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Deep-learning online EEG decoding brain-computer interface using error-related potentials recorded with a

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Summary

This study introduces a portable, AI-enhanced brain-computer interface (BCI) using Emotiv EPOC+ EEG. It achieves high accuracy in detecting error-related potentials (ErrPs), enabling efficient human-computer interaction for individuals with disabilities.

Keywords:
brain computer interfacedeep convolutional generative-adversarial networkdeep-learningelectroencephalographyerror-related potentialintrinsic mode functionlong short-term memory network

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Brain-computer interfaces (BCIs) offer assistive technology for individuals with sensorimotor disabilities.
  • Current non-invasive BCIs using electroencephalography (EEG) face limitations in portability and versatility.
  • Event-related potentials (ERPs) are a common EEG signal for BCIs, but error-related potentials (ErrPs) offer unique discrimination opportunities.

Purpose of the Study:

  • To develop and evaluate a deep-learning augmented BCI system for discriminating ErrPs.
  • To utilize a consumer-grade, portable EEG headset (Emotiv EPOC+) for BCI applications.
  • To assess the performance of the BCI system in both offline and online settings.

Main Methods:

  • Employed deep learning techniques, including generative adversarial networks (GANs) and intrinsic mode function (IMF) augmentation, to enhance ErrP discrimination.
  • Recorded EEG data from 14 subjects performing a visual feedback task.
  • Implemented online and offline ErrP discrimination using the Emotiv EPOC+ headset.

Main Results:

  • Achieved online ErrP discrimination accuracies up to 81%.
  • Demonstrated performance comparable to professional 32/64-channel EEG systems.
  • Utilized minimalistic computing resources for deep learning model execution.

Conclusions:

  • The developed BCI model shows potential for portable, AI-enhanced, and efficient human-computer interaction.
  • This technology can accelerate the deployment of BCIs outside laboratory settings.
  • The system expands the application spectrum of non-invasive BCIs for broader accessibility.