A Novel Tensorial Scheme for EEG-Based Person Identification

Biometrics have attracted growing research interests as information security and safety gain increasing attention to date. As a kind of important biomedical signal, electroencephalogram (EEG) contains valuable information about identity, emotionality, personality, and so on. Thus, automatically distinguishing the identities based on EEG is beneficial to the development of biometrics, forensics, and informatics. Although deep learning has absorbed much research attention for the issue of EEG-based person identification, the performance enhancement of this methodology seems to have hit a bottleneck recently. Hence, by rethinking the problems haunting this issue, we plan to reinvigorate the conventional method pipeline and put forward a novel and effective tensorial scheme away from the deep learning mainstream. Specifically, the proposed tensorial scheme extracts the effective tensorial representation from multichannel EEG at first; then, the scheme performs the designed tensorial learning to improve the discriminability of the feature space; and finally, the scheme carries out the devised tensorial measurement in the learned metric space for classification. Experimental results have demonstrated the superiority of the proposed scheme over the related advanced approaches by means of the challenging benchmark databases: DEAP, SEED, and DREAMER.