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Unsupervised adaptation of an ECoG based brain-computer interface using neural correlates of task performance.

Vincent Rouanne1, Thomas Costecalde1, Alim Louis Benabid1,2

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Summary

This study introduces an auto-adaptive brain-computer interface (aaBCI) that trains and updates neural signal decoders during natural use, improving BCI usability for disabled individuals.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Rehabilitation Technology

Background:

  • Brain-computer interfaces (BCIs) enable communication and control for individuals with severe motor disabilities.
  • Current BCIs require extensive training for decoder calibration and updates, limiting their practical usability.
  • The need for continuous, real-time adaptation of BCIs is critical for enhancing user experience and independence.

Purpose of the Study:

  • To develop and validate an auto-adaptive BCI (aaBCI) system capable of online training and updating neural decoders during free use.
  • To integrate a motor task performance (MTP) classifier to detect user intention and facilitate decoder adaptation.
  • To demonstrate the efficacy of the aaBCI across discrete and continuous control paradigms.

Main Methods:

  • Proposed a novel aaBCI architecture incorporating a control decoder and an MTP decoder.
  • Utilized MTP decoder outputs to generate training data for online, real-time updates of the control decoder.
  • Validated the aaBCI concept using an ECoG dataset from a tetraplegic participant in an online simulation study.
  • Tested aaBCI performance on discrete (4-class exoskeleton control) and continuous (2D cursor control) BCI paradigms.

Main Results:

  • The aaBCI achieved a multiclass area under the ROC curve of 0.7404 for discrete control, outperforming chance levels.
  • For continuous control, the aaBCI achieved a cosine similarity of 0.1211, significantly above chance.
  • While slightly below supervised training performance (0.8187 multiclass AUC, 0.2002 continuous cosine similarity), aaBCI demonstrated effective online adaptation.

Conclusions:

  • The proposed aaBCI system enables real-time training and updating of neural decoders during BCI use, significantly enhancing usability.
  • The MTP decoder effectively identifies task performance, facilitating seamless online adaptation of control decoders.
  • aaBCI represents a promising advancement for more intuitive and accessible brain-computer interfaces, particularly for individuals with severe motor impairments.