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Open AccessArticle
A Hybrid CNN-LSTM Approach for Muscle Artifact Removal from EEG Using Additional EMG Signal Recording
1
Faculty of Electrical Engineering, Warsaw University of Technology, Pl. Politechniki 1, 00-661 Warsaw, Poland
2
1st Military Clinical Hospital with Outpatient Clinic, Municipal Non-Profit Healthcare Facility in Lublin Neurosurgery Department, ul. Kościuszki 30, 19-300 Ełk, Poland
3
Department of Artificial Intelligence, Institute of Information Technology, Warsaw University of Life Sciences, 02-776 Warsaw, Poland
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(9), 4953; https://doi.org/10.3390/app15094953 (registering DOI)
Submission received: 1 April 2025
/
Revised: 24 April 2025
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Accepted: 27 April 2025
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Published: 29 April 2025
Abstract
Removing artifacts from electroencephalography (EEG) signals is a common technique. Although numerous algorithms have been proposed, most rely solely on EEG data. In this study, we introduce a novel approach utilizing a hybrid convolutional neural network–long short-term memory (CNN-LSTM) architecture alongside simultaneous recording of facial and neck EMG signals. This setup enables the precise elimination of artifacts from the EEG signal. To validate the method, we collected a dataset from 24 participants who were presented with a light-emitting diode (LED) stimulus that elicited steady-state visual evoked potentials (SSVEPs) while they performed strong jaw clenching, an action known to induce significant artifacts. We then assessed the algorithm’s ability to remove artifacts while preserving SSVEP responses. The results were compared against other commonly used algorithms, such as independent component analysis and linear regression. The findings demonstrate that the proposed method exhibits excellent performance, effectively removing artifacts while retaining the EEG signal’s useful components.
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MDPI and ACS Style
Kołodziej, M.; Jurczak, M.; Majkowski, A.; Rysz, A.; Świderski, B.
A Hybrid CNN-LSTM Approach for Muscle Artifact Removal from EEG Using Additional EMG Signal Recording. Appl. Sci. 2025, 15, 4953.
https://doi.org/10.3390/app15094953
AMA Style
Kołodziej M, Jurczak M, Majkowski A, Rysz A, Świderski B.
A Hybrid CNN-LSTM Approach for Muscle Artifact Removal from EEG Using Additional EMG Signal Recording. Applied Sciences. 2025; 15(9):4953.
https://doi.org/10.3390/app15094953
Chicago/Turabian Style
Kołodziej, Marcin, Marcin Jurczak, Andrzej Majkowski, Andrzej Rysz, and Bartosz Świderski.
2025. "A Hybrid CNN-LSTM Approach for Muscle Artifact Removal from EEG Using Additional EMG Signal Recording" Applied Sciences 15, no. 9: 4953.
https://doi.org/10.3390/app15094953
APA Style
Kołodziej, M., Jurczak, M., Majkowski, A., Rysz, A., & Świderski, B.
(2025). A Hybrid CNN-LSTM Approach for Muscle Artifact Removal from EEG Using Additional EMG Signal Recording. Applied Sciences, 15(9), 4953.
https://doi.org/10.3390/app15094953
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