In modern society, different identification and verification methods are being used, and everyone finds that security is a top concern. Traditional methods like passwords and hardware tokens may be lost or stolen, resulting in identification failure. Therefore, we require reliable and robust human recognition techniques. Using electroencephalography (EEG), person identification systems can be robust, anti- spoof, and efficient. This paper aims to develop an efficient deep-learning model for person identification using EEG signals. We proposed a deep learning model using a 1D Convolutional Neural Network (CNN) and stacked Long Short-Term Memory (LSTM) separately. The effective dataset DEAP was used for evaluating the proposed method. The proposed models were trained on the first 10 seconds of EEG data (60 seconds long) and tested on random 10 seconds of data from the remaining part of the data. The results indicate that stacked LSTM slightly outperformed 1D CNN with up to 99.97% accuracy with just eight data channels (out of 64 channels) and 15 subjects. The comparative analysis between parameters like the number of channels used, the length of data used for training and testing, and the number of subjects indicates that stacked LSTM outperforms 1D CNN as RNNs can remember certain features of time-series data better than CNN.
This work is licensed under a Creative Commons Attribution 4.0 International License.
- Arnau-Gonzalez, P., Katsigiannis, S., Ramzan, N., Tolson, D., and Arevalillo-Herrez, M. 2017. Es1d: A deep network for eeg-based subject identification. In 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE). IEEE, 81–85. DOI: https://doi.org/10.1109/BIBE.2017.00-74
- Fan, Y., Shi, X., and Li, Q. 2021. Cnn-based personal identification system using resting state electroencephalography. Computational Intelligence and Neuroscience 2021. DOI: https://doi.org/10.1155/2021/1160454
- Fraschini, M., Hillebrand, A., Demuru, M., Didaci, L., and Marcialis, G. L. 2014. An eeg-based biometric system using eigenvector centrality in resting state brain networks. IEEE Signal Processing Letters 22, 6, 666–670. DOI: https://doi.org/10.1109/LSP.2014.2367091
- Goldberger, A. L., Amaral, L. A., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., and Stanley, H. E. 2000. Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. circulation 101, 23, e215–e220. DOI: https://doi.org/10.1161/01.CIR.101.23.e215
- Mao, Z., Yao, W. X., and Huang, Y. 2017. Eeg-based biometric identification with deep learning. In 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 609–612. DOI: https://doi.org/10.1109/NER.2017.8008425
- Rehman, U. u., Kamal, K., Iqbal, J., and Sheikh, M. F. 2019. Biometric identification through ecg signal using a hybridized approach. In Proceedings of the 2019 5th International Conference on Computing and Artificial Intelligence. 226–230. DOI: https://doi.org/10.1145/3330482.3330496
- Schalk, G., McFarland, D. J., Hinterberger, T., Birbaumer, N., and Wolpaw, J. R. 2004. Bci2000: a general-purpose brain-computer interface (bci) system. IEEE Transactions on biomedical engineering 51, 6, 1034–1043. DOI: https://doi.org/10.1109/TBME.2004.827072
- Sun, Y., Lo, F. P.-W., and Lo, B. 2019. Eeg-based user identification system using 1d-convolutional long short-term memory neural networks. Expert Systems with Applications 125, 259–267. DOI: https://doi.org/10.1016/j.eswa.2019.01.080
- Wilaiprasitporn, T., Ditthapron, A., Matchaparn, K., Tongbuasirilai, T., Banluesombatkul, N., and Chuangsuwanich, E. 2019. Affective eeg-based person identification using the deep learning approach. IEEE Transactions on Cognitive and Developmental Systems 12, 3, 486–496. DOI: https://doi.org/10.1109/TCDS.2019.2924648