Rhythmic component extraction for multi-channel EEG data analysis

A practical method for extracting and enhancing a rhythmic waveform appearing in multi-channel electroencephalogram (EEQ) data is proposed. In order to facilitate clinical diagnosis and/or implement so-called brain computer interface (BCI), detecting the rhythmic activity from EEQ data recorded in a noisy environment is crucial; however, classical signal processing techniques like linear filtering or the Fourier transform cannot detect such a rhythmic signal if the power of noise is so large. This paper presents a simple but practical method for extracting a rhythmic signal by fully exploiting the multi-variate nature of EEQ data. The rhythmic component of interest is estimated as the weighted sum of multi-channel signals, and the optimal weights are then derived so as to maximize the power of the component. After the derivation is illustrated, adaptive weights, which give a new time-frequency analysis, are introduced. Moreover, the application to recently developed empirical mode decomposition (EMD) is presented. Experimental results on real EEQ data support the analysis.