16-10-2010, 04:54 PM
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BRAIN COMPUTER INTERFACE A SEMINAR REPORT
SHEENA MATHEW Dr. DAVID PETER
SEMINAR GUIDE HEAD OF THE DEPARTMENT
Lecturer
Computer Science and Engineering
School of Engineering,
CUSAT
For measuring brain function, neuroimaging modalities such as fMRI, EEG and MEG are providing clinicians and neuroscientists with a variety of powerful tools. Without a doubt EEGs have been the best tool so far for this type of research. From the different parts of the brain such as frontal, occipital, parietal & cortical different brain activities are measured with either invasive or non-invasive real time techniques.
After obtaining EEG signals they are applied to signal processing unit, which includes amplifier, special function filters, ICA components (artifact rejection), ADC etc.
Now our task is to classify different EEG patterns according to its features such as frequency and amplitude in different states of consciousness like alertness, lethargy and dreaming. Our approach is generally based on an artificial neural network that recognizes and classifies different brain activation patterns associated with carefully selected mental tasks. Then the classified signal is translated into the control command signal using software to perform mental recognized task and is applied to the control device.
By watching the control action of the device on the computer screen, visual feedback from the eye is given to brain and the next control action can be decided by the user.
This whole close loop system is known as brain computer interface .
