Research
Print page Print page
Switch language
The Capital Region of Denmark - a part of Copenhagen University Hospital
Published

An adaptive CSP filter to investigate user independence in a 3-class MI-BCI paradigm

Research output: Contribution to journalJournal articleResearchpeer-review

  1. Review on motor imagery based BCI systems for upper limb post-stroke neurorehabilitation: From designing to application

    Research output: Contribution to journalReviewResearchpeer-review

  2. Muscle fibre morphology and microarchitecture in cerebral palsy patients obtained by 3D synchrotron X-ray computed tomography

    Research output: Contribution to journalJournal articleResearchpeer-review

  3. Towards an automated multimodal clinical decision support system at the post anesthesia care unit

    Research output: Contribution to journalJournal articleResearchpeer-review

  4. A computerized aid in ventilating neonates.

    Research output: Contribution to journalJournal articleResearch

  1. Workforce Attachment after Ischemic Stroke – The Importance of Time to Thrombolytic Therapy

    Research output: Contribution to journalJournal articleResearchpeer-review

  2. Global Impact of COVID-19 on Stroke Care and IV Thrombolysis

    Research output: Contribution to journalJournal articleResearchpeer-review

  3. Renal Impairment and Risk of Acute Stroke: The INTERSTROKE Study

    Research output: Contribution to journalJournal articleResearchpeer-review

  4. Urinary Sodium and Potassium, and Risk of Ischemic and Hemorrhagic Stroke (INTERSTROKE): a case-control study

    Research output: Contribution to journalJournal articleResearchpeer-review

View graph of relations

This paper describes the implementation of a Brain Computer Interface (BCI) scheme using a common spatial patterns (CSP) filter in combination with a Recursive Least Squares (RLS) approach to iteratively update the coefficients of the CSP filter. The proposed adaptive CSP (ACSP) algorithm is made more robust by introducing regularization using Diagonal Loading (DL), and thus will be able to significantly reduce the length of training sessions when introducing new patients to the BCI system. The system is tested on a 4-class multi-limb motor imagery (MI) data set from the BCI competition IV (2a), and a more complex single limb 3-class MI dataset recorded in-house. The latter dataset is produced to mimic an upper limb rehabilitation session, e.g., after stroke. The findings indicate that when extensive calibration data is available, the ACSP performs comparably to the CSP (kappa value of 0.523 and 0.502, respectively, for the 4-class problem); for reduced calibration sessions, the ACSP significantly improved the performance of the system (up to 4-fold). The proposed paradigm proved feasible and the ACSP algorithm seems to enable a user or semi user independent scenario, where the need for long system calibration sessions without feedback is eliminated.

Original languageEnglish
JournalComputers in Biology and Medicine
Volume103
Pages (from-to)24-33
Number of pages10
ISSN0010-4825
DOIs
Publication statusPublished - 2018

ID: 56480655