ItemConference Paper

Advancements in EEG and EMG Signals for Motor Imagery Classification and Artifact Removal: A Comprehensive Review and Analysis

Title
Advancements in EEG and EMG Signals for Motor Imagery Classification and Artifact Removal: A Comprehensive Review and Analysis
Creator
Kushwah, Piyush; Tiwari, Vibha; Shukla, Sunil Kumar; Tiwari, Akshra
Description
An essential noninvasive method for assessing brain electrical activity and gaining important knowledge about how the brain functions is electroencephalography (EEG). Understanding the brain's reactions to particular sensory, cognitive, or motor events requires understanding event-related potentials (ERPs), which are derived from EEG. By displaying variations in frequency content across time, time- frequency analysis improves ERP interpretation. Each of the five EEG frequency bands - delta, theta, alpha, beta, and gamma - has a unique clinical significance and is linked to different physiological and cognitive processes. In order to improve motor control and rehabilitation, this work focuses on the development of NeuroMotor Fusion approaches, which integrate EEG and Electromyography (EMG) signals for motor imagery classification. It looks at new developments in the classification of motor imagery and investigates cutting edge methods such as VR motor priming and brain-computer interfaces (BCIs). The study also discusses the difficulties in removing artifacts from EEG and EMG signals, using hybrid techniques to reduce ocular and muscular artifacts. The study produced a 96.2% accuracy rate in motor function enhancement using the ShallowFBCSPNet model architecture and the MOABBDataset "BNCI2014-001". These findings show that NeuroMotor Fusion has a great deal of promise for use in neurological disease support, individualized motor skill training, and rehabilitation. 2025 IEEE.
Source
2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Brain-Computer Interfaces (BCIs); EEG signals; EMG signals; Motor imagery classification; Motor rehabilitation; Neurotechnology
Coverage
Kushwah P., Madhav Institute of Technology & Science, Centre for Artificial Intelligence, Gwalior, India; Tiwari V., Madhav Institute of Technology & Science, Centre for Artificial Intelligence, Gwalior, India; Shukla S.K., Madhav Institute of Technology & Science, Centre for Artificial Intelligence, Gwalior, India; Tiwari A., Christ University, School of Sciences, Delhi, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833152169-1;
Format
online
Language
English
Type
Conference paper

Citation

Kushwah, Piyush; Tiwari, Vibha; Shukla, Sunil Kumar; Tiwari, Akshra, “Advancements in EEG and EMG Signals for Motor Imagery Classification and Artifact Removal: A Comprehensive Review and Analysis,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25854.

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