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IJCNN
2008
IEEE

Learning adaptive subject-independent P300 models for EEG-based brain-computer interfaces

11 years 6 months ago
Learning adaptive subject-independent P300 models for EEG-based brain-computer interfaces
Abstract— This paper proposes an approach to learn subjectindependent P300 models for EEG-based brain-computer interfaces. The P300 models are first learned using a pool of existing subjects and Fisher linear discriminant, and then autonomously adapted to the unlabeled data of a new subject using an unsupervised machine learning technique. In data analysis, we apply this technique to a set of EEG data of 10 subjects performing word spelling in an oddball paradigm. The results are very positive: the adapted models with unlabeled data yield virtually the same classification accuracy as the conventional methods with labeled data. Therefore, it proves the feasibility of P300-based BCIs which can be applied directly to a new subject without training sessions.
Shijian Lu, Cuntai Guan, Haihong Zhang
Added 31 May 2010
Updated 31 May 2010
Type Conference
Year 2008
Where IJCNN
Authors Shijian Lu, Cuntai Guan, Haihong Zhang
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