Treffer: Decoding muscle activity via CNN-LSTM from 3D spatiotemporal EEG.

Title:
Decoding muscle activity via CNN-LSTM from 3D spatiotemporal EEG.
Authors:
Amiri G; Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran., Shalchyan V; Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran. Electronic address: shalchyan@iust.ac.ir.
Source:
Computer methods and programs in biomedicine [Comput Methods Programs Biomed] 2025 Nov; Vol. 271, pp. 108983. Date of Electronic Publication: 2025 Jul 22.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Elsevier Scientific Publishers Country of Publication: Ireland NLM ID: 8506513 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-7565 (Electronic) Linking ISSN: 01692607 NLM ISO Abbreviation: Comput Methods Programs Biomed Subsets: MEDLINE
Imprint Name(s):
Publication: Limerick : Elsevier Scientific Publishers
Original Publication: Amsterdam : Elsevier Science Publishers, c1984-
MeSH Terms:
Electroencephalography*/methods , Neural Networks, Computer* , Muscle, Skeletal*/physiology, Humans ; Brain-Computer Interfaces ; Male ; Adult ; Electromyography ; Female ; Deep Learning ; Signal Processing, Computer-Assisted ; Linear Models ; Young Adult ; Algorithms
Contributed Indexing:
Keywords: Decoding; Deep learning; Electroencephalogram (EEG); Electromyogram (EMG); Muscle activity; brain-computer interfaces (BCIs)
Entry Date(s):
Date Created: 20250731 Date Completed: 20250906 Latest Revision: 20250906
Update Code:
20250907
DOI:
10.1016/j.cmpb.2025.108983
PMID:
40743699
Database:
MEDLINE

Weitere Informationen

Objective: Reconstructing muscle activity from electromyogram (EMG) data using non-invasive electroencephalogram (EEG) signals could lead to significant advancements in brain-computer interfaces (BCIs). However, extracting muscle-related signals from EEG poses considerable challenges due to the mixed nature of signals captured by EEG sensors from various cortical regions.
Approach: This study introduces a new method for estimating muscle activity from non-invasive EEG signals while participants performed the grasp and lift (GAL) task. Envelopes of the delta, theta, alpha, beta, and gamma frequency bands were chosen as EEG features for the decoding models, computed similarly to muscle activity (EMG envelopes). These were converted into three-dimensional spatiotemporal matrices based on EEG electrode locations. A deep learning model, combining convolutional neural networks (CNN) for spatial and long short-term memory (LSTM) network for temporal EEG information extraction, was applied. This model was compared with two linear and nonlinear decoding methods: multivariate linear regression (mLR) and multilayer perceptron (MLP).
Main Results: The average ± standard deviation of the normalized root mean square error (nRMSE), coefficient of determination (R²), and correlation coefficient (CC) between the estimated and actual muscle activity of two muscles in five participants were 0.21 ± 0.05, 0.54 ± 0.17, and 0.76 ± 0.10, respectively. The CNN-LSTM model outperformed both mLR and MLP approaches (p-value < 0.016), with higher frequencies proving more effective for decoding.
Significance: The proposed model effectively captures nonlinear relationships between brain and muscle activities, indicating its potential to enhance the accuracy and reliability of non-invasive BCIs.
(Copyright © 2025. Published by Elsevier B.V.)

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.