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We present a simple deep learning-based framework commonly used in computer vision and demonstrate its effectiveness for cross-dataset transfer learning in mental imagery decoding tasks that are common in the field of Brain-Computer Interfaces (BCI).
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Leeb, R., Lee, F., Keinrath, C., Scherer, R., Bischof, H., Pfurtscheller, G.: Brain–Computer Communication: Motivation, Aim, and Impact of Exploring a Virtual Apartment. IEEE Transactions on Neural Systems and Rehabilitation Engineering 15
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Barachant, A.: Commande robuste d’un effecteur par une interface cerveau machine EEG asynchrone. Ph.D. thesis, Université de Grenoble (Mar 2012)
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Faller, J., Vidaurre, C., Solis-Escalante, T., Neuper, C., Scherer, R.: Autocalibration and Recurrent Adaptation: Towards a Plug and Play Online ERD-BCI. IEEE Transactions on Neural Systems and Rehabilitation Engineering 20
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Tangermann, M., Müller, K.R., Aertsen, A., Birbaumer, N., Braun, C., Brunner, C., Leeb, R., Mehring, C., Miller, K., Mueller-Putz, G., Nolte, G., Pfurtscheller, G., Preissl, H., Schalk, G., Schlögl, A., Vidaurre, C., Waldert, S., Blankertz, B.: Review of the BCI Competition IV. Frontiers in Neuroscience 6
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Samek, W., Meinecke, F.C., Müller, K.R.: Transferring Subspaces Between Subjects in Brain–Computer Interfacing. IEEE Transactions on Biomedical Engineering 60
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Luck, S.J.: An Introduction to the Event-Related Potential Technique, Second Edition. MIT Press (Jun 2014)
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Yi, W., Qiu, S., Wang, K., Qi, H., Zhang, L., Zhou, P., He, F., Ming, D.: Evaluation of EEG Oscillatory Patterns and Cognitive Process during Simple and Compound Limb Motor Imagery. PLoS ONE 9
2014
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Jayaram, V., Alamgir, M., Altun, Y., Scholkopf, B., Grosse-Wentrup, M.: Transfer learning in brain-computer interfaces. IEEE Computational Intelligence Magazine 11
2015
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Scherer, R., Faller, J., Friedrich, E.V.C., Opisso, E., Costa, U., Kübler, A., Müller-Putz, G.R.: Individually Adapted Imagery Improves Brain-Computer Interface Performance in End-Users with Disability. PLOS ONE 10
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Clerc, M., Bougrain, L., Lotte, F.: Brain-Computer Interfaces 2. Wiley-ISTE (Jul 2016)
2016
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Ian Goodfellow, Yoshua Bengio, Aaron Courville: Deep Learning. MIT Press (2016)
2016
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Zhou, B., Wu, X., Lv, Z., Zhang, L., Guo, X.: A Fully Automated Trial Selection Method for Optimization of Motor Imagery Based Brain-Computer Interface. PLOS ONE 11
2016
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Cho, H., Ahn, M., Ahn, S., Kwon, M., Jun, S.C.: EEG datasets for motor imagery brain–computer interface. GigaScience 6
2017
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Riyad, M., Khalil, M., Adib, A.: Incep-EEGNet: A ConvNet for Motor Imagery Decoding. In: El Moataz, A., Mammass, D., Mansouri, A., Nouboud, F. (eds.) Image and Signal Processing. pp. 103–111. Lecture Notes in Computer Science, Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-51935-3_11
2020
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Schneider, T., Wang, X., Hersche, M., Cavigelli, L., Benini, L.: Q-EEGNet: An Energy-Efficient 8-bit Quantized Parallel EEGNet Implementation for Edge Motor-Imagery Brain-Machine Interfaces. In: 2020 IEEE International Conference on Smart Computing (SMARTCOMP). pp. 284–289 (Sep 2020). https://doi.org/10.1109/SMARTCOMP50058.2020.00065
2020
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Deng, X., Zhang, B., Yu, N., Liu, K., Sun, K.: Advanced TSGL-EEGNet for Motor Imagery EEG-Based Brain-Computer Interfaces. IEEE Access 9
2021
Later among the works it cites.
