roughly 300 ms in the EEG.
EEG mu-Rhythm Conditioning
VEP Detection
Experiment Design
Alphabet row/column was flashing randomly on the computer screen
Human subject was gazing at the screen
Human EEG was recorded simultaneously
P300 components in EEG was extracted in real time for letter guess
EEG amplifier Human
subject Visual feedback
Experiment Design
Application
Artificial Sensory channel
Artificial Hearing
Artificial Vision
Artificial Motor Channel
3. Neural Disorder Control Parkinson’s disease Seizure prediction and control
Artificial Sensory channel
Artificial Motor Channel
Neural Disorder Control Parkinson’s disease Seizure prediction and control
Future : Silicon Cognition
Advantages
BCIs will help creating a Direct communication pathway between a human or animal brain and any external devices like computers.
BCI has increased the possibility of treatment of disabilities related to nervous system along with the old technique of Neuroprosthetics.
Techniques like EEG, MEG and neurochips have come into discussions since the BCI application have started developing.
This has provided a new work area for scientists and researchers around the world.
Disadvantages
In case of Invasive BCI there is a risk of formation of scar tissue.
There is a need of extensive training before user can use techniques like EEG
BCI techniques still require much enhancement before they can be used by users as they are slow.
Ethical implications of BCI will arise in future
BCI techniques are costly. It requires a lot of money to set up the BCI environment.
Conclusion
Brain-Computer Interface (BCI) is a method of communication based on voluntary neural activity generated by the brain and independent of its normal output pathways of peripheral nerves and muscles.
The neural activity used in BCI can be recorded using invasive or noninvasive techniques.
We can say as detection techniques and experimental designs improve, the BCI will improve as well and would provide wealth alternatives for individuals to interact with their environment.
BRAIN COMPUTER INTERFACE(BCI)
Brain Computer Interface
A brain-computer interface is a direct communication pathway between a human or animal brain ( or brain cell culture) and an external device.
Sometimes called a direct neural interface or a brain machine interface (BMI).
Motivation for BCI Research
In USA, more than 200,000 patients live with the motor sequelae (consequences) of serious injury. There are two ways to help them restore some motor function:
Repair the damaged nerve axons.
Build neuroprosthetic device.
Detecting neural activity
Invasive BCI
Invasive BCI are directly implanted into the grey matter of the brain during neurosurgery.
They produce the highest quality signals of BCI devices .
Prone to building up of scar-tissue
Targeted repairing damaged sight and providing new functionality to paralyzed people (neuroprosthetics).
Detecting neural activity
Non Invasive BCI
Neuroimaging technologies as interfaces are used.
Signals recorded in this way have been used to power muscle implants and restore partial movement in an experimental volunteer.
Non-invasive implants produce poor signal resolution .
EEG
Using BCI
Neuron spike based BCI
High speed real time control
Precise control of movement
Invasive
High risk for clinical application
Working of Simple BCI
BCI Methods
P300 detection
How can you type words by mind?
The P300 (P3) wave is an event related potential (ERP) which can be recorded via electroencephalography (EEG) as a positive deflection in voltage at a latency of roughly 300 ms in the EEG.
EEG mu-Rhythm Conditioning
VEP Detection
Experiment Design
Alphabet row/column was flashing randomly on the computer screen
Human subject was gazing at the screen
Human EEG was recorded simultaneously
P300 components in EEG was extracted in real time for letter guess
EEG amplifier Human
subject Visual feedback
Experiment Design
Application
Artificial Sensory channel
Artificial Hearing
Artificial Vision
Artificial Motor Channel
3. Neural Disorder Control Parkinson’s disease Seizure prediction and control
Artificial Sensory channel
Artificial Motor Channel
Neural Disorder Control Parkinson’s disease Seizure prediction and control
Future : Silicon Cognition
Advantages
BCIs will help creating a Direct communication pathway between a human or animal brain and any external devices like computers.
BCI has increased the possibility of treatment of disabilities related to nervous system along with the old technique of Neuroprosthetics.
Techniques like EEG, MEG and neurochips have come into discussions since the BCI application have started developing.
