Abstract
This manuscript reports feature domains for the recognition of right and left hand movements using Electroencephalogram (EEG). A 21-channel EEG dataset of seven subjects during right and left hand fist open and close movements was collected from PhysioNet of the BCI2000 Instrumentation system. Features in time, frequency, and time-frequency domains have been explored. Support vector machine with radial basis function kernel was used for the recognition of right and left hand fist open and close movements. The recognition with time, frequency, and time-frequency domain features resulted in an accuracy of 90%, 92%, 97.5%, respectively. Time-frequency domain features obtained through discrete wavelet transform (DWT) at four decomposition levels have resulted in maximum recognition rate. The highest recognition rate of 98.6 \( \pm \) 0.6 % has resulted in DWT features at level 2. This was substantiated by the fact that DWT features at level 2 establish maximum correlation with pre-processed EEG. Experimental result shows that time-frequency is the best performing feature domain among the three. Further, correlation measure of time-frequency domain features with EEG are established as a benchmark for selecting DWT decomposition level.
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References
Phukan, N., Kakoty, N.M., Shivam, P., Gan, J.Q.: Finger Movements Recognition using Minimally Redundant Features of Wavelet Denoised EMG. Health and Technology 9(4), 579–593 (2019)
F. Sepulveda. Advances in Robot Navigation, chapter Brain-actuated Control of Robot Navigation. InTech, Europe, 2011
A-K. Mohamed. Towards Improved EEG Interpretation in a Sensorimotor BCI for the Control of a Prosthetic or Orthotic Hand. Technical report, University of Witwatersrand, Johannesburg, 2011
Machado, J., Balbinot, A.: Executed Movement using EEG Signals through a Naive Bayes Classifier. Micromachine 5(4), 1082–1105 (2014)
P. P. M. Shanir, Y. U. Khan, and E. Khan. Classification of EEG Signal for Left and Right Wrist Movements using AR Modelling. In Proceedings of Conference on Modern Trends in Electronics and Communication Systems, pages 65–69, Aligarh, 2008
Y. Wang, B. Hong, X. Gao, , and S. Gao. Implementation of a Brain- Computer Interface Based on Three States of Motor Imagery. In IEEE International Conference on Engineering in Medicine and Biology Society, pages 5059–5062, France, 2007
C. Guger, W. Harkam, C. Hertnaes, and G. Pfurtscheller. Prosthetic Control by an EEG-based Brain-Computer Interface (BCI). In 5th European Conference for the Advancement of Assistive Technology, pages 1–6, Germany, 1999
M. H. Alomari, A. Samaha, and K. Al. Kamha. Automated Classification of L/R Hand Movement EEG Signals using Advanced Feature Extraction and Machine Learning. Advanced Computer Science and Applications, 4(6):207–212, 2013
Syed UmarAmin, Mansour Alsulaiman, Ghulam Muhammadand Mohamed AmineMekhtiche, and M.Shamim Hossain. Deep Learning for EEG motor imagery classification based on multi-layer CNNs feature fusion. Future Generation Computer Systems, 101, 2019
Antonio Maria Chiarelli, Pierpaolo Croce, Arcangelo Merla1, and Filippo Zappasodi. Deep learning for hybrid EEG-fNIRS brain-computer interface: application to motor imagery classification. Journal of Neural Engineering, 15(3):1–24, 2018
Kakoty, N.M., Hazarika, S.M., Gan, J.Q.: EMG Feature Set Selection Through Linear Relationship for Grasp Recognition. Journal on Medical and Biological Engineering 36(6), 883–890 (2016)
S. W. Hilt and Sam Merat. SVM Clustering. BMC Bioinformatics, 8(7), 2007
Schalk, G., McFarland, D.J., Hinterberge, T., Birbaumer, N., Wolpaw, J.R.: BCI2000: A General-Purpose Brain-Computer Interface (BCI) System. IEEE Transactions on Biomedical Engineering 51(6), 1034–1043 (2004)
P. Geethanjali, Y.K. Mohan, and J. Sen. Time domain Feature extraction and classification of EEG data for Brain Computer Interface . In 9th International Conference on Fuzzy Systems and Knowledge Discovery, pages ii36–1139, Sichuan, China, 2012. IEEE
Lemarie, P.G., Meyer, Y.: Ondelettes et Bases Hilbertiennes. Revista Matematica Iberoamericana 2(1), 1–19 (1986)
P. Jahankhani, V. Kodogiannis, and K. Revett. EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks. In Proceedings IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing, 2006
N. M Kakoty and S. M. Hazarika. Recognition of Grasp Types Through Principal Components of DWT based EMG Features. In IEEE International Conference on Rehabilitation Robotics, pages 1–6, Switzerland, 2011
Acknowledgements
Support under project No. 1-5728870614-NPIU (TEQIP III) Collaborative Research Scheme CRS, project No. CRD/2018/000049-ASEAN-India R&D Scheme, SERB-DST, and project No. NECBH/2019-20/144 NECBH-DBT, Government of India, is acknowledged.
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Phukan, N., M. Kakoty, N., Gupta, N., Baruah, N. (2021). EEG-Based Hand Movement Recognition: Feature Domain and Level of Decomposition. In: Rao, Y.V.D., Amarnath, C., Regalla, S.P., Javed, A., Singh, K.K. (eds) Advances in Industrial Machines and Mechanisms. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-1769-0_28
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