Adaptive CSP for user independence in MI-BCI paradigm for upper LIMB stroke rehabilitation

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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.

Original languageEnglish
Title of host publication2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings
Number of pages4
PublisherInstitute of Electrical and Electronics Engineers Inc.
Publication date20 Feb 2019
Pages420-423
Article number8646403
ISBN (Electronic)9781728112954
DOIs
Publication statusPublished - 20 Feb 2019
Event2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Anaheim, United States
Duration: 26 Nov 201829 Nov 2018

Conference

Conference2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018
LandUnited States
ByAnaheim
Periode26/11/201829/11/2018
SponsorIEEE Signal Processing Society, The Institute of Electrical and Electronics Engineers (IEEE)
Series2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

    Research areas

  • Adaptive Common Spatial Patterns (ACSP), Brain-computer interface (BCI), Sensorimotor rhythms (SMR), Stroke rehabilitation

ID: 282089311