Non-target photo images in oddball paradigm improve EEG-based personal identification rates.

A research on biometry based on human brain activities has lately been emerging. In this study, we investigate the feasibility of personal identification using one-channel electroencephalogram during photo retrieval in oddball paradigm. The use of non-target photo images was examined to improve the identification performances. Nine photo images were randomly presented one after another to five subjects. The Principal Component Analysis and the Linear Discriminant Analysis were applied for the signal processing. With EEG activities both during target and non-target photo retrieval, the algorithm successfully improved the identification rates. The rates were 87.2, 95.0, and 97.6% using 5, 10, and 20-time averaging, respectively. The performances with EEG only during target photo retrieval were lower by 5-13%. This study reveals a future possibility of photo retrieval tasks to realize the personal identification using human brain activities, which will yield rich controls of machine for the users of brain computer-interface.