fNIRS4D

The code will be published in this repository when our paper is accepted.

Datasets

MA

Paper: [1]

Dataset: http://doc.ml.tu-berlin.de/hBCI

Github: https://github.com/JaeyoungShin/hybrid-BCI

UFFT

Paper: [2]

Dataset: https://doi.org/10.6084/m9.figshare.9783755.v1

Github: https://github.com/JaeyoungShin/fNIRS-dataset

Baseline Models

fNIRSNet

Paper: [3]

Github: https://github.com/wzhlearning/fNIRSNet

fNIRS-T

Paper: [4]

Github: https://github.com/wzhlearning/fNIRS-Transformer

1D-CNN

Paper: [5]

Github: https://github.com/sunzhe839/tensorfusion_EEG_NIRS

CNN and LSTM

Paper: [6]

Github: https://github.com/boyanglyu/nback_align

References

[1] J. Shin, A. von Luhmann, B. Blankertz, D.-W. Kim, J. Jeong, H.-J. Hwang, and K.-R. Muller, “Open access dataset for EEG+NIRS single-trial classifification,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 25, no. 10, pp. 1735–1745, 2017.

[2] S. Bak, J. Park, J. Shin, and J. Jeong, “Open-access fNIRS dataset for classifification of unilateral finger-and foot-tapping,” Electronics, vol. 8, no. 12, p. 1486, 2019.

[3] Z. Wang, J. Fang and J. Zhang, "Rethinking Delayed Hemodynamic Responses for fNIRS Classification," IEEE Trans. Neural Syst. Rehabil. Eng., vol. 31, pp. 4528-4538, 2023.

[4] Z. Wang, J. Zhang, X. Zhang, P. Chen, and B. Wang, “Transformer model for functional near-infrared spectroscopy classification,” IEEE J. Biomed. Health Inform., vol. 26, no. 6, pp. 2559–2569, 2022.

[5] Z. Sun, Z. Huang, F. Duan, and Y. Liu, “A novel multimodal approach for hybrid brain–computer interface,” IEEE Access, vol. 8, pp. 89 909–89 918, 2020.

[6] B. Lyu, T. Pham, G. Blaney, Z. Haga, A. Sassaroli, S. Fantini, and S. Aeron, “Domain adaptation for robust workload level alignment between sessions and subjects using fNIRS,” J. Biomed. Opt., vol. 26, no. 2, pp. 1–21, 2021.