EEG-Based Brain–Computer Interface for Intelligent Wheelchair Control: A Machine Learning Approach
الكلمات المفتاحية:
Brain–Computer Interface، EEG، Motor Imagery، Filter-Bank Common Spatial Patternss، Wheelchair Control، Support Vector Machineالملخص
Electroencephalography EEG -based motor imagery brain–computer interfaces BCIs offer a noninvasive means of control for individuals with severe motor impairments, but their translation into physically actuated systems remains challenged by the inherent uncertainty of EEG decoding. This thesis presents the design, implementation, and evaluation of an EEG-based motor imagery BCI for wheelchair control, integrating a Filter Bank Common Spatial Pattern FBCSP feature extraction pipeline with a comparative evaluation of three classifiers, Support Vector Machine SVM, k-Nearest Neighbors KNN, and Linear Discriminant Analysis LDA, on the BCI Competition IV Dataset 2a under a subject-dependent paradigm. Using five-fold stratified cross-validation, SVM achieved the highest mean accuracy 90.7 ± 2.6% and Cohen’s Kappa 0.876 ± 0.035, and was subsequently evaluated on an independent, unseen dataset via a replay based Hardware-in-the-Loop HIL methodology, achieving 85.9% accuracy. A confidence-based command validation stage further improved reliability, raising accuracy among accepted predictions to 97.6% at 60.7% coverage. The classifier output was interfaced with a physical differential-drive wheelchair prototype governed by an encoder-based closed-loop steering control scheme. A multi-tiered safety strategy, comprising confidence gating, self-terminating steering, transition braking, communication timeout supervision, and a hardware emergency cutoff, was incorporated to constrain the consequences of residual classification uncertainty. These results demonstrate that careful integration of classification, command validation, and embedded safety design can yield a robust and practical EEG-based wheelchair control framework, despite the imperfect reliability of motor imagery decoding.
المراجع
[1] M. Rashid, N. Sulaiman, A. PP Abdul Majeed, R. M. Musa, A. F. Ab. Nasir, B. S. Bari, and S. Khatun, “Current status, challenges, and possible solutions of EEG-based brain-computer interface: a comprehensive review,” Frontiers in neurorobotics, vol. 14, p. 25, 2020.
[2] A. Palumbo, V. Gramigna, B. Calabrese, and N. Ielpo, “Motorimagery EEG-based BCIs in wheelchair movement and control: A systematic literature review,” Sensors, vol. 21, no. 18, p. 6285, 2021.
[3] A. Saibene, M. Caglioni, S. Corchs, and F. Gasparini, “EEGbased BCIs on motor imagery paradigm using wearable technologies: a systematic review,” Sensors, vol. 23, no. 5, p. 2798, 2023.
[4] C. Brunner, R. Leeb, G. Muller-Putz,¨ A. Schlogl,¨ and G. Pfurtscheller, “BCI competition 2008–graz data set a,” Institute for knowledge discovery (laboratory of brain-computer interfaces), Graz University of Technology, vol. 16, no. 1-6, p. 1, 2008.
[5] Abdullah, I. Faye, and M. R. Islam, “EEG channel selection techniques in motor imagery applications: A review and new perspectives,” Bioengineering, vol. 9, no. 12, p. 726, 2022.
[6] Z. Ma, K. Wang, M. Xu, W. Yi, F. Xu, and D. Ming,
“Transformed common spatial pattern for motor imagery-based brain-computer interfaces,” Frontiers in neuroscience, vol. 17, p. 1116721, 2023.
[7] E. Dagdevir and M. Tokmakci, “Optimization of preprocessing stage in EEG based BCI systems in terms of accuracy and timing cost,” Biomedical Signal Processing and Control, vol. 67, p. 102548, 2021.
[8] K. K. Ang, Z. Y. Chin, H. Zhang, and C. Guan, “Filter bank common spatial pattern FBCSP in brain-computer interface,” in 2008 IEEE international joint conference on neural networks
(IEEE world congress on computational intelligence). IEEE, 2008, pp. 2390–2397.
