Assessing the Impact of Population Data Domain Differences on Transfer Learning in P300-based Brain-Computer Interfaces (Student Abstract)
Rally Lin, Christina Mo, Reyan Shariff, Darrick Zhang, Abdullah Alumar, Kaleb Kassaw, Leslie M. Collins, Boyla O. Mainsah
Abstract
Brain-computer interfaces (BCIs) can provide a means of communication for individuals with severe neuromuscular diseases, the target end-users. While personalized BCI machine learning models are the current standard, models trained on data from other users could reduce BCI calibration time. We use a novel dataset with BCI users with and without amyotrophic lateral sclerosis (ALS) and a popular BCI deep learning model, EEGNet, to assess the impact of population domain data on transfer learning of a P300 speller task in the ALS cohort. Results show that training on source data from the non-ALS cohort was detrimental to transfer learning. In contrast, generic EEGNet models trained on source data from the ALS cohort performed comparably as user-specific models. Our findings highlight the need for more data from target end-users populations in publicly available BCI datasets.
BibTeX
@article{Lin_Mo_Shariff_Zhang_Alumar_Kassaw_Collins_Mainsah_2025, title={Assessing the Impact of Population Data Domain Differences on Transfer Learning in P300-based Brain-Computer Interfaces (Student Abstract)}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/35271}, DOI={10.1609/aaai.v39i28.35271}, abstractNote={Brain-computer interfaces (BCIs) can provide a means of communication for individuals with severe neuromuscular diseases, the target end-users. While personalized BCI machine learning models are the current standard, models trained on data from other users could reduce BCI calibration time. We use a novel dataset with BCI users with and without amyotrophic lateral sclerosis (ALS) and a popular BCI deep learning model, EEGNet, to assess the impact of population domain data on transfer learning of a P300 speller task in the ALS cohort. Results show that training on source data from the non-ALS cohort was detrimental to transfer learning. In contrast, generic EEGNet models trained on source data from the ALS cohort performed comparably as user-specific models. Our findings highlight the need for more data from target end-users populations in publicly available BCI datasets.}, number={28}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Lin, Rally and Mo, Christina and Shariff, Reyan and Zhang, Darrick and Alumar, Abdullah and Kassaw, Kaleb and Collins, Leslie M. and Mainsah, Boyla O.}, year={2025}, month={Apr.}, pages={29415-29417} }