Single channel approach for filtering EEG signals strongly contaminated with facial EMG

Abstract

Background Eliminating facial electromyography (EMG) from the electroencephalogram (EEG) is essential for the accuracy of applications such as brain computer interfaces (BCIs) and quantification of brain functionality. Although it is possible to find several studies that address EEG filtering, there is lack of researches that improve the filtering of EEG strongly corrupted by EMG signals with single-channel approaches, which are necessary in situations in which the number of available channels is reduced for the application of filtering methods based on multichannel techniques. In this context, this research proposes an EEG denoising method for filtering EMG from the masseter and frontal. This method, so-called EMDRLS, combines the use of Empirical Mode Decomposition (EMD) and a Recursive Least Square (RLS) filter to attenuate facial EMG noise from EEG. The results were compared with those obtained from Wavelet, EMD, Wiener and Wavelet-RLS (WRLS) filters. Besides the visual inspection of the resultant waveform of filtered signals, the following objective metrics were employed to contrast the performance of the filtering methods: (i) the signal-noise ratio (SNR) of the contaminated signal, (ii) the root mean square error (RMSE) between the power spectrum of artifact free and filtered EEG epochs, (iii) the spectral preservation of brain rhythms (i.e., delta, theta, alpha, beta, and gamma rhythms) of filtered signals.Results The EMDRLS method yielded filtered EEG signals with SNR varying from 0 to 10 dB for EEG signals with SNR below -10dB. The Spearman’s correlation coefficient estimated between the SNR of filtered and corrupted signals was below 0.04, suggesting, in the evaluated conditions, the independence of the EMDRLS filtering performance to the SNR of noisy signals. The technique also improved the RMSE between the power spectrum of artifact free and filtered EEG epochs by a factor of 27 (from 5.429 to 0.197) in the most corrupted EEG channels with the masseter muscle contraction. The Kruskal-Wallis test and the Tukey-Kramer post-hoc test (p < 0.05) confirmed the preservation of all brain rhythms given by EEG signals filtered with the EMDRLS method.Conclusions The results showed that the single-channel EMDRLS method can be applied to highly contaminated EEG signals by facial EMG signal with performance superior to that of the compared methods. The method can be applied for the offline filtering of EEG signals contaminated by facial EMG.