Paper Info
Title
SSVEP-Based Brain-Computer Interface Controlled Functional Electrical Stimulation System for Upper Extremity Rehabilitation.
Abstract
Traditional rehabilitation techniques have limited effects on the recovery of patients with tetraplegia. A brain–computer interface (BCI) provides an interactive channel that does not depend on the normal output of peripheral nerves and muscles. In this paper, an integrated framework of a noninvasive electroencephalogram (EEG)-based BCI with a noninvasive functional electrical stimulation (FES) is established, which can potentially enable the upper limbs to achieve more effective motor rehabilitation. The EEG signals based on steady-state visual evoked potential are used in the BCI. Their frequency domain characteristics identified by the pattern recognition method are utilized to recognize intentions of five subjects with average accuracy of 73.9%. Furthermore the movement intentions are transformed into instructions to trigger FES, which is controlled with iterative learning control method, to stimulate the relevant muscles of upper limbs tracking desired velocity and position. It is a useful technology with potential to restore, reinforce or replace lost motor function of patients with neurological injuries. Experiments with five healthy subjects demonstrate the feasibility of BCI integrated with upper extremity FES toward improved function restoration for an individual with upper limb disabilities, especially for patients with tetraplegia.
Year
Venue
Field
2016
IEEE Trans. Systems, Man, and Cybernetics: Systems
Rehabilitation,Functional electrical stimulation,Upper limb,Computer science,Brain–computer interface,Evoked potential,Tetraplegia,Artificial intelligence,Physical medicine and rehabilitation,Iterative learning control,Machine learning,Electroencephalography
DocType
Volume
Issue
Journal
46
7
Citations 
PageRank 
References 
2
0.36
0
Authors
4
Name
Order
Citations
PageRank
Xingang Zhao1339.49
Yaqi Chu232.43
Jianda Han322060.61
Zhiqiang Zhang416223.92