Abstract
The attention mechanism is one of the most popular deep learning techniques in recent years and it is arguably able to produce human-interpretable results. In this research, we developed a classification model combining two self-attention modules and a convolutional neural network. This model achieved benchmark or superior performance on two electroencephalography (EEG) recording datasets. Moreover, we demonstrated that the self-attention modules were able to capture features, including average voltage of signal features and instant voltage change of the EEG signals, by visualizing the attention maps they produced.
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References
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate (2014). http://arxiv.org/abs/1409.0473, cite arxiv:1409.0473Comment. Accepted at ICLR 2015 as oral presentation
Bang, J.S., Lee, S.W.: Interpretable convolutional neural networks for subject-independent motor imagery classification (2021)
Blankertz, B., et al.: The BCI competition. III: validating alternative approaches to actual BCI problems. IEEE Trans. Neural Syst. Rehabil. Eng. 14(2), 153–159 (2006). https://doi.org/10.1109/TNSRE.2006.875642
Chaudhari, S., Mithal, V., Polatkan, G., Ramanath, R.: An attentive survey of attention models (2021)
Chen, K., Wang, J., Chen, L.C., Gao, H., Xu, W., Nevatia, R.: ABC-CNN: an attention based convolutional neural network for visual question answering, November 2015
Cheng, J., Dong, L., Lapata, M.: Long short-term memory-networks for machine reading (2016)
Cisotto, G., Zanga, A., Chlebus, J., Zoppis, I., Manzoni, S., Markowska-Kaczmar, U.: Comparison of attention-based deep learning models for EEG classification (2020)
Cordonnier, J.B., Loukas, A., Jaggi, M.: On the relationship between self-attention and convolutional layers (2020)
Craik, A., He, Y., Contreras-Vidal, J.L.: Deep learning for electroencephalogram (EEG) classification tasks: a review. J. Neural Eng. 16(3), 031001 (2019)
Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale (2021)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition (2015)
Lee, Y.E., Lee, S.H.: EEG-transformer: self-attention from transformer architecture for decoding EEG of imagined speech (2021)
Liu, X., Shen, Y., Liu, J., Yang, J., **ong, P., Lin, F.: Parallel spatial-temporal self-attention CNN-based motor imagery classification for BCI. Front. Neurosci. 14 (2020). https://doi.org/10.3389/fnins.2020.587520. https://www.frontiersin.org/article/10.3389/fnins.2020.587520
Lotte, F., et al.: A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update. J. Neural Eng. 15(3), 031005 (2018)
Lotte, F., Lécuyer, A., Guan, C.: Towards a Fully Interpretable EEG-based BCI System, July 2010. https://hal.inria.fr/inria-00504658. Working paper or preprint
Luong, M.T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation (2015)
Qu, X., Hall, M., Sun, Y., Sekuler, R., Hickey, T.J.: A personalized reading coach using wearable EEG sensors-a pilot study of brainwave learning analytics, pp. 501–507 (2018)
Qu, X., Liu, P., Li, Z., Hickey, T.: Multi-class time continuity voting for EEG classification. In: Frasson, C., Bamidis, P., Vlamos, P. (eds.) BFAL 2020. LNCS (LNAI), vol. 12462, pp. 24–33. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60735-7_3
Qu, X., Mei, Q., Liu, P., Hickey, T.: Using EEG to distinguish between writing and ty** for the same cognitive task. In: Frasson, C., Bamidis, P., Vlamos, P. (eds.) BFAL 2020. LNCS (LNAI), vol. 12462, pp. 66–74. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60735-7_7
Qu, X., Sun, Y., Sekuler, R., Hickey, T.: EEG markers of stem learning, pp. 1–9 (2018). https://doi.org/10.1109/FIE.2018.8659031
Schreyer, H.M., Gollisch, T.: Nonlinear spatial integration in retinal bipolar cells shapes the encoding of artificial and natural stimuli. Neuron 109(10), 1692–1706 (2021). https://doi.org/10.1016/j.neuron.2021.03.015
Smith, S.J.M.: EEG in the diagnosis, classification, and management of patients with epilepsy. J. Neurol. Neurosurg. Psychiatry 76(suppl 2), ii2–ii7 (2005). https://doi.org/10.1136/jnnp.2005.069245. https://jnnp.bmj.com/content/76/suppl_2/ii2
Sturm, I., Bach, S., Samek, W., Müller, K.R.: Interpretable deep neural networks for single-trial EEG classification (2016)
Sun, J., **e, J., Zhou, H.: EEG classification with transformer-based models. In: 2021 IEEE 3rd Global Conference on Life Sciences and Technologies (2021)
Vaswani, A., et al.: Attention is all you need 30 (2017). https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
Wairagkar, M., Hayashi, Y., Nasuto, S.J.: Dynamics of long-range temporal correlations in broadband EEG during different motor execution and imagery tasks. Front. Neurosci. 15 (2021). https://doi.org/10.3389/fnins.2021.660032. https://www.frontiersin.org/article/10.3389/fnins.2021.660032
Willett, F.R., Avansino, D.T., Hochberg, L.R., Henderson, J.M., Shenoy, K.V.: High-performance brain-to-text communication via imagined handwriting. Nature 593, 249–254 (2021)
Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3–19. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_1
Yin, W., Schütze, H., **ang, B., Zhou, B.: ABCNN: attention-based convolutional neural network for modeling sentence pairs (2018)
Zhang, X., Yao, L., Wang, X., Monaghan, J., McAlpine, D., Zhang, Y.: A survey on deep learning-based non- invasive brain signals: recent advances and new frontiers. J. Neural Eng. 18, 031002 (2021)
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Yi, L., Qu, X. (2022). Attention-Based CNN Capturing EEG Recording’s Average Voltage and Local Change. In: Degen, H., Ntoa, S. (eds) Artificial Intelligence in HCI. HCII 2022. Lecture Notes in Computer Science(), vol 13336. Springer, Cham. https://doi.org/10.1007/978-3-031-05643-7_29
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