Two Class Motor Imagery Classification via Wave Atom Sub-Bants
Authors: Nebi Gedik
The goal of motor image brain computer interface research is to create a link between the central nervous system and a computer or device. The most important signal for brain-computer interface is the electroencephalogram. The aim of this research is to explore a set of effective features from EEG signals, separated into frequency bands, using wave atom sub-bands to discriminate right and left-hand motor imagery signals. Over the transform coefficients, feature vectors are constructed for each frequency range and each transform sub-band, and their classification performances are tested. The method is validated using EEG signals from the BCI competition III dataset IIIa and classifiers such as support vector machine and k-nearest neighbors.
Keywords: motor imagery, EEG, Wave atom transform sub-bands, SVM, k-NNProcedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 346
 J. R Millan, et al. "Noninvasive brain-actuated control of a mobile robot by human EEG." IEEE Transactions on biomedical Engineering, vol. 51, no.6, pp.1026-1033, 2004.
 J. R. Wolpaw, et al. "Brain–computer interfaces for communication and control." Clinical neurophysiology, vol.113, no.6, pp. 767-791, 2002.
 A. Finke, A. Lenhardt, H. Ritter. "The MindGame: a P300-based brain–computer interface game." Neural Networks, vol.22, no.9, pp. 1329-1333, 2009.
 J. J. Daly, J. E. Huggins, “Brain-computer interface: current and emerging rehabilitation applications.” Arch Phys Med Rehabil, vol. 96, no. 3, pp. 1-7, 2015.
 M. Athif, H. Ren, “WaveCSP: a robust motor imagery classifier for consumer EEG devices.” Australas Phys Eng Sci Med., vol. 42, no. 1, pp. 159-168, 2019.
 M. Miao, A. Wang, F. Liu, “A spatial-frequency-temporal optimized feature sparse representationbased classification method for motor imagery EEG pattern recognition.” Med Biol Eng Comput, vol. 55, no. 9, pp. 1589-603, 2017.
 M. Z. Al-Faiz, A. A. Al-hamadani. “Implementation of EEG signal processing and decoding for twoclass motor imagery data.” Biomed Eng Appl Basis Commun, vol. 31, no. 4, p.1950028, 2019.
 J. Wang, G. Yu, L. Zhong, W. Chen, Y. Sun, “Classification of EEG signal using convolutional neural networks”, in 14th IEEE Conference on Industrial Electronics and Applications, pp. 1694-1698, 2019.
 M. Li, W. Zhu, H. Liu, and J. Yang, “Adaptive feature extraction of motor imagery EEG with optimal wavelet packets and SE-isomap,” Applied Sciences, vol. 7, no. 390, pp. 1-18, 2017.
 L. Cheng, D. Li, G. Yu, Z. Zhang, X. Li, and S. Yu, “A motor imagery EEG feature extraction method based on energy principal component analysis and deep belief networks,” IEEE Access, vol. 8, pp. 21453–21472, 2020.
 BCI competition III, http://www.bbci.de/competition/iii/ (last accessed 20.11.2021).
 BCI competition III dataset IIIa, http://www.bbci.de/competition/iii/desc_IIIa.pdf (last accessed 20.11.2021)
 L. Demanet, and L. Ying, “Wave atoms and sparsity of oscillatory patterns,” Applied and Computational Harmonic Analysis, vol. 23, no. 3, pp. 368-387, 2007.
 J. Jin, Y. Miao, I. Daly, C.D. Hu, A. Cichocki, “Correlation-based channel selection and regularized feature optimization for MI-based BCI,” Neural Networks, vol.118, pp.262-270, 2019.
 G. Pfurtscheller and F. L. Da Silva, "Event-related EEG/MEG synchronization and desynchronization: Basic principles", Clin. Neurophysiol., vol. 110, no. 11, pp. 1842-1857, 1999.