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Decoding of Walking Imagery and Idle State Using Sparse Representation Based on fNIRS.

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  • 1Institute of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.

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|March 10, 2021
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Summary

This study introduces a novel brain-computer interface (BCI) using functional near-infrared spectroscopy (fNIRS) and sparse representation classification (SRC) to decode walking imagery. The developed fNIRS-BCI offers a promising new rehabilitation tool for individuals with walking dysfunction.

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Area of Science:

  • Neuroscience
  • Rehabilitation Engineering
  • Biomedical Signal Processing

Background:

  • Walking dysfunction significantly impacts patients' quality of life.
  • Brain-computer interfaces (BCI) offer potential for active rehabilitation.
  • Functional near-infrared spectroscopy (fNIRS) measures brain activity non-invasively.

Purpose of the Study:

  • To develop and evaluate a fNIRS-BCI system for decoding walking imagery and idle states.
  • To investigate the effectiveness of sparse representation classification (SRC) for fNIRS signal analysis.
  • To establish a novel active rehabilitation training method for patients with walking dysfunction.

Main Methods:

  • Fifteen subjects participated, with fNIRS signals recorded during walking imagery and idle states.
  • HbO (oxyhemoglobin) signals were preprocessed (filtering, drift correction).
  • Mean, peak, and RMS values of HbO signals were extracted as features for SRC, compared against SVM, KNN, LDA, and LR.

Main Results:

  • SRC achieved an average classification accuracy of 91.55±3.30% for walking imagery, outperforming SVM, KNN, LDA, and LR.
  • Combined features yielded higher accuracy than single features.
  • A time window of 2-8s for feature extraction resulted in the highest accuracy (94.33±2.60%).

Conclusions:

  • SRC effectively decodes walking imagery and idle states using fNIRS signals.
  • Feature extraction time windows significantly influence classification accuracy.
  • This fNIRS-BCI with SRC presents a novel, optional active rehabilitation approach for walking dysfunction.