The novel EEMD-CCA approach effectively removes muscle artifacts from EEG recordings, outperforming existing techniques and satisfying real-time computational requirements.
Future electroencephalogram (EEG) recordings in body sensor networks are prone to be contaminated by muscle activity due to the mobile, long-term, and pervasive monitoring needs. In this paper, a novel approach for muscle artifact removal in EEG is proposed by combining ensemble empirical mode decomposition (EEMD) with canonical correlation analysis (CCA), termed as EEMD-CCA. This approach can make good use of inter-channel information. We tested the approach on simulated, semi-simulated, and real-life data sets, respectively. The approach outperformed state-of-the-art techniques, including independent component analysis, CCA, and EEMD-ICA. Statistical tests demonstrate the significance (p <; 0.01) in (semi)-simulated studies. The relative root-mean-squared error can be reduced to around 0.3 and the average correlation coefficient can be kept above 0.9 even when the contamination is quite heavy (SNR <; 2). Besides, we also tested the approach on few-channel EEG randomly selected from multichannel EEG, and obtained competitive results. The computational cost satisfies the real-time requirement. This indicates that the proposed EEMD-CCA approach is applicable under both multichannel and few-channel settings. It is an effective and efficient signal processing tool for enhancing the signal of interest in both hospital and home healthcare body sensor networks.
Chen et al. (2018) studied this question.