Classification of left and right hand motor imagery EEG signals by using deep neural networks

Yıl 2021, Cilt: 9 Sayı: 4, 85 - 90, 31.12.2021

Nuri Korhan , Leyla Abilzade , Taner Ölmez , Zümray Dokur Ölmez

https://doi.org/10.18100/ijamec.995022

Cited By: 1

Öz

The brain-computer interface (BCI) is one of the most promising technologies that allows us to establish a relationship between brain and devices. In this study, three-channel EEG signals collected from nine subjects performing two motor imagery tasks are classified using two different deep neural network (DNN) based approaches called framework 1 (FW1) and framework 2 (FW2). The proposed frameworks were evaluated using BCI Competition IV-IIb dataset. In FW1, the raw EEG data is directly presented to the deep neural network without performing any pre-processing. In FW2, the EEG data is first filtered with five band pass filters with fifth order (Butterworth), then the common spatial patterns (CSP) method, which introduces additional pseudo channels, is applied to the filtered signals. Two experiments were conducted for each framework. In the first experiment, a unique DNN is trained for each subject, and in the second experiment only one DNN is trained with the combination of training sets of all subjects. The performance of the two experiments are then compared in terms of average accuracy. According to the simulation results, we did not observe a significant difference between the average classification accuracies obtained with the first and the second experiments. Therefore, we concluded that, by the use of DNNs we do not need to train several subject-specific networks which requires high computational loads. On the other hand, we observed that the average classification performance significantly improves by the filtering and extracting features with CSP pre-processes.

Anahtar Kelimeler

Common Spatial Patterns, Convolutional Neural Network, Deep Learning, EEG, Motor imagery

Destekleyen Kurum

Istanbul Technical University Scientific Research Project Unit

Proje Numarası

ITU-BAP MYL-2018-41621

Kaynakça

• D. Tan and A. Nijholt, “Brain-computer interfaces and human-computer interaction,” in Brain-Computer Interfaces, Springer, pp. 3–19, 2010.
• K. LaFleur, K. Cassady, A. Doud, K. Shades, E. Rogin, and B. He, “Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain–computer interface,” J. Neural Eng., vol. 10, no. 4, p. 46003, 2013.
• F. Lotte and I. B. Sud-ouest, “BCI’s Beyond Medical Applications.pdf,” pp. 26–34, 2012.
• Z. Tayeb et al., “Gumpy: A Python toolbox suitable for hybrid brain–computer interfaces,” J. Neural Eng., vol. 15, no. 6, p. 65003, 2018.
• B. Venthur, S. Dähne, J. Höhne, H. Heller, and B. Blankertz, “Wyrm: A brain-computer interface toolbox in python,” Neuroinformatics, vol. 13, no. 4, pp. 471–486, 2015.
• A. Gramfort et al., “MNE software for processing MEG and EEG data,” Neuroimage, vol. 86, pp. 446–460, 2014.
• Z. Zhang et al., “A Novel Deep Learning Approach With Data Augmentation to Classify Motor Imagery Signals,” IEEE Access, vol. 7, pp. 15945–15954, 2019.
• J. Yang, S. Yao, and J. Wang, “Deep Fusion Feature Learning Network for MI-EEG Classification,” IEEE Access, vol. 6, pp. 79050–79059, 2018.
• S. Kumar, A. Sharma, K. Mamun, and T. Tsunoda, “A deep learning approach for motor imagery EEG signal classification,” in 2016 3rd Asia-Pacific World Cong. on Com. Sci. and Eng. pp. 34–39, 2016.
• J. Zhang, C. Yan, and X. Gong, “Deep convolutional neural network for decoding motor imagery based brain computer interface,” in 2017 IEEE Int. Conf. on Sig. Pro., Comm. and Comp., pp. 1–5, 2017.
• H. Yang, S. Sakhavi, K. K. Ang, and C. Guan, “On the use of convolutional neural networks and augmented CSP features for multi-class motor imagery of EEG signals classification,” Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, pp. 2620–2623, 2015.
• X. Zhang, L. Yao, Q. Z. Sheng, S. S. Kanhere, T. Gu, and D. Zhang, “Converting your thoughts to texts: Enabling brain typing via deep feature learning of eeg signals,” in 2018 IEEE Int. Conf. on Perv. Comp. and Comm., pp. 1–10, 2018.
• M. Tangermann et al., “Review of the BCI competition IV,” Front. Neurosci., vol. 6, p. 55, 2012.
• M. Dai, D. Zheng, R. Na, S. Wang, and S. Zhang, “EEG Classification of Motor Imagery Using a Novel Deep Learning Framework,” Sensors, vol. 19, no. 3, p. 551, 2019.
• Y. R. Tabar and U. Halici, “A novel deep learning approach for classification of EEG motor imagery signals,” J. Neural Eng., vol. 14, no. 1, p. 16003, 2016.
• N. Kotoky and S.M. Hazarika, "Bispectrum Analysis of EEG for Motor Imagery Classification", Int., conf. on Sig. Pro. and Integ. Netw., pp. 581-586, 2014.
• V.F. Silva, R.M. Barbosa, P.M. Vieira, C.S. Lima, Ensemble learning based classification for BCI applications, IEEE 5th Portuguese Meeting on Bioengineering, (2017).
• D.M. Hermosilla, R.T. Codorniú, R.L. Baracaldo et al., Shallow Convolutional Network Excel for Classifying Motor Imagery EEG in BCI Applications, IEEE Access, 9, 98275-98286, 2021.
• B. Yang, C. Fan, C. Guan, X. Gu and M. Zheng, “A Framework on Optimization Strategy for EEG Motor Imagery Recognition”, 41st Ann. Int. Conf. of the IEEE Eng. in Med. and Bio. Soc., pp. 774-777, 2019.