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Decoding of Turning Intention during Walking Based on EEG Biomarkers.

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Summary

This study introduces a novel brain-computer interface (BCI) to detect the intention to turn in real-time using electroencephalography (EEG). The developed BCI system offers a promising advancement for real-time applications requiring asynchronous intention detection.

Keywords:
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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Human-Computer Interaction

Background:

  • Existing brain-computer interface (BCI) literature lacks asynchronous intention models for real-time applications.
  • Electroencephalography (EEG) has not been fully validated as a real-time biomarker for specific intentions like turning.

Purpose of the Study:

  • To propose and validate a novel BCI approach for real-time detection of the intention to turn.
  • To develop a BCI methodology capable of differentiating between monotonous walking and the intention to turn.
  • To compare the efficacy of popular signal processing and classification algorithms for this BCI application.

Main Methods:

  • Utilized electroencephalography (EEG) signals to develop a BCI for intention detection.
  • Employed H∞ filter and ASR for signal filtering, and Riemannian space analysis for processing and classification.
  • Compared various classifiers to estimate the distance of test samples in Riemannian space, avoiding power-based models and baseline correction.

Main Results:

  • Achieved average cross-validation accuracies of 66.2% (generic) and 69.6% (personalized) using the H∞ filter.
  • In pseudo-online tests, the best subject achieved 43.9% True Positives (TP) and 2.9 False Positives per minute (FP/min).
  • The final validation demonstrated 71.43% accuracy with 2.5 FP/min and 0.21s turn anticipation for the best subject.

Conclusions:

  • EEG can serve as a reliable biomarker for detecting the intention to turn in real-time.
  • The proposed Riemannian space-based BCI methodology effectively differentiates walking from turning intentions.
  • This BCI approach shows significant potential for real-time applications, improving upon existing limitations.