Abstract
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 language | English |
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Article number | 9371701 |
Pages (from-to) | 41688-41703 |
Number of pages | 16 |
Journal | IEEE Access |
Volume | 9 |
DOIs | |
Publication status | Published - 2021 |
Keywords
- Brain-computer interface
- classifier adaptation
- coadaptation
- motor imagery
- online learning
- user training
ASJC Scopus subject areas
- Computer Science(all)
- Materials Science(all)
- Engineering(all)