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An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
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True zero-training brain-computer interfacing--an online study.

Pieter-Jan Kindermans1, Martijn Schreuder2, Benjamin Schrauwen1

  • 1Electronics and Information Systems (ELIS) Dept., Ghent University, Ghent, Belgium.

Plos One
|July 29, 2014
PubMed
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This study bypasses Brain-Computer Interface (BCI) calibration using unsupervised learning. An unsupervised classifier, updated during use, achieves comparable performance to supervised methods after minimal trials, reducing setup time.

Area of Science:

  • Neuroscience
  • Machine Learning
  • Human-Computer Interaction

Background:

  • Brain-Computer Interfaces (BCIs) require user-specific calibration data for optimal performance.
  • Traditional BCI calibration involves time-consuming supervised learning, limiting application for users with low concentration.
  • Reducing calibration time is crucial for practical BCI applications like text entry.

Purpose of the Study:

  • To investigate the efficacy of unsupervised learning to bypass the calibration phase in auditory event-related potential (ERP) based BCIs.
  • To evaluate an unsupervised classifier that initializes randomly and updates during BCI usage.
  • To compare the spelling performance of unsupervised and supervised BCI approaches.

Main Methods:

  • An online study was conducted using an auditory event-related potential (ERP) paradigm.

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  • An unsupervised classifier was trained from scratch and continuously updated during operation.
  • A post-hoc analysis of previously spelled symbols was used to correct initial decoding errors.
  • Performance was compared against a standard supervised calibration-based BCI model with 10 healthy users.

    • Main Results:

    • The unsupervised approach successfully bypassed the need for initial calibration recordings.
    • Initially, the unsupervised classifier made decoding errors, which were rectified through post-hoc analysis.
    • After approximately 30 trials, the unsupervised method achieved performance comparable to the supervised model.
    • The unsupervised BCI demonstrated effective learning behavior even with low signal-to-noise ratio (SNR) data.

    Conclusions:

    • Unsupervised learning offers a viable alternative to traditional supervised calibration in BCI systems.
    • This approach significantly reduces the time and effort required for BCI setup.
    • The proposed method holds promise for improving the accessibility and practicality of BCIs, particularly for users with limited attentional capacity.