Analysis and classification of evoked potentials of familiar and unfamiliar face stimuli

Electroencephalogram (EEG) is an electrical brain activity recorded from the electrodes placed on the skull surface. Evoked potential (EP) is an activity emerging in response to a specific stimulus in EEG. These potentials recorded from the brain are not only used in diagnosis of some diseases but also have recently been used in the design of applications enabling brain's interaction with computers and the environment. Examples of the applications are artificial limbs design, brain-computer design, oddball paradigm design and cerebral forensic inquiry design applications. In this study, familiar and unfamiliar face stimuli images were shown to different participants in different sessions during EEG recording and the acquired EEG data was analyzed and classified utilizing different methods. Analysis process comprised following stages respectively: pre-processing, feature extraction, channel selection, and classification and different methods were tried in each stage. At the pre-processing stage a low pass filter in time domain was used to suppress background signal and discrete cosine transform was employed to reduce spatial correlation between channels. The best classification performance was obtained when piecewise constant modeling was used at the feature extraction stage, mutual information was used at the channel selection stage and support vector machine was used at the classification stage. At the end of classification, brain electric activities were categorized in response to familiar and unfamiliar face stimuli. Consequently, this study will help us both in the pre-diagnosis of diseases such as Prosopagnosia and Alzheimer and in determining suspects' behavior in response to a face stimulus related to a crime instance in forensic inquiries.