Integrating EEG Artifact Removal with Deep Learning for Accurate Motor Imagery Classification in Acute Stroke
Received: 4 October 2025 | Revised: 15 November 2025 and 26 November 2025 | Accepted: 28 November 2025 | Online: 9 February 2026
Corresponding author: Dechrit Maneetham
Abstract
Nowadays, the number of stroke incidents has increased, particularly in aging populations and areas with limited access to healthcare. This rise has highlighted the need for innovative assistance solutions to improve the quality of life of stroke survivors. This study aimed to address the challenges of noisy and artifact-laden Electroencephalogram (EEG) data by employing preprocessing with autonomous and semi-autonomous artifact removal techniques, including the use of EEGLab. This study focuses on the classification of EEG data for stroke patients using Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs). Combining the strengths of CNNs and LSTMs, a hybrid model leverages both spatial and temporal features of EEG data to improve classification accuracy. The hybrid CNN-LSTM model outperforms individual CNN and LSTM models in classifying motor imagery tasks, demonstrating its potential for robust brain-computer interface applications in stroke rehabilitation. This integrative approach not only improves classification performance but also sets the stage for more effective therapeutic interventions, ultimately aiming to enhance patient outcomes.
Keywords:
EEG, stroke patients, artifact removal, CNN, LSTM, hybrid model, brain-computer interfaceDownloads
References
J. Li, Q. Zhong, S. Yuan, and F. Zhu, ''Trends in deaths and disability-adjusted life-years of stroke attributable to low physical activity worldwide, 1990–2019,'' BMC Public Health, vol. 23, no. 1, Nov. 2023, Art. no. 2242. DOI: https://doi.org/10.1186/s12889-023-17162-w
T. Yahya et al., ''Stroke in young adults: Current trends, opportunities for prevention and pathways forward,'' American Journal of Preventive Cardiology, vol. 3, Sept. 2020, Art. no. 100085. DOI: https://doi.org/10.1016/j.ajpc.2020.100085
L. Nedkoff, T. Briffa, D. Zemedikun, S. Herrington, and F. L. Wright, ''Global Trends in Atherosclerotic Cardiovascular Disease,'' Clinical Therapeutics, vol. 45, no. 11, pp. 1087–1091, Nov. 2023. DOI: https://doi.org/10.1016/j.clinthera.2023.09.020
R. Suppiah, N. Kim, K. Abidi, and A. Sharma, ''A comprehensive review of motor movement challenges and rehabilitative robotics,'' Smart Health, vol. 29, Sept. 2023, Art. no. 100402. DOI: https://doi.org/10.1016/j.smhl.2023.100402
Y. C. Chen, W. Chou, R. B. Hong, J. H. Lee, and J. H. Chang, ''Home-based rehabilitation versus hospital-based rehabilitation for stroke patients in post-acute care stage: Comparison on the quality of life,'' Journal of the Formosan Medical Association, vol. 122, no. 9, pp. 862–871, Sept. 2023. DOI: https://doi.org/10.1016/j.jfma.2023.05.007
J. Saraiva, G. Rosa, S. Fernandes, and J. B. Fernandes, ''Current Trends in Balance Rehabilitation for Stroke Survivors: A Scoping Review of Experimental Studies,'' International Journal of Environmental Research and Public Health, vol. 20, no. 19, Sept. 2023, Art. no. 6829. DOI: https://doi.org/10.3390/ijerph20196829
H. Yadav and S. Maini, ''Electroencephalogram based brain-computer interface: Applications, challenges, and opportunities,'' Multimedia Tools and Applications, vol. 82, no. 30, pp. 47003–47047, Dec. 2023. DOI: https://doi.org/10.1007/s11042-023-15653-x
P. Autthasan et al., ''MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification,'' IEEE Transactions on Biomedical Engineering, vol. 69, no. 6, pp. 2105–2118, Jun. 2022. DOI: https://doi.org/10.1109/TBME.2021.3137184
Κ. Μ. Tsiouris, V. C. Pezoulas, M. Zervakis, S. Konitsiotis, D. D. Koutsouris, and D. I. Fotiadis, ''A Long Short-Term Memory deep learning network for the prediction of epileptic seizures using EEG signals,'' Computers in Biology and Medicine, vol. 99, pp. 24–37, Aug. 2018. DOI: https://doi.org/10.1016/j.compbiomed.2018.05.019
S. Sheykhivand, Z. Mousavi, T. Y. Rezaii, and A. Farzamnia, ''Recognizing Emotions Evoked by Music Using CNN-LSTM Networks on EEG Signals,'' IEEE Access, vol. 8, pp. 139332–139345, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.3011882
