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Treffer: Comparison of Classifier Calibration Schemes for Movement Intention Detection in Individuals with Cerebral Palsy for Inducing Plasticity with Brain-Computer Interfaces.

Title:
Comparison of Classifier Calibration Schemes for Movement Intention Detection in Individuals with Cerebral Palsy for Inducing Plasticity with Brain-Computer Interfaces.
Authors:
Jochumsen M; Department of Health Science and Technology, Aalborg University, 9260 Gistrup, Denmark., Sulkjær CS; Department of Health Science and Technology, Aalborg University, 9260 Gistrup, Denmark., Dalgaard KS; Department of Health Science and Technology, Aalborg University, 9260 Gistrup, Denmark.
Source:
Sensors (Basel, Switzerland) [Sensors (Basel)] 2025 Dec 02; Vol. 25 (23). Date of Electronic Publication: 2025 Dec 02.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: MEDLINE
Imprint Name(s):
Original Publication: Basel, Switzerland : MDPI, c2000-
MeSH Terms:
Brain-Computer Interfaces* , Cerebral Palsy*/physiopathology, Humans ; Electroencephalography/methods ; Movement/physiology ; Male ; Female ; Adult ; Calibration ; Intention ; Young Adult
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Grant Information:
22-B01-1432 Elsass Foundation
Contributed Indexing:
Keywords: EEG; brain–computer interface; cerebral palsy; movement intention; movement-related cortical potential; neural plasticity; neurorehabilitation
Entry Date(s):
Date Created: 20251211 Date Completed: 20251211 Latest Revision: 20251214
Update Code:
20251214
PubMed Central ID:
PMC12694662
DOI:
10.3390/s25237347
PMID:
41374720
Database:
MEDLINE

Weitere Informationen

Brain-computer interfaces (BCIs) have successfully been used for stroke rehabilitation by pairing movement intentions with, e.g., functional electrical stimulation. It has also been proposed that BCI training is beneficial for people with cerebral palsy (CP). To develop BCI training for CP patients, movement intentions must be detected from single-trial EEG. The study aim was to detect movement intentions in CP patients and able-bodied participants using different classification scenarios to show the technical feasibility of BCI training in CP patients. Five CP patients and fifteen able-bodied participants performed wrist extensions and ankle dorsiflexions while EEG was recorded. All but one participant repeated the experiment on 1-2 additional days. The EEG was divided into movement intention and idle epochs that were classified with a random forest classifier using temporal, spectral, and template matching features to estimate movement intention detection performance. When calibrating the classifier on data from the same day and participant, 75% and 85% classification accuracies were obtained for CP- and able-bodied participants, respectively. The performance dropped by 5-15 percentage points when training the classifier on data from other days and other participants. In conclusion, movement intentions can be detected from single-trial EEG, indicating the technical feasibility of using BCIs for motor training in people with CP.