A Robust P300-Speller Brain-Computer Interface Based on Kernel PCA

Abstract:

P300 speller is a well-known brain-computer interface (BCI), which allows patients with severe motor disabilities to spell words through the recognition on patients’ brain activity measured by electroencephalography (EEG). The brain-activity recognition is essentially a task of detecting of P300 responses in EEG signals. Support vector machine (SVM) has been a widely-used P300 detector in existing works. However, SVM is computationally expensive, greatly reducing the usability of the speller BCI for practical use. To address this issue, we propose in this paper a novel P300 detector, which is based on the kernel principal component analysis (KPCA). The proposed detector has a lower computational complexity, and can measure the belongingness of an input EEG to P300 class by the construction of EEG in nonlinear eigenspaces. Results carried out on subjects show that the proposed method is able to significantly shorten offline training sessions of the speller BCI while achieving high online P300-detection accuracy.

• Periodical: Applied Mechanics and Materials (Volumes 300-301)
• Pages: 721-724
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.300-301.721
• Online since: February 2013
• Authors: Yi Hung Liu, Jui Tsung Weng, Han Pang Huang, Jyh Tong Teng
• Keywords: Brain Computer Interface (BCI), EEG, P300, Principal Component Analysis (PCA)

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