This is a preview and has not been published. View submission

Feature-Based Classification of Motor Imagery Tasks using Electroencephalogram Recordings

Authors

  • Vishnu Vardhana Reddy Karna Department of Electronics and Communication Engineering, Aditya University, Surampalem, India
  • Viswavardhan Reddy Karna Department of Artificial Intelligence and Machine Learning, R V College of Engineering, Bengaluru, India
  • Aravinda Babu Tummala Department of Electronics and Communication Engineering, Chaitanya Bharathi Institute of Technology, Hyderabad, India
  • Mallikharjuna Rao G Department of Electronics and Communication Engineering, Chaitanya Bharathi Institute of Technology, Hyderabad, India
  • Venkateswarlu Mannepally Department of Electronics and Communication Engineering, Aditya University, Surampalem, India
Volume: 15 | Issue: 4 | Pages: 25641-25646 | August 2025 | https://doi.org/10.48084/etasr.11420

Received: 12 April 2025 | Revised: 11 May 2025 | Accepted: 24 May 2025 | Online: 4 July 2025


Corresponding author: Vishnu Vardhana Reddy Karna

Abstract

Stroke is recognized as a source of numerous impairments, encompassing deficits in physical, motor, and emotional functions in affected individuals. While the visible manifestations of a stroke are evident, the internal effects on the brain remain mostly enigmatic. Research has shown that utilizing motor imagery tasks via Electroencephalogram (EEG) bio signals achieves a 10% increase in accuracy relative to traditional techniques. This research work aims to employ feature extraction techniques on motor imaging tasks combining right- and left-hand grasping, utilizing motor imagery-based EEG data to extract the most pertinent features from two distinct datasets. One dataset comprises individuals with stroke, and the other consists of healthy individuals. Techniques such as the Common Spatial Filter (CSP) and the Filter Bank Common Spatial Filter (FBCSP) are employed to extract relevant features from the processed and filtered data. Three supervised machine learning algorithms, including Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), and Gaussian Naïve Bayes (GNB), have been employed for data classification. A comparative study has been conducted to understand the fundamental differences in the EEG signals between stroke patients and healthy individuals. The findings indicated that the FBCSP approach surpassed CSP in both categories of patients, with the SVM achieving an accuracy of up to 98.86% in classifying motor imagery tasks. This comparative study enhances our understanding of Brain-Computer Interface (BCI) systems and motor rehabilitation methods by elucidating critical differences between EEG data from stroke patients and healthy individuals.

Keywords:

electroencephalogram, motor imagery, common spatial filter, filter bank common spatial filter, support vector machines, brain-computer interface

Downloads

Download data is not yet available.

References

Y. Miao et al., "BCI-Based Rehabilitation on the Stroke in Sequela Stage," Neural Plasticity, vol. 2020, no. 1, Dec. 2020, Art. no. 8882764.

R. Mane, T. Chouhan, and C. Guan, "BCI for stroke rehabilitation: motor and beyond," Journal of Neural Engineering, vol. 17, no. 4, Aug. 2020, Art. no. 041001.

M. A. Cervera et al., "Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis," Annals of Clinical and Translational Neurology, vol. 5, no. 5, pp. 651–663, May 2018.

V. L. Feigin et al., "Global and regional burden of stroke during 1990–2010: findings from the Global Burden of Disease Study 2010," The Lancet, vol. 383, no. 9913, pp. 245–255, Jan. 2014.

F.-L. Zhang et al., "Prevalence of stroke and associated risk factors: a population based cross sectional study from northeast China," BMJ Open, vol. 7, no. 9, Sep. 2017, Art. no. e015758.

X. Yi et al., "Prevalence of stroke and stroke related risk factors: a population based cross sectional survey in southwestern China," BMC Neurology, vol. 20, no. 1, Jan. 2020, Art. no. 5.

V. L. Roger et al., "Heart Disease and Stroke Statistics—2012 Update," Circulation, vol. 125, no. 1, pp. e2–e220, Jan. 2012.

Y. Béjot, H. Bailly, J. Durier, and M. Giroud, "Epidemiology of stroke in Europe and trends for the 21st century," La Presse Médicale, vol. 45, no. 12, Part 2, pp. e391–e398, Dec. 2016.

Y. Shang, X. Gao, and A. An, "Multi-band spatial feature extraction and classification for motor imaging EEG signals based on OSFBCSP-GAO-SVM model," Medical & Biological Engineering & Computing, vol. 61, no. 6, pp. 1581–1602, Jun. 2023.

A. Moran and H. O’Shea, "Motor Imagery Practice and Cognitive Processes," Frontiers in Psychology, vol. 11, Mar. 2020, Art. no. 394.

Z. Keser et al., "Electroencephalogram (EEG) With or Without Transcranial Magnetic Stimulation (TMS) as Biomarkers for Post-stroke Recovery: A Narrative Review," Frontiers in Neurology, vol. 13, Feb. 2022, Art. no. 827866.

