Estimation of EEG during Voluntary Movement by Using Kalman Smoother Based BMFLC

The electroencephalogram (EEG) is a useful tool to let researchers understand the brain states. Frequency analysis of EEG signal during different mental tasks received significant attention. The band-limited Fourier linear combiner (BMFLC) is a truncated Fourier series model for EEG signal. In this paper a time varying state-space model of BMFLC have been proposed. By using Kalman smoother to update the adaptive weights in the BMFLC, an accurate signal tracking performance is obtained. By using the adaptive weights of BMFLC estimated from Kalman smoother, an accurate time frequency decomposition can also be achieved. Since the EEG is non-stationarity signal, the time-frequency analysis is essential to analyze brain states during different mental tasks. A study is conducted on 3 subjects to identify the subject specified frequency band during the voluntary movement.