Citation :

Manoj Kumar Mukul, Ayush Chandra, Prajna Parimita Dash, Aminul Islam, "Development of a Neurofeedback System for Movement Imagery-Based BCI," International Journal of Electronics and Communication Engineering, vol. 13, no. 4, pp. 52-65, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I4P104

Abstract

In recent decades, Brain Research–Computer Interfaces (BCIs) based on Electroencephalograms (EEGs) have become a crucial area of study, particularly for enabling real-time control of electric wheelchairs for individuals with disabilities. A most commonly used approach for this purpose is Movement Imagery (MI). Researchers have proposed various techniques to improve classification accuracy, focusing on effective preprocessing and feature extraction methods for real-time classification of movement imagery. This paper investigates the effectiveness of Empirical Mode Decomposition (EMD) as a preprocessing technique to decompose raw EEG signals into Intrinsic Mode Functions (IMFs) and evaluates suitable power spectrum estimation methods. Different rhythmic bands of the raw EEG signals are selected for EMD decomposition. The resulting IMFs are then used to estimate power spectral density using parametric (Burg method) and non-parametric (Welch method) approaches. The analyzed feature is the average power within the rhythmic bands of the selected IMFs. The outcomes of this study have multiple observations. The reported results indicate that the Welch method outperforms the Burg method, achieving overall classification accuracy that is more than 1% higher. Additionally, the proposed methods achieve good classification accuracy on standard movement imagery datasets but fail to match the performance of BCI-illiterate subjects. Based on this analysis, the authors conclude that signal processing and feature extraction methods alone are insufficient to achieve high classification accuracy, emphasizing that users of BCI technology require proper training.

Keywords

BCI, EEG, EMG, MI, PSD.

