Home /Research /Personalized upper limb stroke rehabilitation using data-driven multi-modal electroencephalography (EEG) and near-infrared spectroscopy (NIRS) based brain computer interface (BCI) with soft robotic glove
OTHER

Personalized upper limb stroke rehabilitation using data-driven multi-modal electroencephalography (EEG) and near-infrared spectroscopy (NIRS) based brain computer interface (BCI) with soft robotic glove

Isaac Okumura Tan, Lau Ha Chloe Chung, Anna Choo, Zhuo Zhang, Wai Hang Patrick Kwong, Ananda Sidarta, Kai Keng Ang

Year
2025
Citations
2
Access
Open access

Abstract

orbitofrontal).We increased stimulation intensity incrementally from 0.1 mA to a maximum of 6 mA in steps of 0.5 mA.Heart rate was monitored via electrocardiogram (ECG).HBC was quantified by interpolating heart rate at each time point and extracting the power at 0.1Hz.Results: HBC was observed during stimulation at two target sites -dACC and sgACC.This was demonstrated by a spectral peak at 0.1Hz and presence of HBC at 0.1Hz.No HBC was detected at control stimulation sites, which included the frontal and orbitofrontal regions.See figure for dACC.Conclusion: Administering iTBS intracranially to sgACC and dACC using sEEG induced HBC, implicating their role in the frontal-vagal pathway.This work supports the hypothesis that HBC involves a pathway through the central nervous system, validating HBC as a metric of frontal-vagal engagement.Further research is needed to explore clinical implications of HBC and its relationship to autonomic changes and treatment outcomes.

Keywords

Brain–computer interfaceElectroencephalographyInterface (matter)RehabilitationPhysical medicine and rehabilitationModalComputer scienceFunctional near-infrared spectroscopyPsychologyMedicine

Related papers

Browse all OTHER papers