Effective automated method for detection and suppression of muscle artefacts from single‐channel EEG signal. Issue 2 (14th April 2020)

• Record Type: Journal Article
• Authors: Saini, Manali; Satija, Udit; Upadhayay, Madhur Deo
• Is Part Of: Healthcare technology letters. Volume 7:Issue 2(2020)
• Page Start: 35
• Page End: 40
• Publication Date: 2020-04-14
• Subjects: electroencephalography -- medical signal processing -- electromyography; effective automated method -- muscle artefacts -- single‐channel EEG signal -- single‐channel electroencephalogram signal -- variational mode decomposition -- VMD -- zero crossings -- threshold criterion -- reference electromyogram -- input EEG signal -- MAs‐free EEG signal -- EMG signals -- publicly available databases -- Mendeley database -- epileptic Bonn database -- mental arithmetic tasks database -- reconstructed EEG signal; Biomedical engineering -- Periodicals; Medical technology -- Periodicals; 610.28
• DOI: 10.1049/htl.2019.0053
• Languages: English
• ISSNs: 2053-3713

Abstract

Abstract : This Letter proposes an automated method for the detection and suppression of muscle artefacts (MAs) in the single‐channel electroencephalogram (EEG) signal based on variational mode decomposition (VMD) and zero crossings count threshold criterion without the use of reference electromyogram (EMG). The proposed method involves three major steps: decomposition of the input EEG signal into two modes using VMD; detection of MAs based on zero crossings count thresholding in the second mode; retention of the first mode as MAs‐free EEG signal only after detection of MAs in the second mode. The authors evaluate the robustness of the proposed method on a variety of EEG and EMG signals taken from publicly available databases, including Mendeley database, epileptic Bonn database and EEG during mental arithmetic tasks database (EEGMAT). Evaluation results using different objective performance metrics depict the superiority of the proposed method as compared to existing methods while preserving the clinical features of the reconstructed EEG signal.