Designing Hybrid Brain-Machine Interfaces to Detect Movement Attempts in Stroke Patients

Abstract

Hybrid brain-machine interfaces (BMIs) combining brain and muscle activity are a promising therapeutic alternative for rehabilitation of stroke patients with severe paralysis. In this study, we compare different approaches utilizing electroencephalographic (EEG) and electromyographic (EMG) activity to detect movement attempts of stroke patients with complete hand paralysis. Data of 20 patients with a chronic stroke involving the motor cortex were analyzed, and the performance of EEG-based, EMG-based or hybrid classifiers were simulated offline. We show that the combination of EEG and EMG improves the accuracy of movement detection, but that muscles unrelated to the task can also provide high accuracies, reflecting compensatory mechanisms. This result underscores the importance of appropriate designs of hybrid BMIs to maximize their rehabilitative potential.