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Assessment and Communication for People with Disorders of Consciousness
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A cross-subject decoding algorithm for patients with disorder of consciousness based on P300 brain computer

Fei Wang1,2, Yinxing Wan1, Zhuorong Li1

  • 1School of Software, South China Normal University, Guangzhou, China.

Frontiers in Neuroscience
|August 7, 2023
PubMed
Summary

This study introduces a novel brain-computer interface (BCI) decoding algorithm to improve communication for patients with disorders of consciousness (DOC). The new method enhances P300 signal detection, aiding in clinical diagnosis and prognosis.

Keywords:
EEGP300brain computer interfacecross-subjectdisorder of consciousness

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Computer Science

Background:

  • Brain-computer interface (BCI) offers potential communication for patients with disorders of consciousness (DOC).
  • Decoding algorithms for DOC patients' EEG data are limited by small datasets and poor performance.
  • Significant differences exist between normal and DOC patient EEG signals, complicating algorithm training.

Purpose of the Study:

  • To improve P300 signal detection in patients with disorders of consciousness (DOC) using a novel brain-computer interface (BCI) decoding algorithm.
  • To enhance the training dataset for DOC patients by incorporating normal population data.
  • To address the challenges posed by inter-subject variability in EEG data for BCI applications.

Main Methods:

  • Proposed a domain adaptation-based decoding algorithm (WD-ADSTCN) for P300 signal detection.
  • Utilized Wasserstein distance to filter normal population EEG data, augmenting the training set.
  • Employed an adversarial approach to minimize discrepancies between normal and patient EEG data.

Main Results:

  • Achieved over 70% average accuracy in cross-subject P300 detection for 7 out of 11 DOC patients.
  • Observed improvements in clinical diagnosis and Coma Recovery Scale-Revised (CRS-R) scores three months post-experiment.
  • Demonstrated the efficacy of the WD-ADSTCN algorithm in a P300 BCI system for DOC patients.

Conclusions:

  • The developed WD-ADSTCN algorithm shows promise for P300 BCI systems in patients with disorders of consciousness.
  • The method has significant implications for improving clinical diagnosis and assessing prognosis in DOC patients.
  • Domain adaptation techniques can effectively enhance BCI performance for individuals with challenging neurological conditions.