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.
