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A FUSION OF A DISCRETE WAVELET TRANSFORM-BASED AND TIME-DOMAIN FEATURE EXTRACTIONFOR MOTOR IMAGERY CLASSIFICATION


(Received: 20-Nov.-2023, Revised: 1-Feb.-2024 , Accepted: 16-Feb.-2024)
A motor imagery (MI)-based brain-computer interface (BCI) has performed successfully as a control mechanism with multiple electroencephalogram (EEG) channels. For practicality, fewer EEG channels are preferable. This paper investigates a single-channel EEG signal for MI. However, there are insufficient features that can be extracted due to a single-channel EEG signal being used in one region of the brain. An effective feature extraction technique plays a critical role in overcoming this limitation. Therefore, this study proposes a fusion of discrete wavelet transform (DWT)-based and time-domain feature extraction to provide more relevant information for classification. The highest accuracy obtained on the BCI Competition III (IVa) dataset is 87.5% with logistic regression (LR) while the OpenBMI dataset attained the highest accuracy of 93% with support vector machine (SVM) as the classifier. Addressing the potential of enhancing the performance of a single EEG channel located on the forehead, the achieved result is relatively promising.

[1] J. L. Collinger et al., "Functional Priorities, Assistive Technology and Brain-Computer Interfaces After Spinal Cord Injury," J. of Rehabilitation Research and Development, vol. 50, no. 2, pp. 145–160, 2013.

[2] E. K. St. Louis, L. C. Frey and J. W. Britton, Electroencephalography (EEG): An Introductory Text and Atlas of Normal and Abnormal Findings in Adults, Children and Infants, Chicago: American Epilepsy Society; PMID: 27748095, 2016.

[3] L. Kauhanen et al., "EEG and MEG Brain-Computer Interface for Tetraplegic Patients," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 14, no. 2, pp. 190–193, Jun. 2006.

[4] N. Kulkarni and V. Bairagi, "Electroencephalogram and Its Use in Clinical Neuroscience," in Book: EEG-based Diagnosis of Alzheimer Disease, pp. 25–35, DOI: 10.1016/B978-0-12-815392-5.00002-2, 2018.

[5] J. Liao, J. Wang, C. A. Zhan and F. Yang, "Parameterized Aperiodic and Periodic Components of Single-channel EEG Enables Reliable Seizure Detection," Physical and Engineering Sciences in Medicine, DOI: 10.1007/s13246-023-01340-6, Sep. 2023.

[6] G. Kaushik, P. Gaur, R. R. Sharma and R. B. Pachori, "EEG Signal Based Seizure Detection Focused on Hjorth Parameters from Tunable-Q Wavelet Sub-bands," Biomed. Signal Process. Control, vol. 76, p. 103645, DOI: 10.1016/j.bspc.2022.103645, Jul. 2022.

[7] A. Babiker and I. Faye, "A Hybrid EMD-Wavelet EEG Feature Extraction Method for the Classification of Students’ Interest in the Mathematics Classroom," Applied Computational Intelligence and Soft Computing, vol. 2021, pp. 1–8, DOI: 10.1155/2021/6617462, Jan. 2021.

[8] M. Zhong, Q. Yang, Y. Liu, B. Zhen, F. Zhao and B. Xie, "EEG Emotion Recognition Based on TQWT-features and Hybrid Convolutional Recurrent Neural Network," Biomed. Signal Process. Control, vol. 79, p. 104211, DOI: 10.1016/j.bspc.2022.104211, Jan. 2023.

[9] M. F. Mridha et al., "Brain-Computer Interface: Advancement and Challenges," Sensors, vol. 21, no. 17, p.5746, DOI: 10.3390/s21175746, Aug. 2021.

[10] X. Gu et al., "EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 18, no. 5, pp. 1645–1666, 2021.

[11] M. Rashid et al., "Current Status, Challenges and Possible Solutions of EEG-based Brain-computer Interface: A Comprehensive Review," Frontiers in Neurorobotics, vol. 14. Frontiers Media S.A., DOI: 10.3389/fnbot.2020.00025, Jun. 03, 2020.