Jeon, E., Ko, W., Yoon, J.S., Suk, H.I.: Mutual Information-Driven Subject-Invariant and Class-Relevant Deep Representation Learning in BCI. IEEE Transactions on Neural Networks and Learning Systems pp. 1–11 (2021). https://doi.org/10.1109/TNNLS.2021.3100583
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Ofner, P., Schwarz, A., Pereira, J., Müller-Putz, G.R.: Upper limb movements can be decoded from the time-domain of low-frequency EEG. PLOS ONE 12
2017
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Schirrmeister, R.T., Springenberg, J.T., Fiederer, L.D.J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W., Ball, T.: Deep learning with convolutional neural networks for EEG decoding and visualization. Human Brain Mapping 38
2017
Cited alongside, same era.
Jayaram, V., Barachant, A.: MOABB: Trustworthy algorithm benchmarking for BCIs. Journal of Neural Engineering 15
2018
Cited alongside, same era.
Lawhern, V.J., Solon, A.J., Waytowich, N.R., Gordon, S.M., Hung, C.P., Lance, B.J.: EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces. Journal of Neural Engineering 15
2018
Cited alongside, same era.
Raza, H., Chowdhury, A., Bhattacharyya, S., Samothrakis, S.: Single-Trial EEG Classification with EEGNet and Neural Structured Learning for Improving BCI Performance. In: 2020 International Joint Conference on Neural Networks (IJCNN). pp. 1–8 (Jul 2020). https://doi.org/10.1109/IJCNN48605.2020.9207100
2020
Cited alongside, same era.
2021
Later among the works it cites.
Xu, F., Miao, Y., Sun, Y., Guo, D., Xu, J., Wang, Y., Li, J., Li, H., Dong, G., Rong, F., Leng, J., Zhang, Y.: A transfer learning framework based on motor imagery rehabilitation for stroke. Scientific Reports 11
2021
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Zhu, Y., Li, Y., Lu, J., Li, P.: EEGNet With Ensemble Learning to Improve the Cross-Session Classification of SSVEP Based BCI From Ear-EEG. IEEE Access 9
2021
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Guetschel, P., Papadopoulo, T., Tangermann, M.: Embedding neurophysiological signals. In: 2022 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence, and Neural Engineering (MetroXRAINE). pp. 169–174. IEEE, Rome (Oct 2022). https://doi.org/10.1109/metroxraine54828.2022.9967496
2022
Later among the works it cites.
Kobler, R., Hirayama, J.i., Zhao, Q., Kawanabe, M.: SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG. In: Advances in Neural Information Processing Systems. vol. 35, pp. 6219–6235 (Dec 2022). https://doi.org/10.48550/arXiv.2206.01323
2022
Later among the works it cites.
Wei, X., Faisal, A.A., Grosse-Wentrup, M., Gramfort, A., Chevallier, S., Jayaram, V., Jeunet, C., Bakas, S., Ludwig, S., Barmpas, K., Bahri, M., Panagakis, Y., Laskaris, N., Adamos, D.A., Zafeiriou, S., Duong, W.C., Gordon, S.M., Lawhern, V.J., Śliwowski, M., Rouanne, V., Tempczyk, P.: 2021 BEETL Competition: Advancing Transfer Learning for Subject Independence and Heterogenous EEG Data Sets. In: Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track. pp. 205–219. PMLR (Jul 2022)
2022
Later among the works it cites.
Aristimunha, B., de Camargo, R.Y., Pinaya, W.H.L., Chevallier, S., Gramfort, A., Rommel, C.: Evaluating the structure of cognitive tasks with transfer learning (Jul 2023). https://doi.org/10.48550/arXiv.2308.02408
2023
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