This has provided a new work area for scientists and researchers around the world.
Disadvantages
In case of Invasive BCI there is a risk of formation of scar tissue.
There is a need of extensive training before user can use techniques like EEG
BCI techniques still require much enhancement before they can be used by users as they are slow.
Ethical implications of BCI will arise in future
BCI techniques are costly. It requires a lot of money to set up the BCI environment.
Conclusion
Brain-Computer Interface (BCI) is a method of communication based on voluntary neural activity generated by the brain and independent of its normal output pathways of peripheral nerves and muscles.
The neural activity used in BCI can be recorded using invasive or noninvasive techniques.
We can say as detection techniques and experimental designs improve, the BCI will improve as well and would provide wealth alternatives for individuals to interact with their environment.
21-07-2011, 03:51 PM
[attachment=14728]
BRAIN MACHINE INTERFACE
INTRODUCTION
Neural Interface- New means of communication
Control of applications by thoughts only
BMI - - Read electrical signals of brain
activity
- - Translate them into digital form
- - Convert into actions of some
kind
Provide assistance to people with inabilities
DIFFERENT TYPES
Invasive -- Implanted directly into the grey
matter
-- Produce the highest quality signals
Partially Invasive
-- Uses Electrocorticography
-- Implanted inside skull but rests outside
the brain
-- Lower technical difficulty
-- Lower clinical risk
-- Probably superior
long-term stability
Non Invasive
-- Uses Electroencephalography
-- Low set-up cost
-- Ease of use
-- Portability
-- Susceptibility
to noise
TECHNOLOGY
Bioelectrical activity of nerves and muscles
Signals generated on brain surface
-- monitored and analyzed
Imagine doing something
-- Small signals generate
from different areas
of the brain
Control the brain functions
-- artificially producing these signals and
sending them to respective parts
BRAIN WAVES
ELECTRODE PLACEMENTS
The sensors are labelled
by proximity over a region
of the brain
Number for exact
position
-- Odd numbers-Left
-- Even numbers-Right
DESCRIPTION
The main components of BMI:
The implant device, or chronic multi-electrode array
The signal recording and processing section
An external device which the subject uses to produce and control motion
A feedback section to the subject.
ADVANTAGES
Means of communication for people with disabilities
Provides better living, more features, more advancement in technologies etc.
Linking people via chip implants to super intelligent machines
Allow for computer intelligence to be hooked more directly into the brain
Gradual co-evolution with computers.
CHALLENGES
Connecting to the nervous system could lead to permanent brain damage
In the networked brain condition -- Human mind exposed to encroachment
Virus attacks may occur to brain causing ill effects
APPLICATIONS
Auditory and visual prosthesis
Functional-neuromuscular stimulation (FNS)
Prosthetic limb control
In effective construction of unmanned systems, in space missions, defense areas etc
Communication over internet can be modified.
PRESENT SCENARIO
FUTURE SCOPE
Linking people via chip implants to super intelligent machines seems to be a natural progression –creating in effect, super humans.
Human-Machine and Machine-Human communication
Humanoids????
CONCLUSION
BMI -- the ability to give people back their vision and hearing
People with disabilities -- New and normal life
Change the way a person looks at the world
Someday these devices -- more common than cellphones
BRAIN MACHINE INTERFACE
INTRODUCTION
Neural Interface- New means of communication
Control of applications by thoughts only
BMI - - Read electrical signals of brain
activity
- - Translate them into digital form
- - Convert into actions of some
kind
Provide assistance to people with inabilities
DIFFERENT TYPES
Invasive -- Implanted directly into the grey
matter
-- Produce the highest quality signals
Partially Invasive
-- Uses Electrocorticography
-- Implanted inside skull but rests outside
the brain
-- Lower technical difficulty
-- Lower clinical risk
-- Probably superior
long-term stability
Non Invasive
-- Uses Electroencephalography
-- Low set-up cost
-- Ease of use
-- Portability
-- Susceptibility
to noise
TECHNOLOGY
Bioelectrical activity of nerves and muscles
Signals generated on brain surface
-- monitored and analyzed
Imagine doing something
-- Small signals generate
from different areas
of the brain
Control the brain functions
-- artificially producing these signals and
sending them to respective parts
BRAIN WAVES
ELECTRODE PLACEMENTS
The sensors are labelled
by proximity over a region
of the brain
Number for exact
position
-- Odd numbers-Left
-- Even numbers-Right
DESCRIPTION
The main components of BMI:
The implant device, or chronic multi-electrode array
The signal recording and processing section
An external device which the subject uses to produce and control motion
A feedback section to the subject.