[9] 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, no. 3, p. 031005, 2018.
[10] C. Zhang and A. Eskandarian, “A computationally efficient multiclass time-frequency common spatial pattern analysis on EEG motor imagery,” in 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society EMBC. IEEE, 2020, pp. 514–518.
[11] J. Dong, M. Komosar, J. Vorwerk, D. Baumgarten, and
J. Haueisen, “Scatter-based common spatial patterns–a unified spatial filtering framework,” arXiv preprint arXiv:2303.06019, 2023.
[12] B. Gu, M. Xu, L. Xu, L. Chen, Y. Ke, K. Wang, J. Tang, and D. Ming, “Optimization of task allocation for collaborative brain–computer interface based on motor imagery,” Frontiers in Neuroscience, vol. 15, p. 683784, 2021.
[13] X. Xiong, Y. Wang, T. Song, J. Huang, and G. Kang, “Improved motor imagery classification using adaptive spatial filters based on particle swarm optimization algorithm,” Frontiers in Neuroscience, vol. 17, p. 1303648, 2023.
[14] S. Kumar, A. Sharma, and T. Tsunoda, “An improved discriminative filter bank selection approach for motor imagery EEG signal classification using mutual information,” BMC bioinformatics, vol. 18, no. Suppl 16, p. 545, 2017.
[15] J. Luo, X. Gao, X. Zhu, B. Wang, N. Lu, and J. Wang, “Motor imagery EEG classification based on ensemble support vector learning,” Computer methods and programs in biomedicine, vol. 193, p. 105464, 2020.
[16] J. Ma, B. Yang, W. Qiu, Y. Li, S. Gao, and X. Xia, “A large EEG dataset for studying cross-session variability in motor imagery brain-computer interface,” Scientific Data, vol. 9, no. 1, p. 531, 2022.
[17] C. Cortes and V. Vapnik, “Support-vector networks,” Machine learning, vol. 20, no. 3, pp. 273–297, 1995.
[18] J. Jiang, C. Wang, J. Wu, W. Qin, M. Xu, and E. Yin,
“Temporal combination pattern optimization based on feature selection method for motor imagery BCIs,” Frontiers in Human Neuroscience, vol. 14, p. 231, 2020.
[19] C.-C. Chang and C.-J. Lin, “LIBSVM: A library for support vector machines,” ACM transactions on intelligent systems and technology (TIST), vol. 2, no. 3, pp. 1–27, 2011.
[20] A. Geron,´ Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. ” O’Reilly Media, Inc.”, 2022.
[21] M. Rashid, B. S. Bari, M. J. Hasan, M. A. M. Razman, R. M. Musa, A. F. Ab Nasir, and A. P. A. Majeed, “The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN,” PeerJ Computer Science, vol. 7, p. e374, 2021.
[22] T. Hastie, R. Tibshirani, J. Friedman et al., “The elements of statistical learning,” 2009.
[23] E. Mohammadi, P. G. Daneshmand, and S. M. S. M. Khorzooghi, “Electroencephalography-based brain–computer interface motor imagery classification,” Journal of Medical Signals & Sensors, vol. 12, no. 1, pp. 40–47, 2022.
[24] M. Ferreira, L. Moreira, and A. Lopes, “Differential drive kinematics and odometry for a mobile robot using TwinCAT.” Electronic Research Archive, vol. 31, no. 4, 2023.
[25] D. Milanes-Hermosilla, R. Trujillo-Codorniu, S. LamarCarbonell, R. Sagaro-Zamora, J. J. Tamayo-Pacheco, J. J. Villarejo-Mayor, and D. Delisle-Rodriguez, “Robust motor imagery tasks classification approach using bayesian neural network,” Sensors, vol. 23, no. 2, p. 703, 2023.
التنزيلات
منشور
إصدار
القسم
الرخصة
الحقوق الفكرية (c) 2026 Journal of Electrical and Electronic Engineering and Information Technology
هذا العمل مرخص بموجب Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
