Open AccessEffective automated method for detection and suppression of muscle artefacts from single‐channel EEG signal
Author(s) -
Saini Manali,
Satija Udit,
Upadhayay Madhur Deo
Publication year - 2020
Publication title -
healthcare technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.45
H-Index - 19
ISSN - 2053-3713
DOI - 10.1049/htl.2019.0053
Subject(s) - electroencephalography , computer science , robustness (evolution) , thresholding , artificial intelligence , pattern recognition (psychology) , signal (programming language) , channel (broadcasting) , speech recognition , psychology , computer network , biochemistry , chemistry , psychiatry , image (mathematics) , gene , programming language
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.