  Evaluating human brain potentials during watching different images can be
used for memory evaluation, information retrieving, guilty-innocent
identification and examining the brain response. In this study, the effects of
watching images, with different levels of familiarity, on subjects'
Electroencephalogram (EEG) have been studied. Three different groups of images
with three familiarity levels of "unfamiliar", "familiar" and "very familiar"
have been considered for this study. EEG signals of 21 subjects (14 men) were
recorded. After signal acquisition, pre-processing, including noise and
artifact removal, were performed on epochs of data. Features, including
spatial-statistical, wavelet, frequency and harmonic parameters, and also
correlation between recording channels, were extracted from the data. Then, we
evaluated the efficiency of the extracted features by using p-value and also an
orthogonal feature selection method (combination of Gram-Schmitt method and
Fisher discriminant ratio) for feature dimensional reduction. As the final step
of feature selection, we used 'add-r take-away l' method for choosing the most
discriminative features. For data classification, including all two-class and
three-class cases, we applied Support Vector Machine (SVM) on the extracted
features. The correct classification rates (CCR) for "unfamiliar-familiar",
"unfamiliar-very familiar" and "familiar-very familiar" cases were 85.6%,
92.6%, and 70.6%, respectively. The best results of classifications were
obtained in pre-frontal and frontal regions of brain. Also, wavelet, frequency
and harmonic features were among the most discriminative features. Finally, in
three-class case, the best CCR was 86.8%.
