Adaptive CSP for user independence in MI-BCI paradigm for upper LIMB stroke rehabilitation
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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.
|Title of host publication||2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings|
|Number of pages||4|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication date||20 Feb 2019|
|Publication status||Published - 20 Feb 2019|
|Event||2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Anaheim, United States|
Duration: 26 Nov 2018 → 29 Nov 2018
|Conference||2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018|
|Periode||26/11/2018 → 29/11/2018|
|Sponsor||IEEE Signal Processing Society, The Institute of Electrical and Electronics Engineers (IEEE)|
|Series||2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings|
© 2018 IEEE.
- Adaptive Common Spatial Patterns (ACSP), Brain-computer interface (BCI), Sensorimotor rhythms (SMR), Stroke rehabilitation