ADVANTAGES
Means of communication for people with disabilities
Provides better living, more features, more advancement in technologies etc.
Linking people via chip implants to super intelligent machines
Allow for computer intelligence to be hooked more directly into the brain
Gradual co-evolution with computers.
CHALLENGES
Connecting to the nervous system could lead to permanent brain damage
In the networked brain condition -- Human mind exposed to encroachment
Virus attacks may occur to brain causing ill effects
APPLICATIONS
Auditory and visual prosthesis
Functional-neuromuscular stimulation (FNS)
Prosthetic limb control
In effective construction of unmanned systems, in space missions, defense areas etc
Communication over internet can be modified.
PRESENT SCENARIO
FUTURE SCOPE
Linking people via chip implants to super intelligent machines seems to be a natural progression –creating in effect, super humans.
Human-Machine and Machine-Human communication
Humanoids????
CONCLUSION
BMI -- the ability to give people back their vision and hearing
People with disabilities -- New and normal life
Change the way a person looks at the world
Someday these devices -- more common than cellphones
22-07-2011, 08:21 AM
22-07-2011, 09:50 AM
To get more information about the topic " BRAIN COMPUTER INTERFACE" please refer the page link below
http://studentbank.in/report-brain-compu...ars-report
http://studentbank.in/report-brain-compu...ort?page=8
http://studentbank.in/report-brain-compu...ort?page=4
http://studentbank.in/report-brain-compu...erface-bci
http://studentbank.in/report-brain-compu...ars-report
http://studentbank.in/report-brain-compu...ort?page=8
http://studentbank.in/report-brain-compu...ort?page=4
http://studentbank.in/report-brain-compu...erface-bci
hi
bci report
read it &dhbnxkvm c,v . v /v.cx;,c;lxg,c;g
bci report
read it &dhbnxkvm c,v . v /v.cx;,c;lxg,c;g
02-09-2011, 10:01 AM
To get more information about the topic " BRAIN COMPUTER INTERFACE" please refer the page link below
http://studentbank.in/report-brain-compu...ars-report
http://studentbank.in/report-brain-compu...ort?page=8
http://studentbank.in/report-brain-compu...ort?page=4
http://studentbank.in/report-brain-compu...erface-bci
http://studentbank.in/report-brain-compu...ars-report
http://studentbank.in/report-brain-compu...ort?page=8
http://studentbank.in/report-brain-compu...ort?page=4
http://studentbank.in/report-brain-compu...erface-bci
26-09-2011, 06:04 PM
i need full document on the blue brain .....
plz attached the copy to this website..
this is higly required
plz attached the copy to this website..
this is higly required
27-09-2011, 09:45 AM
To get more information about the topic " BRAIN COMPUTER INTERFACE" please refer the page link below
http://studentbank.in/report-brain-compu...ars-report
http://studentbank.in/report-brain-compu...ort?page=8
http://studentbank.in/report-brain-compu...ort?page=4
http://studentbank.in/report-brain-compu...erface-bci
http://studentbank.in/report-brain-compu...ars-report
http://studentbank.in/report-brain-compu...ort?page=8
http://studentbank.in/report-brain-compu...ort?page=4
http://studentbank.in/report-brain-compu...erface-bci
10-02-2012, 01:42 PM
BRAIN COMPUTER INTERFACE
[attachment=17413]
Neuron Structure:
Our brain is made of approximately 100 billion nerve cells, called neurons.
Neurons have the amazing ability to gather and transmit electrochemical signals.
