A Multi-Dataset Comparative Analysis of Brain-Computer Interface Classification Techniques Using Public Neural Data

Akash Rajak; Sunil Kumar; Siddheshwari Dutt Mishra; Amit Kumar

doi:10.57159/jcmm.5.4.26679

Authors

Akash Rajak Krishna Institute of Engineering & Technology (KIET), Ghaziabad, Delhi-NCR, India https://orcid.org/0000-0001-9563-6017
Sunil Kumar Krishna Institute of Engineering & Technology (KIET), Ghaziabad, Delhi-NCR, India https://orcid.org/0009-0006-8899-4960
Siddheshwari Dutt Mishra Galgotias University, Greater Noida, India https://orcid.org/0000-0002-2298-208X
Amit Kumar Graphic Era (Deemed to be University), Dehradun, Uttarakhand, India https://orcid.org/0009-0008-9786-3131

DOI:

https://doi.org/10.57159/jcmm.5.4.26679

Keywords:

Brain–Computer Interface, Neural signal processing, EEG classification, Neurorehabilitation, Machine learning, Deep learning, Cognitive monitoring, Assistive technologies

Abstract

Brain-Computer Interface (BCI) systems allow direct communication between the human brain and external devices through the analysis of electroencephalography (EEG) signals; however, the performance and generalization capability of EEG classification models are highly dependent on characteristics of the dataset, subject variability, and evaluation parameters. In this research article, we demonstrated a comparative benchmarking framework and evaluated machine learning-based EEG classification across three publicly available datasets: BCI Competition IV Dataset 2a, PhysioNet-EEG Motor Movement/Imagery, and an Open-Closed Eyes EEG dataset. To ensure fair and reproducible evaluation, a unified processing pipeline comprising EEG preprocessing, Common Spatial Pattern (CSP) feature extraction, and five machine learning classifiers, specifically Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Random Forest (RF), Multi-Layer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost), was presented in this paper. The experimental results show substantial variation in performance across datasets. The RF achieved the highest five-fold cross-validation accuracy on the BCI Competition IV Dataset 2a, which is 55.36 ± 1.54%, while the SVM gave the best subject-independent Leave-One-Subject-Out (LOSO) accuracy of 37.50 ± 8.09%. On the other hand, with the PhysioNet dataset, the LDA achieved the highest accuracy of 60.23 ± 3.01%. In the case of the Open-Closed Eyes dataset, XGBoost achieved an accuracy of 79.82 ± 5.25%. This research demonstrated that EEG classification performance is strongly influenced by dataset complexity and inter-subject variability, highlighting the importance of standardized evaluation protocols and robust benchmarking frameworks for EEG-based BCI research.

References

[1] H. Altaheri, F. Karray, and A.-H. Karimi, "Temporal convolutional transformer for EEG based motor imagery decoding," Scientific Reports, vol. 15, p. 32959, Sept. 2025.

[2] K. K. Ang, Z. Y. Chin, C. Wang, C. Guan, and H. Zhang, "Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b," Frontiers in Neuroscience, vol. 6, 2012.

[3] A. M. Azab, L. Mihaylova, K. K. Ang, and M. Arvaneh, "Weighted transfer learning for improving motor imagery-based brain-computer interface," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, pp. 1352-1359, July 2019.

[4] B. Blankertz, R. Tomioka, S. Lemm, M. Kawanabe, and K.-R. Muller, "Optimizing spatial filters for robust EEG single-trial analysis," IEEE Signal Processing Magazine, vol. 25, no. 1, pp. 41-56, 2008.

[5] A. Craik, Y. He, and J. L. Contreras-Vidal, "Deep learning for electroencephalogram (EEG) classification tasks: A review," Journal of Neural Engineering, vol. 16, p. 031001, June 2019.

[6] S. Cui, D. Lee, and D. Wen, "Toward brain-inspired foundation model for EEG signal processing: Our opinion," Frontiers in Neuroscience, vol. 18, p. 1507654, Dec. 2024.

[7] W. Hang, W. Feng, R. Du, S. Liang, Y. Chen, Q. Wang, and X. Liu, "Cross-subject EEG signal recognition using deep domain adaptation network," IEEE Access, vol. 7, pp. 128273-128282, 2019.

[8] V. Jayaram, M. Alamgir, Y. Altun, B. Scholkopf, and M. Grosse-Wentrup, "Transfer learning in brain-computer interfaces," IEEE Computational Intelligence Magazine, vol. 11, pp. 20-31, Feb. 2016.

[9] O.-Y. Kwon, M.-H. Lee, C. Guan, and S.-W. Lee, "Subject-independent brain-computer interfaces based on deep convolutional neural networks," IEEE Transactions on Neural Networks and Learning Systems, vol. 31, pp. 3839-3852, Oct. 2020.