[12] Jusas and Samuvel, "Classification of Motor Imagery Using a Combination of User-specific Band and Subject-specific Band for Brain-Computer Interface," Applied Sciences, vol. 9, no. 23, p. 4990, 2019.

[13] C. Neuper, M. Wörtz and G. Pfurtscheller, "ERD/ERS Patterns Reflecting Sensorimotor Activation and Deactivation," Progress in Brain Research, pp. 211–222, DOI: 10.1016/S0079-6123(06)59014-4, 2006.

[14] S. Saha et al., "Progress in Brain Computer Interface: Challenges and Opportunities," Frontiers in Systems Neuroscience, vol. 15, DOI: 10.3389/fnsys.2021.578875, Feb. 25, 2021.

[15] L. Brusini, F. Stival, F. Setti, E. Menegatti, G. Menegaz and S. F. Storti, "A Systematic Review on Motor-imagery Brain Connectivity-based Computer Interfaces," IEEE Transactions on Human-Machine Systems, vol. 51, no. 6, pp. 725–733, DOI: 10.1109/THMS.2021.3115094, Dec. 2021.

[16] P. Gaur, K. McCreadie, R. B. Pachori, H. Wang and G. Prasad, "An Automatic Subject Specific Channel Selection Method for Enhancing Motor Imagery Classification in EEG-BCI Using Correlation," Biomed Signal Process Control, vol. 68, p. 102574, DOI: 10.1016/j.bspc.2021.102574, Jul. 2021.

[17] L. Zhang and Q. Wei, "Channel Selection in Motor Imaginary-based Brain-Computer Interfaces: A Particle Swarm Optimization Algorithm," J. of Integrative Neuroscience, vol. 18, no. 2, pp. 141–152, DOI: 10.31083/j.jin.2019.02.17, 2019.

[18] S. Ge, R. Wang and D. Yu, "Classification of Four-class Motor Imagery Employing Single-channel Electroencephalography," PLoS One, vol. 9, no. 6, p. e98019, Jun. 2014.

[19] S. K. Khare and V. Bajaj, "A Facile and Flexible Motor Imagery Classification Using Electroencephalogram Signals," Computer Methods and Programs in Biomedicine, vol. 197, p. 105722, DOI: 10.1016/j.cmpb.2020.105722, Dec. 2020.

[20] S. K. Khare, N. Gaikwad and N. D. Bokde, "An Intelligent Motor Imagery Detection System Using Electroencephalography with Adaptive Wavelets," Sensors, vol. 22, no. 21, p. 8128, Oct. 2022.

[21] 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, p. 119921, DOI: 10.1016/j.eswa.2023.119921, Aug. 2023.

[22] K. Kotegawa, A. Yasumura and W. Teramoto, "Activity in the Prefrontal Cortex during Motor Imagery of Precision Gait: An fNIRS Study," Experimental Brain Research, vol. 238, no. 1, pp. 221–228, 2020.

[23] L. Almulla, I. Al-Naib, I. S. Ateeq and M. Althobaiti, "Observation and Motor Imagery Balance Tasks Evaluation: An fNIRS Feasibility Study," PLoS One, vol. 17, no. 3, p. e0265898, Mar. 2022.

[24] S. Glover, E. Bibby and E. Tuomi, "Executive Functions in Motor Imagery: Support for the Motor-cognitive Model over the Functional Equivalence Model," Experimental Brain Research, vol. 238, no. 4, pp. 931–944, DOI: 10.1007/s00221-020-05756-4, Apr. 2020.

[25] J. A. Wilson, G. Schalk, L. M. Walton and J. C. Williams, "Using an EEG-based Brain-Computer Interface for Virtual Cursor Movement with BCI2000," Journal of Visualized Experiments, no. 29, DOI: 10.3791/1319, Jul. 2009.

[26] S. S. Moumgiakmas and G. A. Papakostas, "Robustly Effective Approaches on Motor Imagery-based Brain Computer Interfaces," Computers, vol. 11, no. 5, p. 61, DOI: 10.3390/computers11050061, 2022.

