Abstract:
Brain Computer Interface (BCI) Systems have
developed for new way of communication between
computer and human who are suffer from severe motor
disabilities and difficult to communicate with their
environment. BCI let them for communication by non
muscular way. For communication between human and
computer, BCI uses a type of signal called
Electroencephalogram (EEG) signal which are
recorded from the human‘s brain by mean of electrode.
Electroencephalogram (EEG) signal is an important
information source for knowing brain processes for the
non-invasive BCI. In translating human’s thought, it
needs to classify acquired EEG signal accurately.
Independent Component analysis (ICA) method via
EEGLab Tools for removing artifacts which are caused
by eye blinks in the recorded mental task EEG signal.
For features extraction, the Time and Frequency
features of non stationary EEG signals are extracted
by Matching Pursuit (MP) algorithm. The
classification of mental tasks is performed by
Multi_Class Support Vector Machine (SVM).
