Pattern recognition for brain-computer interface on disabled subjects using a wavelet transformation

The main objective of this work is to present an exploratory approach on electroencephalographic (EEG) signal, analyzing the patterns on the time-frequency plane. This work also aims to optimize the EEG signal analysis through the improvement of classifiers and, eventually, of the BCI performance. In this paper a novel exploratory approach for data mining on EEG signal based on continuous wavelet transformation (CWT) and wavelet coherence (WC) statistical analysis is introduced and applied. The CWT allows the signal underlying information content illustration by representing time-frequency patterns on Wavelet Coherence qualitative analysis. Results suggest that the proposed methodology is capable of identifying regions on time-frequency spectrum during the specified task on BCI. Furthermore, an example of a region is identified, and the patterns were classified using a radial basis function neural network (RBF-NN). This innovative characteristic of the process justify the feasibility of the proposed approach on another data mining applications. It can open new physiologic researches on this field and researches on different non-stationary time series analysis.