On the design of EEG-based movement decoders for completely paralyzed stroke patients
Date
2018-11-20Keywords
Neuroprostheses
Brain machine interface (BMI)
Rehabilitation robotics
Proprioceptive feedback
Motor rehabilitation
Stroke
Neurotechnology
Abstract
Background: Brain machine interface (BMI) technology has demonstrated its efficacy for rehabilitation of paralyzed
chronic stroke patients. The critical component in BMI-training consists of the associative connection (contingency)
between the intention and the feedback provided. However, the relationship between the BMI design and its
performance in stroke patients is still an open question.
Methods: In this study we compare different methodologies to design a BMI for rehabilitation and evaluate their
effects on movement intention decoding performance. We analyze the data of 37 chronic stroke patients who
underwent 4 weeks of BMI intervention with different types of association between their brain activity and the
proprioceptive feedback. We simulate the pseudo-online performance that a BMI would have under different
conditions, varying: (1) the cortical source of activity (i.e., ipsilesional, contralesional, bihemispheric), (2) the type of
spatial filter applied, (3) the EEG ...
Type
article