期刊名称：International Journal of Education and Management Engineering(IJEME)
印刷版ISSN：2305-3623
电子版ISSN：2305-8463
出版年度：2011
卷号：1
期号：6
页码：36-43
出版社：MECS Publisher
摘要：With the aim to improve the divisibility of the features extracted by wavelet transform in P300 detection, we research the P300 frequency domain of event related potentials and the influence of mother wavelet selection towards the divisibility of extracted features, and then a new P300 feature extraction method based on wavelet transform and Fisher distance is proposed, which overcomes the drawbacks of no systematic feature selection method during traditional P300 feature extraction based on wavelet transform. In this paper, both the BCI Competition 2003 and the BCI Competition 2005 data sets of P300 were used for validation, the results showed that the proposed method can increase the divisibility by 121.8% of the features extracted by wavelet transform, and contribute to the followed classification.
