Pytorch implementation for a transformer-based model for SSVEP classifcation in EEG based Brain-Computer Interface (BCI)
About this project
SSVEPformer A transformer-based model for SSVEP classifcation in EEG based Brain-Computer Interface (BCI) Reproducing the paper A transformer-based deep neural network model for SSVEP classification (https://arxiv.org/abs/2210.04172) [1] on Dataset 1 from Nakanishi et al.2015 [2] We followed the same procedure in the paper in the 1st experiment on the same dataset and got same results. to reproduce the experiment check the colab notebook in this repo, it is a straighforward self contained tutorial. Requirements PyTorch Numpy Scipy Keras 3 A Keras 3 version with JAX backend is available (only the SSVEPFormer model) on the Keras site examples section : EEG BCI SSVEP Tutorial References [1] Chen, J. et al. (2023) ‘A transformer-based deep neural network model for SSVEP classification’, Neural Networks, 164, pp. 521–534. Available at: https://doi.org/10.1016/j.neunet.2023.04.045. [2] Nakanishi, M. et al. (2015) ‘A Comparison Study of Canonical Correlation Analysis Based Methods for Detecting Steady-State Visual Evoked Potentials’, Plos One, 10(10), p. e0140703. Available at: https://doi.org/10.1371/journal.pone.0140703. Usage
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- Apache-2.0
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- 8 Feb 2025
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