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Related Experiment Videos

World's fastest brain-computer interface: Combining EEG2Code with deep learning.

Sebastian Nagel1, Martin Spüler1

  • 1Department of Computer Engineering, Wilhelm-Schickard-Institute for Computer Science, University of Tübingen, Tübingen, Germany.

Plos One
|September 7, 2019
PubMed
Summary

Deep learning decodes visual stimuli from electroencephalograms (EEG) for brain-computer interfaces (BCI). This novel method significantly improves information transfer rates, suggesting EEG holds more data than previously thought.

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Biomedical Engineering

Background:

  • Non-invasive Electroencephalograms (EEG) are widely used for brain-computer interfaces (BCI).
  • Decoding sensory information from EEG signals presents significant challenges.
  • Previous BCI approaches have limitations in information transfer rate and stimulus discrimination.

Purpose of the Study:

  • To present a novel deep learning approach for decoding sensory information from non-invasively recorded EEG.
  • To evaluate the performance of this approach in both passive and active BCI applications.
  • To investigate the information capacity of EEG signals for BCI control.

Main Methods:

  • A deep learning model was developed to decode sensory information from EEG.

Related Experiment Videos

  • The method was tested in a passive BCI to predict visual stimulus properties.
  • The method was also applied to an active BCI for asynchronous spelling control.

    • Main Results:

    • An average information transfer rate (ITR) of 701 bit/min was achieved in the passive BCI, with a peak online ITR of 1237 bit/min.
    • Up to 100% accuracy was obtained in discriminating 500,000 visual stimuli using only 2 seconds of EEG data.
    • An average utility rate of 175 bit/min was achieved for asynchronous spelling BCI, translating to 35 error-free letters per minute.

    Conclusions:

    • The presented deep learning method significantly outperforms previous approaches, extracting over three times more information from EEG.
    • EEG signals contain more information than currently utilized for BCI applications.
    • A performance ceiling for non-invasive visual BCI control may have been reached due to the high information content in EEG.