A. Tiwari, ''A logistic binary Jaya optimization-based channel selection scheme for motor-imagery classification in brain-computer interface,'' Expert Systems with Applications, vol. 223, Aug. 2023, Art. no. 119921. DOI: https://doi.org/10.1016/j.eswa.2023.119921
W. A. Awuah et al., ''Bridging Minds and Machines: The Recent Advances of Brain-Computer Interfaces in Neurological and Neurosurgical Applications,'' World Neurosurgery, vol. 189, pp. 138–153, Sept. 2024. DOI: https://doi.org/10.1016/j.wneu.2024.05.104
M. Varlı and H. Yılmaz, ''Multiple classification of EEG signals and epileptic seizure diagnosis with combined deep learning,'' Journal of Computational Science, vol. 67, Mar. 2023, Art. no. 101943. DOI: https://doi.org/10.1016/j.jocs.2023.101943
J. R. De Miras, A. J. Ibáñez-Molina, M. F. Soriano, and S. Iglesias-Parro, ''Schizophrenia classification using machine learning on resting state EEG signal,'' Biomedical Signal Processing and Control, vol. 79, Jan. 2023, Art. no. 104233. DOI: https://doi.org/10.1016/j.bspc.2022.104233
F. Hassan, S. F. Hussain, and S. M. Qaisar, ''Fusion of multivariate EEG signals for schizophrenia detection using CNN and machine learning techniques,'' Information Fusion, vol. 92, pp. 466–478, Apr. 2023. DOI: https://doi.org/10.1016/j.inffus.2022.12.019
M. Jafari et al., ''Emotion recognition in EEG signals using deep learning methods: A review,'' Computers in Biology and Medicine, vol. 165, Oct. 2023, Art. no. 107450. DOI: https://doi.org/10.1016/j.compbiomed.2023.107450
X. Wang, Y. Wang, D. Liu, Y. Wang, and Z. Wang, ''Automated recognition of epilepsy from EEG signals using a combining space–time algorithm of CNN-LSTM,'' Scientific Reports, vol. 13, no. 1, Sept. 2023, Art. no. 14876. DOI: https://doi.org/10.1038/s41598-023-41537-z
S. Shanmugam and S. Dharmar, ''A CNN-LSTM hybrid network for automatic seizure detection in EEG signals,'' Neural Computing and Applications, vol. 35, no. 28, pp. 20605–20617, Oct. 2023. DOI: https://doi.org/10.1007/s00521-023-08832-2
H. Albaqami, G. M. Hassan, and A. Datta, ''MP-SeizNet: A multi-path CNN Bi-LSTM Network for seizure-type classification using EEG,'' Biomedical Signal Processing and Control, vol. 84, Jul. 2023, Art. no. 104780. DOI: https://doi.org/10.1016/j.bspc.2023.104780
J. Wang, S. Cheng, J. Tian, and Y. Gao, ''A 2D CNN-LSTM hybrid algorithm using time series segments of EEG data for motor imagery classification,'' Biomedical Signal Processing and Control, vol. 83, May 2023, Art. no. 104627. DOI: https://doi.org/10.1016/j.bspc.2023.104627
X. Liu, S. Xiong, X. Wang, T. Liang, H. Wang, and X. Liu, ''A compact multi-branch 1D convolutional neural network for EEG-based motor imagery classification,'' Biomedical Signal Processing and Control, vol. 81, Mar. 2023, Art. no. 104456. DOI: https://doi.org/10.1016/j.bspc.2022.104456
J. Zhang and K. Li, ''A multi-view CNN encoding for motor imagery EEG signals,'' Biomedical Signal Processing and Control, vol. 85, Aug. 2023, Art. no. 105063. DOI: https://doi.org/10.1016/j.bspc.2023.105063
S. Soni, S. Chaudhary, and K. P. Miyapuram, ''Enhancing Motor Imagery based Brain Computer Interfaces for Stroke Rehabilitation,'' in Proceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD), Bangalore India, Jan. 2024, pp. 439–443. DOI: https://doi.org/10.1145/3632410.3632441
C. B. Gonsisko, D. P. Ferris, and R. J. Downey, ''iCanClean Improves Independent Component Analysis of Mobile Brain Imaging with EEG,'' Sensors, vol. 23, no. 2, Jan. 2023, Art. no. 928. DOI: https://doi.org/10.3390/s23020928
K. Kotowski, J. Ochab, K. Stapor, and W. Sommer, ''The importance of ocular artifact removal in single-trial ERP analysis: The case of the N250 in face learning,'' Biomedical Signal Processing and Control, vol. 79, Jan. 2023, Art. no. 104115. DOI: https://doi.org/10.1016/j.bspc.2022.104115
K. Kyriaki, D. Koukopoulos, and C. A. Fidas, ''A Comprehensive Survey of EEG Preprocessing Methods for Cognitive Load Assessment,'' IEEE Access, vol. 12, pp. 23466–23489, 2024. DOI: https://doi.org/10.1109/ACCESS.2024.3360328
K. Gramann, ''Mobile EEG for neurourbanism research - What could possibly go wrong? A critical review with guidelines,'' Journal of Environmental Psychology, vol. 96, Jun. 2024, Art. no. 102308. DOI: https://doi.org/10.1016/j.jenvp.2024.102308
"EEG datasets of stroke patients." figshare, Dec. 07, 2022.
