Paper Info
Title
ADAPTIVE CSP FOR USER INDEPENDENCE IN MI-BCI PARADIGM FOR UPPER LIMB STROKE REHABILITATION
Abstract
A 3-class motor imagery (MI) Brain-Computer Interface (BCI) system, that implements subject adaptation with short to non-existing calibration sessions is proposed. The proposed adaptive common spatial patterns (ACSP) algorithm was tested on two datasets (an open source data set (4-class MI), and an in-house data set (3-class MI)). Results show that when long calibration data is available, the ACSP performs only slightly better (4%) than the CSP, but for short calibration sessions, the ACSP significantly improved the performance (up to 4-fold). An investigation into class separability of the in-house data set was performed and was concluded that the "Pinch"movement was more easily discriminated than "Grasp" and "Elbow Flexion". The proposed paradigm proved feasible and provided insights to help choose the motor tasks leading to best results in potential real-life applications. The ACSP enabled a successful semi user independent scenario and showed potential to be a tool towards an improved, personalized stroke rehabilitation protocol.
Year
DOI
Venue
2018
10.1109/GlobalSIP.2018.8646403
2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
Keywords
Field
DocType
Brain-computer interface (BCI),Stroke rehabilitation,Sensorimotor rhythms (SMR),Adaptive Common Spatial Patterns (ACSP)
Rehabilitation,GRASP,Upper limb,Computer science,Source data,Brain–computer interface,Stroke,Speech recognition,Class separability,Motor imagery
Conference
ISSN
ISBN
Citations 
2376-4066
978-1-7281-1295-4
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Ana P. Costa100.34
Jakob S. Møller2171.73
Helle K Iversen3183.56
Sadasivan Puthusserypady418127.49