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P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
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Week-long visuomotor coordination and relaxation trainings do not increase sensorimotor rhythms (SMR) based

L Botrel1, A Kübler1

  • 1Institute of Psychology, University of Würzburg, Würzburg, Germany.

Behavioural Brain Research
|June 5, 2019
PubMed
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.

Keywords:
BCIBCI predictorsBrain–computer interfacesProgressive muscle relaxationPsychological interventionsSMRSensorimotor rhythmsTrainingVisuo-motor coordination

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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.