Motor imagery EEG-based person verification

Authors:
Phuoc Nguyen;Dat Tran;Xu Huang;Wanli Ma
Affiliations:
Faculty of Education Science Technology and Mathematics, University of Canberra, ACT, Australia;Faculty of Education Science Technology and Mathematics, University of Canberra, ACT, Australia;Faculty of Education Science Technology and Mathematics, University of Canberra, ACT, Australia;Faculty of Education Science Technology and Mathematics, University of Canberra, ACT, Australia
Venue:
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
Year:
2013

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Abstract

We investigate in this paper the activity-dependent person verification method using electroencephalography (EEG) signal from a person performing motor imagery tasks. Two tasks were performed in our experiments were performed. In the first task, the same motor imagery task of left hand or right hand was applied to all persons. In the second task, only the best motor imagery task for each person was performed. The Gaussian mixture model (GMM) and support vector data description (SVDD) methods were used for modelling persons. Experimental results showed that lowest person verification error rate could be achieved when each person performed his/her best motor imagery task.