Feature extraction of P300s in EEG signal with discrete wavelet transform and fisher criterion

P300 Speller is a P300-based Brain-Computer Interfaces (BCIs), which help users write the desired characters to the computer screen by detecting the P300 event-related potentials in users´ electroencephalographic (EEG). This article proposes a feature extraction method for P300 detection. This method combines discrete wavelet transform (DWT) with Fisher criterion. This method, firstly transforms the EEG signal into wavelet domain by DWT. Then, the feature space where best distinguish two kinds of EEG signals will be found by means of Fisher criterion. Finally, a feature extraction matrix will be constructed to extract feature vector from EEG signals. The method was tested on EEG data that obtained by P300 Speller paradigm. The result shows that, the proposed method is better than the existing method used in BCI2000, in terms of averaged accuracy over 238 runs.