[2E1-GS-10] AI application

[2E1-GS-10-01] NIRS-based BCI systems with ensemble learning for rehabilitation in patients with neurological diseases

〇Akira Masuo^1,2, Takuto Sakuma², Shohei Kato² (1. Shubun University Junior College, 2. Graduate School of Engineering, Nagoya Institute of Technology)

Keywords:Human Interface, Communication, Augmentative and Alternative Communication, Occupational Therapy, Near-Infrared Spectoroscopy

Providing a means of communication to support daily living for patients with severe motor dysfunction is crucial. We propose a brain-computer interface system based on near-infrared spectroscopy (NIRS) using ensemble learning to utilize physiological signals for communication. We used the OEG-SpO₂ to measure NIRS signals in three patients with neurological disorders. Brain function was measured using a block design consisting of 30 seconds of rest and task each, with a mental arithmetic task and a music recall task. The classifier was a random forest with feature selection and dimensionality compression. We evaluated the model performance integrating predictions obtained from datasets generated by applying different preprocessing methods. The results showed an accuracy of 85%, 79%, and 67% in participants A, B, and C, respectively. We plan to improve the discrimination performance and validate the robustness of the model against non-stationarity of the NIRS signal by long-term measurements.