Multiclass Classification of Spatially Filtered Motor Imagery EEG Signals Using Convolutional Neural Network for BCI Based Applications

Wolpaw, J. R., & Wolpaw, E. W. (2012). Brain-computer interfaces: something new under the sun. In J. Wolpaw & E. W. Wolpaw (Eds.), Brain-computer interfaces: Principles and practice (pp. 1–14). Oxford: Oxford University Press.

Zhang, W., Sun, F., Wu, H., Tan, C., & Ma, Y. (2019). Asynchronous brain-computer interface shared control of robotic grasping. Tsinghua Science and Technology, 24(3), 360–370. https://doi.org/10.26599/TST.2018.9010111.

Rahman, M. K. M., & Joadder, M. A. M. (2017). A review on the components of EEG-based motor imagery classification with quantitative comparison. Appl Theory Comput Technol, 2(2), 1–15.

Thomas, J., Maszczyk, T., Sinha, N., Kluge, T., & Dauwels, J. (2017). Deep learning-based classification for brain-computer interfaces. In IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 234–239)

Blankertz, B., Tomioka, R., Lemm, S., Kawanabe, M., & Muller, K. R. (2007). Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Processing Magazine, 25(1), 41–56. https://doi.org/10.1109/MSP.2008.4408441.

Lu, H., Eng, H. L., Guan, C., Plataniotis, K. N., & Venetsanopoulos, A. N. (2010). Regularized common spatial pattern with aggregation for EEG classification in small-sample setting. IEEE Transactions on Biomedical Engineering, 57(12), 2936–2946. https://doi.org/10.1109/TBME.2010.2082540.

Tang, Z., Li, C., & Sun, S. (2017). Single-trial EEG classification of motor imagery using deep convolutional neural networks. Optik, 130, 11–18. https://doi.org/10.1016/j.ijleo.2016.10.117.

Zhang, J., Yan, C., & Gong, X. (2017, October). Deep convolutional neural network for decoding motor imagery based brain computer interface. In IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) (pp. 1–5). https://doi.org/10.1109/ICSPCC.2017.8242581

Tian, G., & Liu, Y. (2018). Study on Classification of Left-Right Hands Motor Imagery EEG Signals Based on CNN. In IEEE International Conference on Cognitive Informatics & Cognitive Computing (ICCI* CC) (pp. 324–329). https://doi.org/10.1109/ICCI-CC.2018.8482042

Lee, H. K., & Choi, Y. S. (2018). A convolution neural networks scheme for classification of motor imagery EEG based on wavelet time-frequecy image. In IEEE International Conference on Information Networking (ICOIN) (pp. 906–909). https://doi.org/10.1109/ICOIN.2018.8343254

Schirrmeister, R., Gemein, L., Eggensperger, K., Hutter, F., & Ball, T. (2017). Deep learning with convolutional neural networks for decoding and visualization of EEG pathology. In IEEE Signal Processing in Medicine and Biology Symposium (SPMB) (pp. 1–7). https://doi.org/10.1109/SPMB.2017.8257015

Yang, H., Sakhavi, S., Ang, K. K., & Guan, C. (2015). On the use of convolutional neural networks and augmented CSP features for multi-class motor imagery of EEG signals classification. In International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 2620–2623). https://doi.org/10.1109/EMBC.2015.7318929

Korhan, N., Dokur, Z., & Olmez, T. (2019). Motor imagery based EEG classification by using common spatial patterns and convolutional neural networks. In Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT) (pp. 1–4). https://doi.org/10.1109/EBBT.2019.8741832

Başar, E., & Düzgün, A. (2016). The CLAIR model: Extension of Brodmann areas based on brain oscillations and connectivity. International Journal of Psychophysiology, 103, 185–198. https://doi.org/10.1016/j.ijpsycho.2015.02.018.

Repovs, G. (2010). Dealing with noise in EEG recording and data analysis. Informatica Medica Slovenica, 15(1), 18–25.

Tabar, Y. R., & Halici, U. (2016). A novel deep learning approach for classification of EEG motor imagery signals. Journal of neural engineering, 14(1), 016003. https://doi.org/10.1088/1741-2560/14/1/016003.

Uktveris, T., & Jusas, V. (2017). Application of convolutional neural networks to four-class motor imagery classification problem. Information Technology and Control, 46(2), 260–273. https://doi.org/10.5755/j01.itc.46.2.17528.

Grosse-Wentrup, M., & Buss, M. (2008). Multiclass common spatial patterns and information theoretic feature extraction. IEEE Transactions on Biomedical Engineering, 55(8), 1991–2000. https://doi.org/10.1109/TBME.2008.921154.

Moffett, S. X., O’Malley, S. M., Man, S., Hong, D., & Martin, J. V. (2017). Dynamics of high frequency brain activity. Scientific Reports, 7(1), 1–5. https://doi.org/10.1038/s41598-017-15966-6.

Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018). Convolutional neural networks: an overview and application in radiology. Insights into Imaging, 9(4), 611–629. https://doi.org/10.1007/s13244-018-0639-9.

Fawzi, A., Samulowitz, H., Turaga, D., & Frossard, P. (2016). Adaptive data augmentation for image classification. In IEEE International Conference on Image Processing (ICIP) (pp. 3688–3692). https://doi.org/10.1109/ICIP.2016.7533048

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097–1105). https://doi.org/10.1145/3065386

Alaskar, H. M. A. (2018). Deep learning of EMG time–frequency representations for identifying normal and aggressive actions. In International Journal of Computer Science and Network Security (IJCSNS) (pp. 16–25)

Hussain, Z., Gimenez, F., Yi, D., & Rubin, D. (2017). Differential data augmentation techniques for medical imaging classification tasks. In AMIA Annual Symposium Proceedings (pp. 979–984)

Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2017). A survey of deep neural network architectures and their applications. Neurocomputing, 234, 11–26. https://doi.org/10.1016/j.neucom.2016.12.038.

Sakhavi, S., Guan, C., & Yan, S. (2018). Learning temporal information for brain-computer interface using convolutional neural networks. IEEE Transactions on Neural Networks and Learning Systems, 29(11), 5619–5629. https://doi.org/10.1109/TNNLS.2018.2789927.

Martín-Clemente, R., Olias, J., Thiyam, D. B., Cichocki, A., & Cruces, S. (2018). Information theoretic approaches for motor-imagery BCI systems: Review and experimental comparison. Entropy, 20(7), 1–29. https://doi.org/10.3390/e20010007.

Chaudhary, S., Taran, S., Bajaj, V., & Sengur, A. (2019). Convolutional neural network based approach towards motor imagery tasks EEG signals classification. IEEE Sensors Journal, 19(12), 4494–4500. https://doi.org/10.1109/JSEN.2019.2899645.

Pfurtscheller, G., Neuper, C., Flotzinger, D., & Pregenzer, M. (1997). EEG-based discrimination between imagination of right and left hand movement. Electroencephalography and Clinical Neurophysiology, 103(6), 642–651. https://doi.org/10.1016/S0013-4694(97)00080-1.

Trakoolwilaiwan, T., Behboodi, B., Lee, J., Kim, K., & Choi, J. W. (2017). Convolutional neural network for high-accuracy functional near-infrared spectroscopy in a brain–computer interface: Three-class classification of rest, right-, and left-hand motor execution. Neurophotonics, 5(1), 011008. https://doi.org/10.1117/1.NPh.5.1.011008.