EEG data classification using multilinear regression model

Brain Computer Interfaces (BCI) are systems that facilitate people to use a computer, to control an electromechanical or a neuroprosthetic device without using their motor nervous system. It is possible to obtain an information about the brain tissues with electrodes placed on the skull which record the electrical activity called electroencephalogram (EEG) . The electrodes placed in di erent regions capture the activity in their neighborhood. BCI systems combining the electrical signals from these electrodes use signal processing and machine learning algorithms to identify the motor or the cognitive activity that is embedded in the brain signals so as to mobilize the peripheral devices according to the information gathered. Emotion estimation is often used in brain computer interface applications to improve and control the communication between man and machine. In recent years, emotion estimation studies based on brain electrical activity, which is the most widespread method used for accurate emotion analysis, have gained momentum. In this thesis study, multichannel EEG data taken from normal subjects who encountered emotionally pleasant and unpleasant pictures were classi ed with a multilinear regression algorithm. The results were compared with those of the Support Vector Machine (SVM) and proved to be better in accuracy.