Transfer Learning of an Ensemble of DNNs for SSVEP BCI Spellers without User-Specific Training

This is the official repository for the paper titled "Transfer Learning of an Ensemble of DNNs for SSVEP BCI Spellers without User-Specific Training", which is published in Journal of Neural Engineering [1]: https://iopscience.iop.org/article/10.1088/1741-2552/acacca (Arxiv link: https://arxiv.org/abs/2209.01511). This repository allows you to train the ensemble of DNNs and classify the SSVEP signal using the most similar $k$ many DNNs as explained in the paper.

Preparation

The Benchmark dataset [2] and BETA dataset [3] must be downloaded. The link for the both datasets: http://bci.med.tsinghua.edu.cn/download.html.

Training and evaluating the proposed Ensemble of DNNs method

In our performance evaluations, we conducted the comparisons (following the procedure in the literature) in a leave-one-participant-out fashion. For example, we constitute the ensemble using 34 (69) participants and test the performance on the remaining participant, who is considered a new user. This process is repeated 35 (70) times in the case of the benchmark (BETA) dataset. While calculating the information transfer rate (ITR) results, a 0.5 second gaze shift time is taken into account. We use the DNN architecture of [4] as a DNN architecture in the ensemble. In the DNN architecture, we use three sub-bands and nine channels (Pz, PO3, PO5, PO4, PO6, POz, O1, Oz, O2).

Results

The original results of our ensemble method for both the benchmark and BETA datasets are now available in the 'Results' folder.

References

1. O. B. Guney and H. Ozkan, “Transfer learning of an ensemble of dnns for ssvep bci spellers without user-specific training,” Journal of Neural Engineering, vol. 20, 016013, Jan 2023.
2. Y. Wang, X. Chen, X. Gao, and S. Gao, “A benchmark dataset for ssvep-based brain–computer interfaces,” IEEE Transactions on Neural Systems and Rehabilitation Engineering,vol. 25, no. 10, pp. 1746–1752, 2016.
3. B. Liu, X. Huang, Y. Wang, X. Chen, and X. Gao, “Beta: A large benchmark database toward ssvep-bci application,” Frontiers in Neuroscience, vol. 14, p. 627, 2020.
4. O. B. Guney, M. Oblokulov and H. Ozkan, "A Deep Neural Network for SSVEP-Based Brain-Computer Interfaces," IEEE Transactions on Biomedical Engineering, vol. 69, no. 2, pp. 932-944, 2022.