Multitask learning for EEG-based biometrics

Biometrics based on electroencephalogram (EEG) signals is an emerging research topic. Several recent results have shown its feasibility and potential for personal identification. However, they all use a single task (e.g., signals recorded during imagination of repetitive left hand movements or during resting with eyes open) for classifier design and subsequent identification. In contrast with this, in this paper multiple related tasks are used simultaneously for classifier learning. This mechanism has the advantage of integrating information from extra tasks and thus hopefully can guide classifier learning in a hypothesis space more effectively. Experimental results on EEG-based personal identification show the effectiveness of the proposed multitask learning approach.

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