Comparison of EEG-Features and Classification Methods for Motor Imagery in Patients with Disorders of Consciousness

PLoS ONE, 2013 · DOI: 10.1371/journal.pone.0080479 · Published: November 25, 2013

Simple Explanation

The study investigates whether brain-computer interface (BCI) techniques used on healthy individuals can accurately detect voluntary brain activity in patients with disorders of consciousness (DOC). Electroencephalogram (EEG) data was collected while participants imagined moving their hands. Various EEG features and machine learning algorithms were used to classify the data. The research explores the reliability of different EEG markers in both healthy subjects and DOC patients, aiming to identify markers that consistently reflect motor imagery despite potential neurological differences.

Study Duration

2 years

Participants

22 healthy participants, 5 MCS patients, and 9 UWS patients

Evidence Level

Not specified

Key Findings

1
In healthy participants, coherence and power spectra features showed the highest classification accuracies, with support vector machines (SVM) being the best classification tool.
2
Coherence patterns in healthy participants primarily involved frontal regions, differing from the expected central modulated m-rhythm.
3
No patients showed above-chance accuracies in power spectra and coherence after false-discovery rate (FDR) correction, suggesting challenges in applying these features to DOC patients.

Research Summary

This study compared different EEG features and classification methods to detect motor imagery in DOC patients, assessing their suitability based on performance in healthy participants. Coherence and power spectra yielded high classification accuracies in healthy subjects, with SVM being the best classifier. However, coherence patterns involved mainly frontal regions, not the expected central m-rhythm. Despite the promising results in healthy participants, no DOC patients showed above-chance accuracies in power spectra and coherence after FDR correction, highlighting the challenges in detecting voluntary brain activation in unresponsive patients.

Practical Implications

Diagnostic Tool Development

The research contributes to developing EEG-based diagnostic tools for detecting awareness and command-following in non-communicative brain-injured patients.

Feature Selection in BCI

The findings suggest that coherence measures, particularly those involving frontal regions, may be valuable features for brain-computer interfaces, even in individuals with motor disabilities or altered brain activity patterns.

Methodological Considerations

The study highlights the importance of FDR correction and careful consideration of potential false positives when applying machine learning techniques to data from DOC patients.

Study Limitations

1
The limited sample size of DOC patients.
2
The lack of ground truth to validate the results in unresponsive patients.
3
The potential for false positives in classification accuracies, requiring careful statistical correction.

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