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. © 2020 Institution of Engineering and Technology. All rights reserved.

Madhur Deo Upadhayay

Electrical Engineering

(SoE) School of Engineering

Saini M.

Satija U.

Fulltext

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Shiv Nadar University

Healthcare Technology Letters

An objective distortion measure is very crucial to accurately quantify the distortion introduced in the electroencephalogram (EEG) signal during the denoising process. Most of the existing algorithms report their denoising performance by comparing the original EEG signal and the reconstructed EEG signal using root mean square error (RMSE) and other similar measures. However, it is very important to quantify the distortion in each band of EEG signal since each band provides distinct information about the specific brain activity. Furthermore, quantification of band-wise distortion enables the selection of particular denoising algorithm for the application-specific EEG signal analysis. Therefore, in this paper, we propose two robust distortion measures such as weighted signal to noise ratio (WSNR) and weighted correlation coefficient (WCC) for accurately representing the objective reconstruction loss in each band. These performance measures are computed between the wavelet subbands of the original and the denoised/reconstructed signal with weights equal to the relative wavelet energy and wavelet entropy of the corresponding subband wavelet coefficients. To demonstrate the effectiveness of the proposed performance measures, we evaluate the performance of six existing denoising methods using these measures. Results depict that these measures can adequately provide high mutual agreement between objective scores and subjective analysis. © 1994-2012 IEEE.

IEEE Signal Processing Letters

Wavelet Based Waveform Distortion Measures for Assessment of Denoised EEG Quality with Reference to Noise-Free EEG Signal

Specific emitter identification is the process of identifying or discriminating different emitters based on the radio frequency fingerprints extracted from the received signal. Due to inherent non-linearities of the power amplifiers of emitters, these fingerprints provide distinguish features for emitter identification. In this paper, we develop an emitter identification based on variational mode decomposition and spectral features (VMD-SF). As VMD decomposes the received signal simultaneously into various temporal and spectral modes, we choose to explore different spectral features, including spectral flatness, spectral brightness, and spectral roll-off for improving the identification accuracy contrary to existing temporal features-based methods. For demonstrating the robustness of VMD in decomposing the received signal into emitter-specific modes, we also develop a VMD-entropy and moments (E M2) method based on existing temporal features extracted from the Hilbert Huang transform of the emitter-specific temporal modes. Our proposed method has three major steps: received signal decomposition using VMD, feature extraction, and emitter identification. We evaluate the performance of the proposed methods using the probability of correct classification (Pcc ) both in single hop and in relaying scenario by varying the number of emitters. To demonstrate the superior performance of our proposed methods, we compared our methods with the existing empirical mode decomposition-(entropy-, first-, and second-order moments) (EMD- E M2) method both in terms of Pcc and computational complexity. Results depict that the proposed VMD-SF emitter identification method outperforms the proposed VMD- E M2 method and the existing EMD- E M2 method both in single hop and relaying scenarios for a varying number of emitters. In addition, the proposed VMD-SF method has lowest computational cost as compared with the aforementioned methods. © 2005-2012 IEEE.

IEEE Transactions on Information Forensics and Security

Specific emitter identification based on variational mode decomposition and spectral features in single hop and relaying scenarios

IEEE Access

Identification and Removal of Physiological Artifacts From Electroencephalogram Signals: A Review

Data in Brief

A semi-simulated EEG/EOG dataset for the comparison of EOG artifact rejection techniques

IEEE Sensors Journal

Removing Muscle Artifacts From EEG Data: Multichannel or Single-Channel Techniques?

2016 Twenty Second National Conference on Communication (NCC)

A novel method for automatic modulation classification under non-Gaussian noise based on variational mode decomposition

Effective automated method for detection and suppression of muscle artefacts from single-channel EEG signal