Investigation of a Rapid EEG Feature Extraction Method Using a Spectrum Observer

Abstract

Numerous studies of brain-computer interface (BCI) attempt to operate something by estimating the brain state. They often try to extract frequency domain features from electroencephalography (EEG). In real-time BCI, bandpass filter (BPF) based feature extraction remains a critical technique. However, due to the low-frequency and narrow-band nature of primary EEG components, the BPF's response delay cannot be ignored. Hence, we propose the spectrum observer that decomposes EEG signals into combinations of sine waves at known frequencies. First, we implemented the spectrum observer as a stationary Kalman filter. Next, we validated that the proposed method achieves feature extraction from raw EEG with the same accuracy as BPF. Finally, response tests with simple signals indicated that the proposed method achieves approximately a 0.1-second reduction in response time compared to BPF.