N. Jaafar, A. Z. Che Daud, N. F. Ahmad Roslan, and W. Mansor, "Mirror Therapy Rehabilitation in Stroke: A Scoping Review of Upper Limb Recovery and Brain Activities," Rehabilitation Research and Practice, vol. 2021, no. 1, Dec. 2021, Art. no. 9487319.

L. Ding et al., "Camera-Based Mirror Visual Input for Priming Promotes Motor Recovery, Daily Function, and Brain Network Segregation in Subacute Stroke Patients," Neurorehabilitation and Neural Repair, vol. 33, no. 4, pp. 307–318, Apr. 2019.

S. Tsuchimoto, K. Shindo, F. Hotta, T. Hanakawa, M. Liu, and J. Ushiba, "Sensorimotor Connectivity after Motor Exercise with Neurofeedback in Post-Stroke Patients with Hemiplegia," Neuroscience, vol. 416, pp. 109–125, Sep. 2019.

M. Saes, C. G. M. Meskers, A. Daffertshofer, E. E. H. van Wegen, and G. Kwakkel, "Are early measured resting-state EEG parameters predictive for upper limb motor impairment six months poststroke?," Clinical Neurophysiology, vol. 132, no. 1, pp. 56–62, Jan. 2021.

N. A. Bhagat et al., "Neural activity modulations and motor recovery following brain-exoskeleton interface mediated stroke rehabilitation," NeuroImage: Clinical, vol. 28, Jan. 2020, Art. no. 102502.

R.-R. Lu et al., "Motor imagery based brain-computer interface control of continuous passive motion for wrist extension recovery in chronic stroke patients," Neuroscience Letters, vol. 718, Jan. 2020, Art. no. 134727.

C. Fan, B. Yang, X. Li, S. Gao, and P. Zan, "EEG-Based Feature Classification Combining 3D-Convolutional Neural Networks with Generative Adversarial Networks for Motor Imagery," Journal of Integrative Neuroscience, vol. 23, no. 8, Aug. 2024, Art. no. 153.

M. K. Titkanlou, D. T. Pham, and R. Mouček, "Classification of EEG Signal Using Deep Learning Architectures Based Motor-Imagery for an Upper-Limb Rehabilitation Exoskeleton," SN Computer Science, vol. 6, no. 3, Feb. 2025, Art. no. 193.

V. S. Kardam, S. Taran, and A. Pandey, "Motor Imagery Tasks Based Electroencephalogram Signals Classification Using Data-Driven Features," Neuroscience Informatics, vol. 3, no. 2, Jun. 2023, Art. no. 100128.

K. V. V. Reddy, I. Elamvazuthi, A. A. Aziz, S. Paramasivam, H. N. Chua, and S. Pranavanand, "An Efficient Prediction System for Coronary Heart Disease Risk Using Selected Principal Components and Hyperparameter Optimization," Applied Sciences, vol. 13, no. 1, Jan. 2023, Art. no. 118.

P. Sajda, A. Gerson, K.-R. Muller, B. Blankertz, and L. Parra, "A data analysis competition to evaluate machine learning algorithms for use in brain-computer interfaces," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 11, no. 2, pp. 184–185, Jun. 2003.

S. R. Sreeja, J. Rabha, K. Y. Nagarjuna, D. Samanta, P. Mitra, and M. Sarma, "Motor Imagery EEG Signal Processing and Classification Using Machine Learning Approach," in 2017 International Conference on New Trends in Computing Sciences, Amman, Jordan, 2017, pp. 61–66.

R. R. Krishna and N. Jyothi, "Road Surface Condition Identification with Deep Neural Networks and SVM Classifier," Engineering, Technology & Applied Science Research, vol. 15, no. 2, pp. 21998–22003, Apr. 2025.

D. K. Choubey, M. Kumar, V. Shukla, S. Tripathi, and V. K. Dhandhania, "Comparative Analysis of Classification Methods with PCA and LDA for Diabetes," Current Diabetes Reviews, vol. 16, no. 8, pp. 833–850, 2020.

C. Sharpe, T. Wiest, P. Wang, and C. C. Seepersad, "A Comparative Evaluation of Supervised Machine Learning Classification Techniques for Engineering Design Applications," Journal of Mechanical Design, vol. 141, no. 12, Oct. 2019, Art. no. 121404.

Downloads

How to Cite

[1]
Karna, V.V.R., Karna, V.R., Tummala, A.B., G, M.R. and Mannepally, V. 2025. Feature-Based Classification of Motor Imagery Tasks using Electroencephalogram Recordings. Engineering, Technology & Applied Science Research. 15, 4 (Aug. 2025), 25641–25646. DOI:https://doi.org/10.48084/etasr.11420.

Metrics

Abstract Views: 21
PDF Downloads: 5

Metrics Information

License

Copyright (c) 2025 Vishnu Vardhana Reddy Karna, Viswavardhan Reddy Karna, Aravinda Babu Tummala, Mallikharjuna Rao G, Venkateswarlu Mannepally

Creative Commons License

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.