Abstract
Deep learning (DL) methods for attention detection based on electroencephalography (EEG) have received increasing interest in recent years. To improve the performance of the existing state-of-the-art (SOTA) DL models, we proposed to utilize time delay embedding method to construct new input for DL model which can yield better results on EEG classification task. To verify the effectiveness of the proposed strategy, the evaluation experiments were conducted on a public EEG dataset. Experimental results demonstrated the DL models with time delay embedding extension could outperform the counterpart original models. The findings indicate the improving DL models based on time delay embedding method could be a prospective methodology for EEG classification, especially for attention detection task.
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Cai, H., Xia, M., Nie, L., Wu, Y., Zhang, Y. (2021). Deep Learning Models with Time Delay Embedding for EEG-Based Attentive State Classification. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Communications in Computer and Information Science, vol 1517. Springer, Cham. https://doi.org/10.1007/978-3-030-92310-5_36
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DOI: https://doi.org/10.1007/978-3-030-92310-5_36
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