INFORMATION CHANGE THE WORLD

International Journal of Modern Education and Computer Science (IJMECS)

ISSN: 2075-0161 (Print), ISSN: 2075-017X (Online)

Published By: MECS Press

IJMECS Vol.2, No.2, Dec. 2010

P300 Detection Algorithm Based on Fisher Distance

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Author(s)

Pan WANG,Ji-zhong SHEN,Jin-he SHI

Index Terms

BCI; wavelet transform; P300; Fisher distance; Feature extraction

Abstract

With the aim to improve the divisibility of the features extracted by wavelet transformation in P300 detection, we researched the P300 frequency domain of event related potentials and the influence of mother wavelet selection towards the divisibility of extracted features, and then a novel P300 feature extraction method based on wavelet transform and Fisher distance. This can select features dynamically for a particular subject and thereby overcome 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 experiment results showed that the proposed method can increase the divisibility by 121.8% of the features extracted by wavelet transformation, and the classification results showed that the proposed method can increase the classification accuracy by 1.2% while reduce 73.5% of the classification time. At the same time, integration of multi-domain algorithm is proposed based on the research of EEG feature extraction algorithm, and can be utilized in EEG preprocessing and feature extraction, even classification.

Cite This Paper

Pan WANG,Ji-zhong SHEN,Jin-he SHI,"P300 Detection Algorithm Based on Fisher Distance", IJMECS, vol.2, no.2, pp.9-17, 2010.

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