Abstract
An electroencephalogram (EEG) is an electrophysiological signal reflecting the functional state of the brain. As the control signal of the brain–computer interface (BCI), EEG may build a bridge between humans and computers to improve the life quality for patients with movement disorders. The collected EEG signals are extremely susceptible to the contamination of electromyography (EMG) artifacts, affecting their original characteristics. Therefore, EEG denoising is an essential preprocessing step in any BCI system. Previous studies have confirmed that the combination of ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA) can effectively suppress EMG artifacts. However, the time-consuming iterative process of EEMD may limit the application of the EEMD-CCA method in real-time monitoring of BCI. Compared with the existing EEMD, the recently proposed signal serialization based EEMD (sEEMD) is a good choice to provide effective signal analysis and fast mode decomposition. In this study, an EMG denoising method based on sEEMD and CCA is discussed. All of the analyses are carried out on semi-simulated data. The results show that, in terms of frequency and amplitude, the intrinsic mode functions (IMFs) decomposed by sEEMD are consistent with the IMFs obtained by EEMD. There is no significant difference in the ability to separate EMG artifacts from EEG signals between the sEEMD-CCA method and the EEMD-CCA method (p > 0.05). Even in the case of heavy contamination (signal-to-noise ratio is less than 2 dB), the relative root mean squared error is about 0.3, and the average correlation coefficient remains above 0.9. The running speed of the sEEMD-CCA method to remove EMG artifacts is significantly improved in comparison with that of EEMD-CCA method (p < 0.05). The running time of the sEEMD-CCA method for three lengths of semi-simulated data is shortened by more than 50%. This indicates that sEEMD-CCA is a promising tool for EMG artifact removal in real-time BCI systems.
Highlights
Brain–computer interface (BCI) is a type of human–computer interaction, which can provide a possible way to improve the quality of life for the disabled [1,2]
The multi-channel EEG signals are used as the control signals of the BCI system
Whether intrinsic mode functions (IMFs) decomposed by serialization based EEMD (sEEMD) are consistent with those generated by ensemble empirical mode decomposition (EEMD) requires further verification
Summary
Brain–computer interface (BCI) is a type of human–computer interaction, which can provide a possible way to improve the quality of life for the disabled [1,2]. With its highly accurate time resolution and excellent clinical environment applicability, the electroencephalogram (EEG) has become the main non-invasive neurophysiological recording technology used by BCI control systems to monitor brain consciousness activities [4].The EEG signal has low amplitude and high time-varying characteristics. EEG is often mixed with various artifacts generated by non-cerebral nerve tissues, such as electrooculograms, electromyograms (EMGs), electrocardiograms, and power frequency interference [5]. These interference signals and EEG signals are overlapped with each other, submerging the original waveform characteristics of EEG signals. In consideration of the complex physiological process and the insufficient prior knowledge for EMG, blind source separation (BSS) technology is often recommended to separate the EMG noise from EEG signals [10]
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