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Intra- and Inter-subject Variability in EEG-Based Sensorimotor Brain Computer Interface: A Review.

Simanto Saha1, Mathias Baumert1

  • 1School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA, Australia.

Frontiers in Computational Neuroscience
|February 11, 2020
PubMed
Summary
This summary is machine-generated.

Brain computer interfaces (BCI) use electroencephalogram (EEG) signals for motor rehabilitation. Transfer learning methods address variability in EEG data, improving BCI performance and reducing calibration needs.

Keywords:
brain computer interfaceelectroencephalographyinter-subject associativitysensorimotor rhythmstransfer learning

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Machine Learning

Background:

  • Brain-computer interfaces (BCI) for motor rehabilitation rely on sensorimotor rhythms (SMR) from electroencephalogram (EEG) data.
  • Variability in SMR over time and across subjects (intra- and inter-subject) creates a covariate shift, hindering model transferability in BCIs.

Purpose of the Study:

  • To highlight the significance of inter-session and inter-subject performance predictors for developing generalized BCI frameworks.
  • To reduce the need for extensive calibration and training sessions in BCI applications for both unimpaired and motor-impaired individuals.

Main Methods:

  • Exploration of transfer learning techniques to mitigate covariate shift in EEG-derived features.
  • Investigation of psychological and neurophysiological predictors to augment transfer learning in EEG-based BCIs.
  • Assessment of inter-subject associativity to enhance BCI model generalization.

Main Results:

  • Transfer learning effectively compensates for inter-subject and inter-session variability in EEG data for BCIs.
  • Psychological and neurophysiological predictors show potential to improve transfer learning efficacy.
  • Inter-subject associativity assessment can further augment BCI generalization.

Conclusions:

  • Measuring inter-session/subject performance predictors is crucial for generalized BCI frameworks.
  • These predictors can significantly reduce the burden of calibration and training for BCI users.
  • This approach enhances the practicality and accessibility of BCIs for motor rehabilitation.