[27] S. Selim, M. Tantawi, H. Shedeed and A. Badr, "A Comparative Analysis of Different Feature Extraction Techniques for Motor Imagery Based BCI System," Proc. of the Int. Conf. on Artificial Intelligence and Computer Vision (AICV2020), pp. 740–749, DOI: 10.1007/978-3-030-44289-7_69, 2020.

[28] J. Camacho and V. Manian, "Real-time Single Channel EEG Motor Imagery Based Brain Computer Interface," Proc. of the IEEE 2016 World Automation Congress (WAC), pp. 1–6, DOI: 10.1109/WAC.2016.7582973, Rio Grande, PR, USA, Jul. 2016.

[29] L.-W. Ko, S. S. K. Ranga, O. Komarov and C.-C. Chen, "Development of Single-channel Hybrid BCI System Using Motor Imagery and SSVEP," J. of Healthcare Eng., vol. 2017, pp. 1–7, DOI: 10.1155/2017/3789386, 2017.

[30] R. Chen et al., "Enhancement of Time-frequency Energy for the Classification of Motor Imagery Electroencephalogram Based on an Improved FitzHugh–Nagumo Neuron System," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 31, pp. 282–293, 2023.

[31] G. Rodríguez-Bermúdez and P. J. García-Laencina, "Automatic and Adaptive Classification of Electroencephalographic Signals for Brain Computer Interfaces," J. of Medical Systems, vol. 36, no. S1, pp. 51–63, DOI: 10.1007/s10916-012-9893-4, Nov. 2012.

[32] R. R. Sharma and R. B. Pachori, "A New Method for Non-stationary Signal Analysis Using Eigenvalue Decomposition of the Hankel Matrix and Hilbert Transform," Proc. of the 2017 4th IEEE Int. Conf. on Signal Processing and Integrated Networks (SPIN), pp. 484–488, DOI: 10.1109/SPIN.2017.8049998, Feb. 2017.

[33] J. Kevric and A. Subasi, "Comparison of Signal Decomposition Methods in Classification of EEG Signals for Motor-imagery BCI System," Biomed. Signal Process. Control, vol. 31, pp. 398–406, DOI: 10.1016/j.bspc.2016.09.007, Jan. 2017.

[34] O. Attallah, J. Abougharbia, M. Tamazin and A. A. Nasser, "A BCI System Based on Motor Imagery for Assisting People with Motor Deficiencies in the Limbs," Brain Sciences, vol. 10, no. 11, p. 864, DOI: 10.3390/brainsci10110864, Nov. 2020.

[35] Ji, Ma, Dong and Zhang, "EEG Signals Feature Extraction Based on DWT and EMD Combined with Approximate Entropy," Brain Sciences, vol. 9, no. 8, p. 201, DOI: 10.3390/brainsci9080201, Aug. 2019.

[36] A. al-Qerem, F. Kharbat, S. Nashwan, S. Ashraf and K. Blaou, "General Model for Best Feature Extraction of EEG Using Discrete Wavelet Transform Wavelet Family and Differential Evolution," Int. J. of Distributed Sensor Networks, vol. 16, no. 3, p. 155014772091100, Mar. 2020.

[37] G. C. Jana, A. Agrawal, P. K. Pattnaik and M. Sain, "DWT-EMD Feature Level Fusion Based Approach over Multi and Single Channel EEG Signals for Seizure Detection," Diagnostics, vol. 12, no. 2, p. 324, DOI: 10.3390/diagnostics12020324, Jan. 2022.

[38] B. Blankertz et al., "The BCI Competition III: Validating Alternative Approaches to Actual BCI Problems," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 14, no. 2, pp. 153–159, DOI: 10.1109/TNSRE.2006.875642, Jun. 2006.

[39] M.-H. Lee et al., "EEG Dataset and OpenBMI Toolbox for Three BCI Paradigms: An Investigation into BCI Illiteracy," Gigascience, vol. 8, no. 5, DOI: 10.1093/gigascience/giz002, May 2019.

