Post-Adaptation Effects in a Motor Imagery Brain-Computer Interface Online Coadaptive Paradigm

Jose Diogo Cunha, Serafeim Perdikis, Sebastian Halder, Reinhold Scherer*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Online coadaptive training has been successfully employed to enable people to control motor imagery (MI)-based brain-computer interfaces (BCIs), allowing to completely skip the lengthy and demotivating open-loop calibration stage traditionally applied before closed-loop control. However, practical reasons may often dictate to eventually switch off decoder adaptation and proceed with BCI control under a fixed BCI model, a situation that remains rather unexplored. This work studies the existence and magnitude of potential post-adaptation effects on system performance, subject learning and brain signal modulation stability in a state-of-the-art, coadaptive training regime inspired by a game-like design. The results extracted in a cohort of 20 able-bodied individuals reveal that ceasing classifier adaptation after three runs (approx. 30 min) of a single-session training protocol had no significant impact on any of the examined BCI control and learning aspects in the remaining two runs (about 20 min) with a fixed classifier. Fifteen individuals achieved accuracies that are better than chance level and allowed them to successfully execute the given task. These findings alleviate a major concern regarding the applicability of coadaptive MI BCI training, thus helping to further establish this training approach and allow full exploitation of its benefits.

Original languageEnglish
Article number9371701
Pages (from-to)41688-41703
Number of pages16
JournalIEEE Access
Publication statusPublished - 2021


  • Brain-computer interface
  • classifier adaptation
  • coadaptation
  • motor imagery
  • online learning
  • user training

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering


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