A. Raza and M. Z. Yusoff, ''Development of a CNN-LSTM Deep Learning Model for Motor Imagery EEG Classification for BCI Applications,'' Engineering, Technology & Applied Science Research, vol. 15, no. 3, pp. 22705–22711, Jun. 2025. DOI: https://doi.org/10.48084/etasr.9945
H. Liu et al., ''An EEG motor imagery dataset for brain computer interface in acute stroke patients,'' Scientific Data, vol. 11, no. 1, Jan. 2024, Art. no. 131. DOI: https://doi.org/10.1038/s41597-023-02787-8
D. Dadebayev, W. W. Goh, and E. X. Tan, ''EEG-based emotion recognition: Review of commercial EEG devices and machine learning techniques,'' Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 7, pp. 4385–4401, July 2022. DOI: https://doi.org/10.1016/j.jksuci.2021.03.009
A. Delorme, ''EEG is better left alone,'' Scientific Reports, vol. 13, no. 1, Feb. 2023, Art. no. 2372. DOI: https://doi.org/10.1038/s41598-023-27528-0
M. Fayaz, ''The bibliometric analysis of EEGLAB software in the Web of Science indexed articles,'' Neuroscience Informatics, vol. 4, no. 1, Mar. 2024, Art. no. 100154. DOI: https://doi.org/10.1016/j.neuri.2023.100154
J. D. Nielsen, O. Puonti, R. Xue, A. Thielscher, and K. H. Madsen, ''Evaluating the influence of anatomical accuracy and electrode positions on EEG forward solutions,'' NeuroImage, vol. 277, Aug. 2023, Art. no. 120259. DOI: https://doi.org/10.1016/j.neuroimage.2023.120259
F. Lopes et al., ''Removing artefacts and periodically retraining improve performance of neural network-based seizure prediction models,'' Scientific Reports, vol. 13, no. 1, Apr. 2023, Art. no. 5918. DOI: https://doi.org/10.1038/s41598-023-30864-w
R. K. Das, A. Martin, T. Zurales, D. Dowling, and A. Khan, ''A Survey on EEG Data Analysis Software,'' Sci, vol. 5, no. 2, Jun. 2023, Art. no. 23. DOI: https://doi.org/10.3390/sci5020023
M. G. Asogbon et al., ''Analysis of Artifactual Components Rejection Threshold towards Enhanced Characterization of Neural Activity in Post-Stroke Survivor,'' in 2023 45th Annual International syrence of the IEEE Engineering in Medicine & Biology Society (EMBC), Sydney, Australia, Jul. 2023, pp. 1–5. DOI: https://doi.org/10.1109/EMBC40787.2023.10340688
X. Zhang, L. He, Q. Gao, and N. Jiang, ''Performance of the Action Observation-Based Brain–Computer Interface in Stroke Patients and Gaze Metrics Analysis,'' IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 32, pp. 1370–1379, 2024. DOI: https://doi.org/10.1109/TNSRE.2024.3379995
Downloads
How to Cite
License
Copyright (c) 2025 Yamin Thwe, Dechrit Maneetham, Padma Nyoman Crisnapati
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
