Emotion Decoding and Consciousness Evaluation in patients with DOC through EEG Microstate analysis.

Area of Science:

• Neuroscience
• Biomedical Engineering
• Clinical Neurology

Background:

• The Coma Recovery Scale-Revised (CRS-R) is standard for assessing disorders of consciousness (DOC) but suffers from subjective judgment and limited patient response.
• Existing emotion recognition brain-computer interfaces (BCIs) for DOC lack specific, quantitative indicators.
• There is a need for objective, reliable methods to assess consciousness in patients with DOC.

Purpose of the Study:

• To investigate the feasibility of using electroencephalography (EEG) microstates for emotion recognition in patients with disorders of consciousness (DOC).
• To develop a more objective and quantitative indicator for assessing conscious processing in DOC.
• To explore the relationship between EEG microstate dynamics and conscious processing in DOC.

Main Methods:

• Recorded EEG data from 9 patients with DOC and 11 healthy volunteers.
• Applied EEG microstate analysis to capture spatio-temporal features of EEG signals.
• Utilized microstate topoplots to simplify complex EEG data for emotion recognition.

Main Results:

• Achieved 94.16% average classification accuracy for emotion recognition in healthy participants.
• Demonstrated 77.94% average classification accuracy when applying the system to patients with DOC.
• EEG microstate dynamics showed association with conscious processing in the DOC group.

Conclusions:

• EEG microstates offer a promising tool for emotion recognition and objective assessment in patients with disorders of consciousness.
• This novel BCI approach provides a more quantitative indicator compared to traditional methods.
• Further validation with larger patient cohorts is necessary to confirm these preliminary findings.