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

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 < 0.05, one-tailed test). When using PCS for channel selection, ONCS-inter outperforms ONCS for intra-subject (ONCS-intra) (P < 0.01$ , for 16, 18, . . . , 28 channels, two-tailed test) and surpasses RECS (P < 0.05$ for all channels, two-tailed test). Remarkably, ONCS-inter exceeds the best results achieved with traditional RECS, even with only two channels. Extensive comparison and ablation studies demonstrate the effectiveness of our proposed ONCS combined with inter-subject models and a few channels in maintaining classification accuracy. The proposed ONCS with PCS holds promise for enhancing the convenience of continuous BCI interventions within one month for stroke patients.