Saini Manali, Satija Udit, Upadhayay Madhur Deo
Department of Electrical Engineering, Shiv Nadar University, Greater Noida, UP 201314, India.
Department of Electrical Engineering, Indian Institute of Technology Patna, Bihta, Patna 801103, Bihar, India.
Healthc Technol Lett. 2020 Apr 14;7(2):35-40. doi: 10.1049/htl.2019.0053. eCollection 2020 Apr.
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.
本文提出了一种基于变分模态分解（VMD）和过零计数阈值准则的单通道脑电图（EEG）信号中肌肉伪迹（MA）检测与抑制的自动化方法，无需使用参考肌电图（EMG）。所提出的方法包括三个主要步骤：使用VMD将输入的EEG信号分解为两个模态；基于第二模态中的过零计数阈值检测MA；仅在第二模态中检测到MA后，保留第一模态作为无MA的EEG信号。作者在从公开可用数据库获取的各种EEG和EMG信号上评估了所提出方法的鲁棒性，这些数据库包括Mendeley数据库、癫痫波恩数据库和心算任务期间的EEG数据库（EEGMAT）。使用不同客观性能指标的评估结果表明，与现有方法相比，所提出的方法具有优越性，同时保留了重建EEG信号的临床特征。
