A Once-Calibration Brain-Computer Interface to Enhance Convenience for Continuous BCI Interventions in Stroke Patients

作者：Zuguang Rao, Rui Zhang, Shenghong He, Yajun Zhou, Zilin Lu, Kendi Li, Yuanqing Li · 发表于：IEEE Sensors Journal · 年份：2024 · DOI：10.1109/jsen.2024.3510059 · 被引用次数：6 · 研究领域：EEG and Brain-Computer Interfaces、Functional Brain Connectivity Studies、Neuroscience and Neural Engineering

Brain-computer interfaces (BCIs) provide a means of translating neural activity into movement for stroke rehabilitation. Electroencephalography (EEG)-based motor imagery (MI) is a cognitive strategy to enhance motor recovery after stroke. However, traditional MI-BCI systems require extensive calibration before conducting online experiments, thus constraining their practicality. To enhance convenience, we propose a once-calibration strategy (ONCS) that allows each subject to perform only one calibration in continuous BCI interventions over one month. By using supervised and transfer learning to update the model with previous online data, repeated calibrations are eliminated. Furthermore, personalized channel selection (PCS) is designed to reduce the number of channels through the lowest event-related desynchronization (ERD). Compared to the traditional repetitive calibration strategy (RECS), RECS for intra- and inter-subject models, the proposed ONCS for inter-subject (ONCS-inter) models achieve better classification performance using 28 channels. Wherein, the ONCS-inter shows statistically significant improvements (${p} \lt 0.05$, one-tailed test). When using PCS for channel selection, ONCS-inter outperforms ONCS for intra-subject (ONCS-intra) (${p} \lt 0.01$, for 16,${18}, \ldots, {28}$channels, two-tailed test) and surpasses RECS (${p} \lt 0.05$for all channels, two-tailed test). Remarkably, ONCS-inter exceeds the best results achieved with traditional RECS, even with only ...