References

1. Zamzuri Idris et al., “Quantum and Electromagnetic Fields in Our Universe and Brain: A New Perspective to Comprehend Brain Function,” Brain Science, vol.11, no. 5, pp. 1-15, 2021.
   [CrossRef] [Google Scholar] [Publisher Link]
2. Jennifer L. Collinger et al, “Special Issue on Brain–Computer Interfaces: Highlighting Research from the 10^th International Brain–Computer Interface Meeting,” Journal of Neural Engineering, vol. 22, no. 3, pp. 1-4, 2025.
   [CrossRef] [Google Scholar] [Publisher Link]
3. Jonathan R Wolpaw et al., “Brain–Computer Interfaces for Communication and Control,” Clinical Neurophysiology, vol. 113, no. 6, pp.   767-791, 2002.
   [CrossRef] [Publisher Link]
4. G. Pfurtscheller et al., “EEG-based Discrimination between Imagination of Right and Left Hand Movement,” Electroencephalography and Clinical Neurophysiology, vol. 103, no. 6, pp. 642-651, 1997.
   [CrossRef] [Google Scholar] [Publisher Link]
5. H. Ramoser, J. Muller-Gerking, and G. Pfurtscheller, “Optimal Spatial Filtering of Single Trial EEG During Imagined Hand Movement,” IEEE Transactions on Rehabilitation Engineering, vol. 8, no. 4, pp. 441-446, 2000.
   [CrossRef] [Google Scholar] [Publisher Link]
6. Frederico Caiado, and Arkadiy Ukolov, “The History, Current State and Future Possibilities of the Non-Invasive Brain Computer Interfaces,” Medicine in Novel Technology and Devices, vol. 25, pp. 1-9, 2025.
   [CrossRef] [Google Scholar] [Publisher Link]
7. Mamunur Rashid et al., “Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review,” Frontiers in Neurorobotics, vol. 14, pp. 1-35, 2020.
   [CrossRef] [Google Scholar] [Publisher Link]
8. David E Thompson et al., “Performance Measurement for Brain-Computer or Brain-Machine Interfaces: A Tutorial,” Jouranl of Neural Engineerings, vol. 11, no. 3, pp. 1-28, 2014.
   [CrossRef] [Google Scholar] [Publisher Link]
9. K. Tanaka, K. Matsunaga, and H.O. Wang, “Electroencephalogram-based Control of an Electric Wheelchair,” IEEE Transactions on Robotics, vol. 21, no. 4, pp. 762-766, 2005.
   [CrossRef] [Google Scholar] [Publisher Link]
10. Le Wu et al., “Signal Processing for Brain–Computer Interfaces: A Review and Current Perspectives,” IEEE Signal Processing Magazine, vol. 40, no. 5, pp. 80-91, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
11. D. Jaipriya, and K. C. Sriharipriya, “Brain Computer Interface-Based Signal Processing Techniques for Feature Extraction and Classification of Motor Imagery Using EEG: A Literature Review,” Biomedical Materials & Devices, vol. 2, pp. 601-613, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
12. Ali Bashashati et al., “A Survey of Signal Processing Algorithms in Brain-Computer Interfaces based on Electrical Brain Signals,”  Journal of Neural Engineering, vol. 4, no. 2, pp. 32-57, 2007.
    [CrossRef] [Google Scholar] [Publisher Link]
13. Suolin Duan et al., “The Feature Extraction of ERD/ERS Signals based on the Wavelet Package and ICA,” Proceeding of the 11^th World Congress on Intelligent Control and Automation, Shenyang, pp. 5621-5625, 2014.
    [CrossRef] [Google Scholar] [Publisher Link]
14. Izabela Rejer, and Łukasz Cieszyński, “Independent Component Analysis for a Low-Channel SSVEP-BCI,” Pattern Analysis and Applications, vol. 22, pp. 47-62, 2019.
    [CrossRef] [Google Scholar] [Publisher Link]
15. Darren K. Emge et al., “Independent Vector Analysis for SSVEP Signal Enhancement, Detection, and Topographical Mapping,” Brain Topography, vol. 31, pp. 117-124, 2018.
    [CrossRef] [Google Scholar] [Publisher Link]
16. Haleema Al-Turabi, and Hessa Al-Junaid, “Brain Computer Interface for Wheelchair Control in Smart Environment,” ​Smart Cities Symposium 2018, Bahrain, pp. 1-6, 2018.
    [CrossRef] [Google Scholar] [Publisher Link]
17. Kyuwan Choi, “Control of a Vehicle with EEG Signals in Real-time and System Evaluation,” European Journal of Applied Physiology, vol. 112, pp. 755-766, 2012.
    [CrossRef] [Google Scholar] [Publisher Link]
18. Tom Carlson, and Jose del R. Millan, “Brain-Controlled Wheelchairs: A Robotic Architecture,” IEEE Robotics & Automation Magazine, vol. 20, no. 1, pp. 65-73, 2013.
    [CrossRef] [Google Scholar] [Publisher Link]
19. Vikram Singh Kardam, Sachin Taran, and Anukul Pandey, “Motor Imagery Tasks Based Electroencephalogram Signals Classification Using Data-Driven Features,” Neuroscience Informatics, vol. 3, no. 2, pp. 1-13, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
20. Wei Xiong, and Qingguo Wei, “Reducing Calibration Time in Motor Imagery-based BCIs by Data Alignment and Empirical Mode Decomposition,” PloS One, vol. 17, no. 2, pp. 1-18, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
21. Xianlun Tang et al., “Motor Imagery EEG Recognition based on Conditional Optimization Empirical mode Decomposition and Multi-Scale Convolutional Neural Network,” Expert Systems with Applications, vol. 149, 2020.
    [CrossRef] [Google Scholar] [Publisher Link]
