Optimal Deep Learning-Based Recognition Model for EEG Enabled Brain-Computer Interfaces Using Motor-Imagery

[1] Stephe, S., Jayasankar, T., Vinoth Kumar, K. (2022). Motor imagery EEG recognition using deep generative adversarial network with EMD for BCI applications. Technical Gazette, 29 (1), 92-100. https://doi.org/10.17559/TV-20210121112228 Search in Google Scholar

[2] León, J., Escobar, J. J., Ortiz, A., Ortega, J., González, J., Martín-Smith, P., Gan, J. Q., Damas, M. (2020). Deep learning for EEG-based Motor Imagery classification: Accuracy-cost trade-off. PLOS One, 15 (6), e0234178. https://doi.org/10.1371/journal.pone.0234178 Search in Google Scholar

[3] Khan, J., Bhatti, M. H., Khan, U. G., Iqbal, R. (2019). Multiclass EEG motor-imagery classification with sub-band common spatial patterns. EURASIP Journal on Wireless Communications and Networking, 174. https://doi.org/10.1186/s13638-019-1497-y Search in Google Scholar

[4] Lee, H. K., Choi, Y.-S. (2018). A convolution neural networks scheme for classification of motor imagery EEG based on wavelet time-frequency image. In 2018 International Conference on Information Networking (ICOIN). IEEE, 906-909. https://doi.org/10.1109/ICOIN.2018.8343254 Search in Google Scholar

[5] Amin, S. U., Altaheri, H., Muhammad, G., Alsulaiman, M., Abdul, W. (2021). Attention based Inception model for robust EEG motor imagery classification. In 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE. https://doi.org/10.1109/I2MTC50364.2021.9460090 Search in Google Scholar

[6] Al-Saegh, A., Dawwd, S. A., Abdul-Jabbar, J. M. (2021). Deep learning for motor imagery EEG-based classification: A review. Biomedical Signal Processing and Control, 63, 102172. http://dx.doi.org/10.1016/j.bspc.2020.102172 Search in Google Scholar

[7] Altaheri, H., Muhammad, G., Alsulaiman, M., Amin, S. U., Altuwaijri, G. A., Abdul, W., Bencherif, M. A., Faisal, M. (2023). Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: A review. Neural Computing and Applications, 35, 14681-14722. https://doi.org/10.1007/s00521-021-06352-5 Search in Google Scholar

[8] Bang, J.-S., Lee, M.-H., Fazli, S., Guan, C., Lee, S.-W. (2022). Spatio-spectral feature representation for motor imagery classification using convolutional neural networks. IEEE Transactions on Neural Networks and Learning Systems, 33 (7), 3038-3049. https://doi.org/10.1109/tnnls.2020.3048385 Search in Google Scholar

[9] Bria, A., Marrocco, C., Tortorella, F. (2021). Sinc-based convolutional neural networks for EEG-BCI-based motor imagery classification. In Pattern Recognition: ICPR International Workshops and Challenges. Springer, LNCS 12661, 526-535. https://doi.org/10.1007/978-3-030-68763-2_40 Search in Google Scholar

[10] Yang, L., Song, Y., Ma, K., Xie, L. (2021). Motor imagery EEG decoding method based on a discriminative feature learning strategy. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29, 368-379. https://doi.org/10.1109/tnsre.2021.3051958 Search in Google Scholar

[11] Altuwaijri, G. A., Muhammad, G. (2022). A multibranch of convolutional neural network models for electroencephalogram-based motor imagery classification. Biosensors, 12 (1), 22. https://doi.org/10.3390%2Fbios12010022 Search in Google Scholar

[12] Huang, W., Chang, W., Yan, G., Yang, Z., Luo, H., Pei, H. (2022). EEG-based motor imagery classification using convolutional neural networks with local reparameterization trick. Expert Systems with Applications, 187, 115968. http://dx.doi.org/10.1016/j.eswa.2021.115968 Search in Google Scholar

[13] Mirzaei, S., Ghasemi, P. (2021). EEG motor imagery classification using dynamic connectivity patterns and convolutional autoencoder. Biomedical Signal Processing and Control, 68, 102584. https://doi.org/10.1016/j.bspc.2021.102584 Search in Google Scholar

[14] Musallam, Y. K., AlFassam, N. I., Muhammad, G., Amin, S. U., Alsulaiman, M., Abdul, W., Altaheri, H., Bencherif, M. A., Algabri, M. (2021). Electroencephalography-based motor imagery classification using temporal convolutional network fusion. Biomedical Signal Processing and Control, 69, 102826. https://doi.org/10.1016/j.bspc.2021.102826 Search in Google Scholar

[15] Zhang, C., Kim, Y.-K., Eskandarian, A. (2021). EEG-inception: An accurate and robust end-to-end neural network for EEG-based motor imagery classification. Journal of Neural Engineering, 18 (4), 046014. https://doi.org/10.1088/1741-2552/abed81 Search in Google Scholar

[16] Majoros, T., Oniga, S. (2021). Comparison of motor imagery EEG classification using feedforward and convolutional neural network. In IEEE EUROCON 2021 - 19th International Conference on Smart Technologies. IEEE, 25-29. https://doi.org/10.1109/EUROCON52738.2021.9535592 Search in Google Scholar