CoAdaptive Rehabilitative Brain-Computer Interface

User centered BCI for motor rehabilitation, based on low-cost devices, adaptive machine learning models, neurofeedback and operant conditioning. 

We design machine learning models to speed-up calibration time between session and between users. We design and evaluate different stimulating protocols to improve user engagement. We study the cognitive and attentional states to understand their impact in user BCI controlling capability. 

The ultimate goal is to validate coAR.BCI as a neurotechnology for post-straoke rehabilitation

Publications

Peterson, V., Spagnolo, V., Galván, C. M., Nieto, N., Spies, R., & Milone, D. H. (2025). Towards subject-centered co-adaptive brain-computer interfaces based on backward optimal transport. Journal of Neural Engineering. doi: 10.1088/1741-2552/addb7a 

Galván, C. M., Spies, R. D., Milone, D. H., & Peterson, V. (2024). Neurophysiologically meaningful motor imagery EEG simulation with applications to data augmentation. IEEE Trans. Neural Syst. Rehabil. Eng, 32, 2346-2355. doi: https://doi.org/10.1109/tnsre.2024.3417311

Peterson, V., Nieto, N., Wyser, D., Lambercy, O., Gassert, R., Milone, D. H., & Spies, R. D. (2021). Transfer learning based on optimal transport for motor imagery brain-computer interfaces. IEEE Transactions on Biomedical Engineering, 69(2), 807-817. doi: 10.1109/TBME.2021.3105912

Peterson, V., Galván, C., Hernández, H., & Spies, R. (2020). A feasibility study of a complete low-cost consumer-grade brain-computer interface system. Heliyon, 6(3), e0342. doi: https://doi.org/10.1016/j.heliyon.2020.e03425

Peterson, V., Galván, C., Hernández, H., & Spies, R. (2022). A motor imagery vs. rest dataset with low-cost consumer grade EEG hardware. Data in Brief, 6, 108225. doi: https://doi.org/10.1016/j.dib.2022.108225