Motor imagery EEG-based person verification

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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
Original languageEnglish
Title of host publicationInternational Work-Conference on Artificial Neural Networks
Subtitle of host publicationAdvances in Computational Intelligence IWANN 2013
EditorsIgnacio Rojas, Gonzalo Joya, Joan Gabestany
Place of PublicationBerlin
Number of pages9
ISBN (Electronic)9783642386824
ISBN (Print)9783642386817
Publication statusPublished - 2013
Event12th International Work-Conference on Artificial Neural Networks, IWANN 2013 - Tenerife, Tenerife, Spain
Duration: 12 Jun 201314 Jun 2013


Conference12th International Work-Conference on Artificial Neural Networks, IWANN 2013
Abbreviated titleIWANN 2013


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