Curvelet Transform Based Two Class Motor Imagery Classification
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32845
Curvelet Transform Based Two Class Motor Imagery Classification

Authors: Nebi Gedik

Abstract:

One of the important parts of the brain-computer interface (BCI) studies is the classification of motor imagery (MI) obtained by electroencephalography (EEG). The major goal is to provide non-muscular communication and control via assistive technologies to people with severe motor disorders so that they can communicate with the outside world. In this study, an EEG signal classification approach based on multiscale and multi-resolution transform method is presented. The proposed approach is used to decompose the EEG signal containing motor image information (right- and left-hand movement imagery). The decomposition process is performed using curvelet transform which is a multiscale and multiresolution analysis method, and the transform output was evaluated as feature data. The obtained feature set is subjected to feature selection process to obtain the most effective ones using t-test methods. SVM and k-NN algorithms are assigned for classification.

Keywords: motor imagery, EEG, curvelet transform, SVM, k-NN

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 551

References:


[1] Y. Li, R. Zhou, R. Xu, J. Luo, S.-X. Jiang, “A quantum mechanics-based framework for EEG signal feature extraction and classification.” IEEE Transactions on Emerging Topics Computing, vol. 14, no. 8, pp. 1-11, 2020.
[2] M. K. Islam Molla, S. Sultana, S. Zahan, S. Yasmin, “Rhythmic component extraction from EEG signal using data adaptive multiband filtering” 2018 International Conference on Comp. Com. Chemical, Material and Electronic Engineering (IC4ME2), Rajshahi, pp. 1-5, 2018.
[3] R. K. Chaurasiya, N. D. Londhe, S. Ghosh “Statistical Wavelet Features, PCA, and SVM Based Approach for EEG Signals Classification,” World Academy of Science, Engineering and Technology International Journal of Electrical, Computer, Electronics and Communication Engineering, vol.9, no.2, 2015.
[4] D. Zhang, H. Y. Song, R. Xu, W. J. Zhou, Z. P. Ling, B. Hong, “Toward a minimally invasive brain-computer interface using a single subdural channel: A visual speller study,” NeuroImage, vol.71, pp. 30-4, 2013.
[5] J. Jin, H. H. Zhang, I. Daly, X. Y. Wang, A. Cichocki, “An improved P300 pattern in BCI to catch user’s attention,” Journal of Neural Engineering, vol.14. no.3, 2017.
[6] G. Xu, X. Shen, S. Chen, Y. Zong, C. Zhang, H. Yue, ..., W. Che, “A deep transfer convolutional neural network framework for EEG signal classification,” IEEE Access, vol.7, pp. 112767-112776, 2019.
[7] E. A. Mousavi, J. J. Maller, P. B. Fitzgerald, B. J. Lithgow, “Wavelet common spatial pattern in asynchronous offline brain computer interfaces,” Biomedical Signal Processing and Control, vol.6, no.2, pp. 121-128, 2011.
[8] S. Kumar, A. Sharma, T. Tsunoda, “An improved discriminative filter bank selection approach for motor imagery EEG signal classification using mutual information,” BMC Bioinform., vol.18, no.16, p. 545, 2017.
[9] Y. You, W. Chen, T. Zhang, “Motor imagery EEG classification based on flexible analytic wavelet transform,” Biomedical Signal Processing and Control, vol.62, p.102069, 2020.
[10] N. S. Malan, S. Sharma, “Feature selection using regularized neighbourhood component analysis to enhance the classification performance of motor imagery signals,” Comput. Biol. Med., vol.107, pp. 118-126, 2019.
[11] BCI competition III, http://www.bbci.de/competition/iii/ (last accessed 3.11.2021).
[12] BCI competition III dataset IIIa, http://www.bbci.de/competition/iii/desc_IIIa.pdf (last accessed 3.11.2021)
[13] E. J. Cand`es and D. L. Donoho, “Curvelets, Multiresolution Representation, and Scaling Laws,” Department of Statistics Stanford University Stanford, CA 94305-4065, USA, 1999.
[14] E. Cand`es, L. Demanet, D. Donoho, L. Ying, “Fast Discrete Curvelet Transforms” SIAM Journal on Multiscale Modeling and Simulation, vol.5, no.3, pp.861-889, 2006.
[15] 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.
[16] 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.