22. Syed Khairul Bashar, and Mohammed Imamul Hassan Bhuiyan, “Classification of Motor Imagery Movements using Multivariate Empirical Mode Decomposition and Short Time Fourier Transform based Hybrid Method,” Engineering Science and Technology, an International Journal, vol. 19, no. 3, pp. 1457-1464, 2016. [CrossRef] [Google Scholar] [Publisher Link]
23. G. Pfurtscheller et al., “Mu Rhythm (de)Synchronization and EEG Single-trial Classification of Different Motor Imagery Tasks,” Neuroimage, vol. 31, no. 1, pp.153-159, 2006.
    [CrossRef] [Google Scholar] [Publisher Link]
24. Sébastien Rimbert, David Trocellier, and Fabien Lotte, “Is Event-Related Desynchronization Variability Correlated with BCI Performance?,” 2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), Rome, Italy, pp. 163-168, 2022. [CrossRef] [Google Scholar] [Publisher Link]
25. Kosei Nakayashiki et al., “Modulation of Event-Related Desynchronization during Kinematic and Kinetic hand Movements,” Journal of NeuroEngineering and Rehabilitation, vol. 11, pp. 1-9, 2014.
    [CrossRef] [Google Scholar] [Publisher Link]
26. Christa Neuper et al., “Motor Imagery and Action Observation: Modulation of Sensorimotor Brain Rhythms during Mental Control of a  Brain–Computer Interface,” Clinical Neurophysiology, vol. 120, no. 2, pp. 239-247, 2009.
    [CrossRef] [Google Scholar] [Publisher Link]
27. Madiha Tariq, Pavel M. Trivailo, and Milan Simic, “Classification of Left and Right Foot Kinaesthetic Motor Imagery using Common Spatial Pattern,” Biomedical Physics & Engineering Express, vol. 6, no. 1, pp. 1-11, 2019.
    [CrossRef] [Google Scholar] [Publisher Link]
28. Jin Woo Choi, Sejoon Huh, and Sungho Jo, “Improving Performance in Motor imagery BCI-based Control Applications via Virtually Embodied Feedback,” Computers in Biology and Medicine, vol. 127, pp. 1-9, 2020.
    [CrossRef] [Google Scholar] [Publisher Link]
29. Esra Kaya, and Ismail Saritas, “Motor Imagery BCI Classification with Frequency and Time-Frequency Features by Using Different Dimensions of the Feature Space Using Autoencoders,” Intelligent Methods In Engineering Sciences, vol. 1, no. 1, pp. 8-12, 2022.  
    [CrossRef] [Google Scholar] [Publisher Link]
30. G. Dornhege et al., “Boosting Bit Rates in Noninvasive EEG Single-trial Classifications by Feature Combination and Multiclass Paradigms,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 6, pp. 993-1002, 2004.
    [CrossRef] [Google Scholar] [Publisher Link]
31. J. Esther, and T. UmmalSarbaBegum, “WITHDRAWN: Exploration of EEG based Classification using Imagined Motor Movements,” Materials Today: Proceedings, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
32. F Lotte et al., “A Review of Classification Algorithms for EEG-based Brain–Computer Interfaces: A 10 Year Update,” Journal of Neural Engineering, vol . 15, no. 3, pp. 1-28, 2018.
    [CrossRef] [Google Scholar] [Publisher Link]
33. Tianyou Yu et al., “Enhanced Motor Imagery Training Using a Hybrid BCI with Feedback,” IEEE Transactions on Biomedical Engineering, vol. 62, no. 7, pp. 1706-1717, 2015.
    [CrossRef] [Google Scholar] [Publisher Link]
34. Toshiyuki Kondo et al., “Effect of Instructive Visual Stimuli on Neurofeedback Training for Motor Imagery-based Brain–Computer Interface,” Human Movement Science, vol. 43, pp. 239-249, 2015.
    [CrossRef] [Google Scholar] [Publisher Link]
35. Benjamin Blankertz, BCI Competitions IV, 2008. [Online]. Available: https://www.bbci.de/competition/iv/
36. Tong Wang et al, “Comparing the Applications of EMD and EEMD on Time–Frequency Analysis of Seismic Signal,” Journal of Applied Geophysics, vol. 83, pp. 29-34, 2012.
    [CrossRef] [Google Scholar] [Publisher Link]
37. Hao-Teng Hsu et al., “Analyses of EEG Oscillatory Activities during Slow and Fast Repetitive Movements Using Holo-Hilbert Spectral Analysis,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 26, no. 9, pp. 1659-1668, 2018.
    [CrossRef] [Google Scholar] [Publisher Link]
38. Mohammad Nur Alam, Muhammad Ibn Ibrahimy, and S. M. A. Motakabber, “Feature Extraction of EEG Signal by Power Spectral Density for Motor Imagery Based BCI,” 2021 8^th International Conference on Computer and Communication Engineering (ICCCE), Kuala Lumpur, Malaysia, pp. 234-237, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
39. G. Dornhege et al., “Combined Optimization of Spatial and Temporal Filters for Improving Brain-Computer Interfacing,” IEEE Transactions on Biomedical Engineering, vol. 53, no. 11, pp. 2274-2281, 2006.
    [CrossRef] [Google Scholar] [Publisher Link]
40. Rui Zhang et al., “Control of a Wheelchair in an Indoor Environment Based on a Brain–Computer Interface and Automated Navigation,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 24, no. 1, pp. 128-139, 2016.
    [CrossRef] [Google Scholar] [Publisher Link]
41. Bin Xia et al., “A Neurofeedback Training Paradigm for Motor Imagery based Brain-Computer Interface,” The 2012 International Joint Conference on Neural Networks (IJCNN), Brisbane, QLD, Australia, pp. 1-4, 2012.
    [CrossRef] [Google Scholar] [Publisher Link]