Wavelet shrinkage applications of EEG-ECG-based human-computer interface

Abstract

In many situations where ambulatory monitoring is needed, the electroencephalogram (EEG) and the electrocardiogram (ECG) are desired to be simultaneously used. Therefore, it would be desirable if one can acquire both EEG and ECG signals with a single measurement. One of the key techniques in such kind of application is to separate EEG and ECG using signal processing techniques. In our study, we have confirmed that by applying non-cephalic reference electrode in the EEG recording, it is possible to obtain ECG signals available for signal processing. We also improved the wavelet shrinkage algorithm by performing window operations in approximation level five of wavelet components to successfully separate the EEG components from the ECG signal. Our results showed that the improved normalized power spectrum were accurate in all frequency bands for the EEG components, while the R peak detection for the ECG were 100 % successful in this study.