[40] S. Kanoga, A. Kanemura and H. Asoh, "A Comparative Study of Features and Classifiers in Single-channel EEG-based Motor Imagery BCI," Proc. of the 2018 IEEE Global Conf. on Signal and Information Processing (GlobalSIP), pp. 474–478, DOI: 10.1109/GlobalSIP.2018.8646636, Nov. 2018.

[41] M. Al-Quraishi, I. Elamvazuthi, S. Daud, S. Parasuraman and A. Borboni, "EEG-based Control for Upper and Lower Limb Exoskeletons and Prostheses: A Systematic Review," Sensors, vol. 18, no. 10, p. 3342, DOI: 10.3390/s18103342, Oct. 2018.

[42] H. U. Amin et al., "Feature Extraction and Classification for EEG Signals Using Wavelet Transform and Machine Learning Techniques," Australasian Physical and Engineering Sciences in Medicine, vol. 38, no. 1, pp. 139–149, DOI: 10.1007/s13246-015-0333-x, Mar. 2015.

[43] A. A. Abdul-latif, I. Cosic, D. K. Kumar, B. Polus and C. da_Costa, "Power Changes of EEG Signals Associated with Muscle Fatigue: The Root Mean Square Analysis of EEG Bands," Proc. of the 2004 Intelligent Sensors, Sensor Networks and Information Processing Conf., pp. 531–534, DOI: 10.1109/ISSNIP.2004.1417517, 2004.

[44] B. Hjorth, "EEG Analysis Based on Time Domain Properties," Electroencephalography and Clinical Neurophysiology, vol. 29, no. 3, pp. 306–310, DOI: 10.1016/0013-4694(70)90143-4, Sep. 1970.

[45] M. S. Safi and S. M. M. Safi, "Early Detection of Alzheimer’s Disease from EEG Signals Using Hjorth Parameters," Biomed. Signal Process. Control, vol. 65, p. 102338, DOI: 10.1016/j.bspc.2020.102338, 2021.

[46] F. Lotte, "A New Feature and Associated Optimal Spatial Filter for EEG Signal Classification: Waveform Length," Proc. of the 21st Int. Conf. on Pattern Recognition (ICPR2012), pp. 1302–1305, Tsukuba, Japan, 2012.

[47] D. Garrett, D. A. Peterson, C. W. Anderson and M. H. Thaut, "Comparison of Linear, Nonlinear and Feature Selection Methods for EEG Signal Classification," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 11, no. 2, pp. 141–144, DOI: 10.1109/TNSRE.2003.814441, Jun. 2003.

[48] P. Nadkarni, "Core Technologies: Machine Learning and Natural Language Processing," Clinical Research Computing, pp. 85–114, DOI: 10.1016/B978-0-12-803130-8.00004-X, Elsevier, 2016.

[49] S. Dreiseitl and L. Ohno-Machado, "Logistic Regression and Artificial Neural Network Classification Models: AMethodology Review," J. of Biomedical Informatics, vol. 35, no. 5–6, pp. 352–359, Oct. 2002.

[50] Y. Huang and L. Li, "Naive Bayes Classification Algorithm Based on Small Sample Set," Proc. of the 2011 IEEE Int. Conf. on Cloud Computing and Intelligence Systems, pp. 34–39, DOI: 10.1109/CCIS.2011.6045027, Sep. 2011.

[51] Pawan and R. Dhiman, "Motor Imagery Signal Classification Using Wavelet Packet Decomposition and Modified Binary Grey Wolf Optimization," Measurement: Sensors, vol. 24, p. 100553, DOI: 10.1016/j.measen.2022.100553, Dec. 2022.

[52] M. Ahn, H. Cho, S. Ahn and S. C. Jun, "High Theta and Low Alpha Powers May Be Indicative of BCI-illiteracy in Motor Imagery," PLoS One, vol. 8, no. 11, p. e80886, Nov. 2013.

[53] G. Roy, A. K. Bhoi and S. Bhaumik, "A Comparative Approach for MI-Based EEG Signals Classification Using Energy, Power and Entropy," IRBM, vol. 43, no. 5, pp. 434–446, Oct. 2022.