On Recalibration Strategies for Brain-Computer Interfaces Based on the Detection of Motor Intentions

J. Ibáñez*, E. López-Larraz, E. Monge, F. Molina-Rueda, L. Montesano, Jose L Pons

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Coupling motor intentions decoded from cortical activities with coherent proprioceptive feedback is of interest for the motor rehabilitation of neurological patients with lesions in the central nervous system. For these interventions to be effective, repeated sessions need to be carried out to achieve functional long-lasting plastic changes of cortical circuits. Electroencephalography-based Brain-Computer Interfaces typically show significant decreases in accuracy when used across multiple sessions with fixed parameters. Therefore, it is important to look for optimal strategies to recalibrate these classifiers. Here we compare different recalibration strategies for systems decoding motor intentions based on electroencephalographic data of neurological patients.

Original languageEnglish (US)
Title of host publicationBiosystems and Biorobotics
PublisherSpringer International Publishing
Pages775-779
Number of pages5
DOIs
StatePublished - Jan 1 2017

Publication series

NameBiosystems and Biorobotics
Volume15
ISSN (Print)2195-3562
ISSN (Electronic)2195-3570

ASJC Scopus subject areas

  • Biomedical Engineering
  • Mechanical Engineering
  • Artificial Intelligence

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    Ibáñez, J., López-Larraz, E., Monge, E., Molina-Rueda, F., Montesano, L., & Pons, J. L. (2017). On Recalibration Strategies for Brain-Computer Interfaces Based on the Detection of Motor Intentions. In Biosystems and Biorobotics (pp. 775-779). (Biosystems and Biorobotics; Vol. 15). Springer International Publishing. https://doi.org/10.1007/978-3-319-46669-9_127