[10] V. J. Lawhern, A. J. Solon, N. R. Waytowich, S. M. Gordon, C. P. Hung, and B. J. Lance, "EEGNet: A compact convolutional neural network for EEG-based brain-computer interfaces," Journal of Neural Engineering, vol. 15, p. 056013, Oct. 2018.

[11] J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, "Brain-computer interfaces for communication and control," Clinical Neurophysiology, vol. 113, pp. 767-791, June 2002.

[12] G. Schalk, D. J. McFarland, T. Hinterberger, N. Birbaumer, and J. R. Wolpaw, "BCI2000: A general-purpose brain-computer interface (BCI) system," IEEE Transactions on Biomedical Engineering, vol. 51, pp. 1034-1043, June 2004.

[13] L. F. Nicolas-Alonso and J. Gomez-Gil, "Brain computer interfaces, a review," Sensors, vol. 12, pp. 1211-1279, Jan. 2012.

[14] M. Tangermann, K.-R. Muller, A. Aertsen, N. Birbaumer, C. Braun, C. Brunner, R. Leeb, C. Mehring, K. J. Miller, G. R. Muller-Putz, G. Nolte, G. Pfurtscheller, H. Preissl, G. Schalk, A. Schlogl, C. Vidaurre, S. Waldert, and B. Blankertz, "Review of the BCI competition IV," Frontiers in Neuroscience, vol. 6, 2012.

[15] A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, and H. E. Stanley, "PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals," Circulation, vol. 101, pp. e215-e220, June 2000.

[16] V. Jayaram and A. Barachant, "MOABB: Trustworthy algorithm benchmarking for BCIs," Journal of Neural Engineering, vol. 15, p. 066011, Dec. 2018.

[17] F. Lotte, L. Bougrain, A. Cichocki, M. Clerc, M. Congedo, A. Rakotomamonjy, and F. Yger, "A review of classification algorithms for EEG-based brain-computer interfaces: A 10 year update," Journal of Neural Engineering, vol. 15, p. 031005, June 2018.

[18] R. Shoorangiz, S. J. Weddell, and R. D. Jones, "EEG-based machine learning: Theory and applications," in Handbook of Neuroengineering (N. V. Thakor, ed.), pp. 2463-2501, Singapore: Springer Nature Singapore, 2023.

[19] Y. Roy, H. Banville, I. Albuquerque, A. Gramfort, T. H. Falk, and J. Faubert, "Deep learning-based electroencephalography analysis: A systematic review," Journal of Neural Engineering, vol. 16, p. 051001, Oct. 2019.

[20] R. T. Schirrmeister, J. T. Springenberg, L. D. J. Fiederer, M. Glasstetter, K. Eggensperger, M. Tangermann, F. Hutter, W. Burgard, and T. Ball, "Deep learning with convolutional neural networks for EEG decoding and visualization," Human Brain Mapping, vol. 38, pp. 5391-5420, Nov. 2017.

[21] D. Wu, B. Lance, and V. Lawhern, "Transfer learning and active transfer learning for reducing calibration data in single-trial classification of visually-evoked potentials," in 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), (San Diego, CA, USA), pp. 2801-2807, IEEE, Oct. 2014.

[22] S. Liang, C. Xuan, W. Hang, B. Lei, J. Wang, J. Qin, K.-S. Choi, and Y. Zhang, "Domain-generalized EEG classification with category-oriented feature decorrelation and cross-view consistency learning," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 31, pp. 3285-3296, 2023.

[23] L. Li and N. Sun, "Attention-based DSC-ConvLSTM for multiclass motor imagery classification," Computational Intelligence and Neuroscience, vol. 2022, pp. 1-13, May 2022.

[24] Q. Li, T. Zhang, Y. Song, and M. Sun, "Transformer-based spatial-temporal feature learning for P300," in 2022 16th ICME International Conference on Complex Medical Engineering (CME), (Zhongshan, China), pp. 310-313, IEEE, Nov. 2022.

[25] X. Wang, K. Zhao, E. Shi, S. Yu, G. Chen, and S. Zhang, "ST-GF: Graph-based fusion of spatial and temporal features for EEG motor imagery decoding," in 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), (Lisbon, Portugal), pp. 3811-3816, IEEE, Dec. 2024.

[26] A. Rajak, "Neural signatures of alcoholism revealed by event-related potential analysis of open EEG data," Journal of Applied Science and Technology Trends, vol. 7, pp. 157-167, Mar. 2026.

[27] A. Gramfort, M. Luessi, E. Larson, D. A. Engemann, D. Strohmeier, C. Brodbeck, L. Parkkonen, and M. S. Hamalainen, "MNE software for processing MEG and EEG data," NeuroImage, vol. 86, pp. 446-460, Feb. 2014.