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

Please wait, while we are loading the content...

Biosurface and Biotribology

CAAI Transactions on Intelligence Technology

CIRED - Open Access Proceedings Journal

Chinese Journal of Electronics

Cognitive Computation and Systems

Electronics Letters

Energy Conversion and Economics

Engineering & Technology

Engineering Biology

Healthcare Technology Letters

Volume 8 (2021)

Volume 7 (2020)

Volume 7 (2020) : Issue 6, Dec

Volume 7 (2020) : Issue 5, Oct

Volume 7 (2020) : Issue 4, Aug

Volume 7 (2020) : Issue 3, Jun

Volume 7 (2020) : Issue 2, Apr

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

Evaluating cognitive task result through heart rate pattern analysis

Severity detection tool for patients with infectious disease

Volume 7 (2020) : Issue 1, Feb

IET Circuits, Devices & Systems

IET Collaborative Intelligent Manufacturing

IET Communications

IET Computer Vision

IET Computers & Digital Techniques

IET Control Theory & Applications

IET Cyber-Physical Systems: Theory & Applications

IET Cyber-Systems and Robotics

IET Electric Power Applications

IET Electrical Systems in Transportation

IET Energy Systems Integration

IET Generation, Transmission & Distribution

IET Image Processing

IET Information Security

IET Intelligent Transport Systems

IET Microwaves, Antennas & Propagation

IET Nanobiotechnology

IET Nanodielectrics

IET Networks

IET Optoelectronics

IET Power Electronics

IET Quantum Communication

IET Radar, Sonar & Navigation

IET Renewable Power Generation

IET Science, Measurement & Technology

IET Signal Processing

IET Wireless Sensor Systems

Micro & Nano Letters

The Journal of Engineering

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

Content Provider	IET Digital Library
Author	Saini, Manali Satija, Udit Upadhayay, Madhur Deo
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.
Starting Page	35
Ending Page	40
Page Count	6
Volume Number	7
e-ISSN	20533713
Issue Number	Issue 2, Apr (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/htl/7/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/htl.2019.0053
Journal	Healthcare Technology Letters
Publisher	The Institution of Engineering and Technology
Publisher Date	2020-04-14
Access Restriction	Open
Rights License	Creative Commons Attribution -Non Commercial License (http://creativecommons.org/licenses/by-nc/3.0/)
Subject Keyword	Bioelectric Signal Biology And Medical Computing Digital Signal Processing Effective Automated Method Electrical Activity in Neurophysiological Processes Electrodiagnostics And Other Electrical Measurement Technique Electroencephalography Electromyography EMG Signal Epileptic Bonn Database Input EEG Signal MAs-free EEG Signal Medical Signal Processing Mendeley Database Mental Arithmetic Task Database Muscle Artefacts Publicly Available Database Reconstructed EEG Signal Reference Electromyogram Signal Processing And Detection Single-channel EEG Signal Single-channel Electroencephalogram Signal Statistics Threshold Criterion Variational Mode Decomposition VMD Zero Crossings
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Removal of muscle artefacts from few-channel EEG recordings based on multivariate empirical mode decomposition and independent vector analysis

Novel eye-blink artefact detection algorithm from raw EEG signals using FCN-based semantic segmentation method

FAUST Multiplatform toolbox for Body Brain Digital Musical Instruments

Automatic correction of short-duration artefacts in single-channel EEG recording: a group-sparse signal denoising algorithm

Automated eye blink artefact removal from EEG using support vector machine and autoencoder

SeizureNet: a model for robust detection of epileptic seizures based on convolutional neural network

Ensemble classifier for driver's fatigue detection based on a single EEG channel

State-of-the-art methods and future perspectives for personal recognition based on electroencephalogram signals

Multifractal detrended fluctuation analysis based novel feature extraction technique for automated detection of focal and non-focal electroencephalogram signals

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

Similar Documents

Removal of muscle artefacts from few-channel EEG recordings based on multivariate empirical mode decomposition and independent vector analysis

Novel eye-blink artefact detection algorithm from raw EEG signals using FCN-based semantic segmentation method

FAUST Multiplatform toolbox for Body Brain Digital Musical Instruments

Automatic correction of short-duration artefacts in single-channel EEG recording: a group-sparse signal denoising algorithm

Automated eye blink artefact removal from EEG using support vector machine and autoencoder

SeizureNet: a model for robust detection of epileptic seizures based on convolutional neural network

Ensemble classifier for driver's fatigue detection based on a single EEG channel

State-of-the-art methods and future perspectives for personal recognition based on electroencephalogram signals

Multifractal detrended fluctuation analysis based novel feature extraction technique for automated detection of focal and non-focal electroencephalogram signals

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