Original Article

Introducing a Convolutional Neural Network and Visualization of its Filters for Classification of EEG Signal for SSVEP Task

Abstract

Purpose: Brain-Computer Interface (BCI) systems are able to understand and execute commands through processing brain signals. It has numerous applications in the field of biomedical engineering such as rehabilitation, biometric and entertainment. A BCI system consists of four major parts: signal acquisition, signal pre-processing, feature extraction and classification. Steady State Visually Evoked Potentials (SSVEP) is one of the most common paradigms in BCI systems, which is generally a response to visual stimuli with the frequency between 5 to 60 Hz.
Materials and Methods: In this study, we suggest a Convolutional Neural Network (CNN) based model for the classification of EEG signal during SSVEP task. For the evaluation, the model was tested with different channels and electrodes.
Results: Results show that channels number 138 and 139 have the great potential to appropriately classify EEG signal.
Conclusion: Using the suggested model and the mentioned channels, the accuracy of 73.74% could be achieved in this study.

1- N. K. N. Aznan, S. Bonner, J. Connolly, N. Al Moubayed, and T. Breckon, “On the classification of ssvep-based dryeeg signals via convolutional neural networks,” in 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 3726–3731, IEEE, 2018.
2- S. N. Carvalho, T. B. Costa, L. F. Uribe, D. C. Soriano, G. F. Yared, L. C. Coradine, and R. Attux, “Comparative analysis of strategies for feature extraction and classification in ssvep bcis,” Biomedical Signal Processing and Control, vol. 21, pp. 34–42, 2015.
3- B. Z. Allison, E. W. Wolpaw, and J. R. Wolpaw, “Brain– computer interface systems: progress and prospects,” Expert review of medical devices, vol. 4, no.
4, pp. 463– 474, 2007. 4- H.-I. Suk and S.-W. Lee, “A novel bayesian framework for discriminative feature extraction in brain computer interfaces,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 2, pp. 286–299, 2012.
5- S.-K. Yeom, S. Fazli, K.-R. Müller, and S.-W. Lee, “An efficient erp-based brain-computer interface using random set presentation and face familiarity,” PloS one, vol. 9, no. 11, p. e111157, 2014.
6- D.-O. Won, H.-J. Hwang, S. Dähne, K.-R. Müller, and S.-W. Lee, “Effect of higher frequency on the classification of steadystate visual evoked potentials,” Journal of neural engineering, vol. 13, no. 1, p. 016014, 2015.
7- V. P. Oikonomou, G. Liaros, K. Georgiadis, E. Chatzilari, K. Adam, S. Nikolopoulos, and I. Kompatsiaris, “Comparative evaluation of state-of-the-art algorithms for ssvep-based bcis,” arXiv preprint arXiv:1602.00904, 2016.
8- J. Thomas, T. Maszczyk, N. Sinha, T. Kluge, and J. Dauwels, “Deep learning-based classification for brain computer interfaces,” in 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 234–239, IEEE, 2017.
9- N.-S. Kwak, K.-R. Müller, and S.-W. Lee, “A convolutional neural network for steady state visual evoked potential classification under ambulatory environment,” PloS one, vol. 12, no. 2, p. e0172578, 2017.
10- Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature, vol. 521, no. 7553, pp. 436–444, 2015. 11- Y. Bengio, I. Goodfellow, and A. Courville, Deep learning, vol. 1. Citeseer, 2017.
12- W. B. Ng, A. Saidatul, Y. Chong, and Z. Ibrahim, “Psd based features extraction for eeg signal during typing task,” in IOP Conference Series: Materials Science and Engineering, vol. 557, p. 012032, IOP Publishing, 2019.
13- F. Lotte, L. Bougrain, A. Cichocki, M. Clerc, M. Congedo, A. Rakotomamonjy, and F. Yger, “A review of classification algorithms for eeg-based brain–computer interfaces: a 10 year update,” Journal of neural engineering, vol. 15, no. 3, p. 031005, 2018.
14- R. T. Schirrmeister, J. T. Springenberg, L. D. J. Fiederer, M. Glasstetter, K. Eggensperger, M. Tangermann, F. Hutter, W. Burgard, and T. Ball, “Deep learning with convolutional neural networks for eeg decoding and visualization,” Human brain mapping, vol.38, no.11, pp.5391–5420, 2017.
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IssueVol 7 No 3 (2020) QRcode
SectionOriginal Article(s)
DOI https://doi.org/10.18502/fbt.v7i3.4617
Keywords
Brain-Computer Interface Steady State Visually Evoked Potentials Electroencephalogram Signal Processing Convolutional Neural Network
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
1.
Nouri A, Azizi K. Introducing a Convolutional Neural Network and Visualization of its Filters for Classification of EEG Signal for SSVEP Task. Frontiers Biomed Technol. 2020;7(3):151-159.