A self-paced BCI using stationary wavelet packets

Abstract

The stationary wavelet packet analysis is exploited for the first time in the design of a self-paced BCI based on mental tasks. The BCI system is custom designed to achieve a zero false positive rate, as false activations highly restricts the applications of BCIs in real life. The EEG signals of four subjects performing five different mental tasks are used as the dataset. The stationary wavelet packets decompose the signal into eight components. The features used are the autoregressive coefficients obtained by applying autoregressive modeling on the resultant wavelet components. Classification is a two-stage process. The first stage is based on quadratic discriminant analysis which is extremely fast. The second stage is a simple majority voting classifier. During model selection, which is performed via 5-folded cross-validation, the combination of decomposed components and the autoregressive model order that yield the best performance are selected. Results show enhancements in the overall performance for three subjects comparing to our previously designed BCI.