ERD-Based Online Brain–Machine Interfaces (BMI) in the Context of Neurorehabilitation: Optimizing BMI Learning and Performance

摘要

Event-related desynchronization (ERD) of sensori-motor rhythms (SMR) can be used for online brain–machine interface (BMI) control, but yields challenges related to the stability of ERD and feedback strategy to optimize BMI learning. Here, we compared two approaches to this challenge in 20 right-handed healthy subjects (HS, five sessions each, S1-S5) and four stroke patients (SP, 15 sessions each, S1-S15). ERD was recorded from a 275-sensor MEG system. During daily training, motor imagery-induced ERD led to visual and proprioceptive feedback delivered through an orthotic device attached to the subjects' hand and fingers. Group A trained with a heterogeneous reference value (RV) for ERD detection with binary feedback and Group B with a homogenous RV and graded feedback (10 HS and 2 SP in each group). HS in Group B showed better BMI performance than Group A $({p} and improved BMI control from S1 to S5 $({ p}=0.012)$ while Group A did not. In spite of the small n, SP in Group B showed a trend for a higher BMI performance $({ p}=0.06)$ and learning was significantly better $({ p}< 0.05)$. Using a homogeneous RV and graded feedback led to improved modulation of ipsilesional activity resulting in superior BMI learning relative to use of a heterogeneous RV and binary feedback.