Detection of Familiar and Unfamiliar Images using EEG-based Brain-Computer Interface

Electroencephalography (EEG) signals have widely been used for developing Brain Computer Interface (BCI) systems. BCI systems generally record, process and extract informative features hidden in brain signals, ultimately aiming towards "human thought translation". A number of EEG based BCI studies focus on estimation and enhancement of cognitive functions such as attention, memory and creativity, assessing mental workload, fatigue, etc. Detection of discriminative EEG features associated with presentation of familiar and non-familiar images is not well-studied so far, though it is worthy to explore its usability even in EEG-based authentication systems. In this paper, a set of time-frequency based EEG features are investigated, while the subjects are exposed to familiar and unfamiliar visual stimuli (images) for a fixed time period during the experimental paradigm. The results show that combination of features such as band power values, signal peaks, activity and mobility of the signal gives an average accuracy of 70.71% in classifying between familiar and unfamiliar images among 7 subjects. Further investigation is necessary to improve the classification performance and to reduce the effects of inter-subject and intra-subject variability of EEG signals during feature extraction.