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Week-long visuomotor coordination and relaxation trainings do not increase sensorimotor rhythms (SMR) based
Behavioural Brain Research
|June 5, 2019
Summary
This study investigated predictors for brain-computer interface (BCI) accuracy. While predictor training improved some measures, it did not enhance BCI performance, suggesting further research is needed for effective prediction models.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Human-Computer Interaction
Background:
- Brain-computer interfaces (BCI) translate neural activity into device commands.
- Electroencephalography (EEG)-based sensorimotor rhythm (SMR) BCIs face challenges with up to 30% of users showing no detectable imagery-related changes.
- Individual factors like concentration and visuomotor coordination (VMC) task performance have shown potential in predicting SMR-BCI accuracy.
Purpose of the Study:
- To investigate the effectiveness of extended predictor training on SMR-BCI performance.
- To determine if psychological variables, such as mindfulness and VMC, can be replicated as predictors of SMR-BCI accuracy after training.
Main Methods:
- A pre-post study design was employed with 39 participants undergoing four daily sessions of predictor training (VMC or relaxation).
- SMR-BCI accuracy was assessed before and after the training period.
- Psychological predictors, including state mindfulness and VMC performance, were measured.
Main Results:
- SMR-BCI accuracy showed a slight increase from the first to the second session after predictor training.
- The manipulation of predictor variables was successful, but overall SMR-BCI performance did not significantly improve.
- BCI accuracy positively correlated with a neurophysiological SMR predictor and the state mindfulness scale.
Conclusions:
- Extended predictor training did not enhance SMR-BCI performance, despite successful manipulation of predictor variables.
- The neurophysiological SMR predictor and mindfulness show promise for predicting BCI accuracy.
- Future research should focus on refining prediction models by further exploring VMC and mindfulness as potential predictors.
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