Adaptive multimodal biosignal control for exoskeleton supported stroke rehabilitation

A relevant issue of neuro-interfacing wearable robots in rehabilitation is the necessity to have training data, since the collection of sufficient data from patients within a reasonable recording time is not always possible. However, the use of historic data (e.g., session-to-session transfer, subject-to-subject transfer) can often lead to a reduction in classification performance which is affected by the selection of the historic data (i.e., which historic data was chosen for transfer). In this paper, we analyze two approaches to handle this reduction. First, we used incremental algorithms that can be adapted to the current session when trainable components (the spatial filter and the classifier) are transferred between different sessions. Second, we increased the number of sessions to learn more generalized models. To evaluate the approaches, we used electroencephalographic data that was recorded as training data for demonstrating our neuro-interfacing wearable robot in the application of upper-body sensorimotor rehabilitation. The data was collected from the same healthy subject on 14 different days (14 sessions). Our results showed that the use of a mixture of training sessions improved the classification performance. Further, we could show that the adaptive approaches contributed to less variability in performance that allows the system to be more robust. Hence, one can efficiently use both approaches (i.e., adapting and generalizing the models) depending on how much training data is available. Finally, the analyzed approaches are very promising to increase system applicability in upper-body sensorimotor robotic rehabilitation.