Neurons have three basic parts:
How does BCI work
The reason a BCI works at all is because of the way our brains function.
Our brains are filled with neurons connected to one another by dendrites and axons.
Every time we think, move, feel or remember something, our neurons are at work. That work is carried out by small electric signals that zip from neuron to neuron as fast as 250 mph.
The signals are generated by differences in electric potential carried by ions on the membrane of each neuron.
Scientists can detect those signals, interpret what they mean and use them to direct a device of some kind.
Invasive
To get a higher-resolution signal, scientists can implant electrodes directly into the gray matter of the brain itself, or on the surface of the brain, beneath the skull.
This allows for much more direct reception of electric signals and allows electrode placement in the specific area of the brain where the appropriate signals are generated.
The electrodes measure minute differences in the voltage between neurons. The signal is then amplified and filtered.
It is then interpreted by a computer program,
[attachment=17413]
Neuron Structure:
Our brain is made of approximately 100 billion nerve cells, called neurons.
Neurons have the amazing ability to gather and transmit electrochemical signals.
Neurons have three basic parts:
How does BCI work
The reason a BCI works at all is because of the way our brains function.
Our brains are filled with neurons connected to one another by dendrites and axons.
Every time we think, move, feel or remember something, our neurons are at work. That work is carried out by small electric signals that zip from neuron to neuron as fast as 250 mph.
The signals are generated by differences in electric potential carried by ions on the membrane of each neuron.
Scientists can detect those signals, interpret what they mean and use them to direct a device of some kind.
Invasive
To get a higher-resolution signal, scientists can implant electrodes directly into the gray matter of the brain itself, or on the surface of the brain, beneath the skull.
This allows for much more direct reception of electric signals and allows electrode placement in the specific area of the brain where the appropriate signals are generated.
The electrodes measure minute differences in the voltage between neurons. The signal is then amplified and filtered.
It is then interpreted by a computer program,
10-02-2012, 10:42 PM
hi
can you please send complete ppt for brain computer interface seminar to this following mail ID sk.veena111[at]gmail.com
can you please send complete ppt for brain computer interface seminar to this following mail ID sk.veena111[at]gmail.com
11-02-2012, 10:41 AM
to get information about the topic BRAIN COMPUTER INTERFACE A SEMINAR full report ,ppt and related topic refer the page link bellow
http://studentbank.in/report-brain-compu...ars-report
http://studentbank.in/report-brain-compu...2#pid67292
http://studentbank.in/report-brain-compu...ort?page=8
http://studentbank.in/report-brain-compu...ort?page=2
http://studentbank.in/report-brain-machine-interface
http://studentbank.in/report-human-compu...ars-report
http://studentbank.in/report-adaptive-br...t-download
http://studentbank.in/report-brain-compu...ort?page=5
http://studentbank.in/report-brain-compu...rt?page=14
http://studentbank.in/report-brain-compu...rt?page=12
http://studentbank.in/report-brain-compu...rt?page=11
http://studentbank.in/report-brain-compu...erface-bci
http://studentbank.in/report-brain-compu...?pid=11708
http://studentbank.in/report-brain-compu...ace--15580
http://studentbank.in/report-brain-compu...ars-report
http://studentbank.in/report-brain-compu...2#pid67292
http://studentbank.in/report-brain-compu...ort?page=8
http://studentbank.in/report-brain-compu...ort?page=2
http://studentbank.in/report-brain-machine-interface
http://studentbank.in/report-human-compu...ars-report
http://studentbank.in/report-adaptive-br...t-download
http://studentbank.in/report-brain-compu...ort?page=5
http://studentbank.in/report-brain-compu...rt?page=14
http://studentbank.in/report-brain-compu...rt?page=12
http://studentbank.in/report-brain-compu...rt?page=11
http://studentbank.in/report-brain-compu...erface-bci
http://studentbank.in/report-brain-compu...?pid=11708
http://studentbank.in/report-brain-compu...ace--15580
17-02-2012, 12:24 PM
plz will any one submit seminar report n full ppt on topic BRAIN COMPUTER INTERFACE by evening ...itz urgent plz do as soon as possible....
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