Acute single channel EEG predictors of cognitive function after stroke.

Area of Science:

• Neuroscience
• Clinical Neurology
• Biomedical Engineering

Background:

• Accurate prediction of post-stroke cognitive outcome is crucial for rehabilitation planning.
• Behavioral cognitive assessments are challenging in the acute stroke phase due to fatigue and language deficits.
• Exploring electrophysiological measures for early cognitive prediction is warranted.

Purpose of the Study:

• To evaluate the predictive value of single-channel wireless EEG data obtained acutely after stroke for longer-term cognitive function.
• To identify specific EEG indices that correlate with cognitive outcomes at 90 days post-stroke.

Main Methods:

• Resting-state EEG data (delta, theta, alpha, beta relative power, delta/alpha ratio (DAR), delta/theta ratio (DTR)) were recorded from 24 first-ever stroke patients within 72 hours.
• The Montreal Cognitive Assessment (MoCA) was administered at 90 days post-stroke.
• Correlation and regression analyses were used to assess relationships between acute EEG data, clinical factors, and 90-day MoCA scores.

Main Results:

• Four acute qEEG indices showed significant correlations with 90-day MoCA scores: DTR (r = -0.57), RP theta (r = 0.50), RP delta (r = -0.47), and DAR (r = -0.45).
• Acute DTR and stroke severity were the strongest predictors of MoCA scores at 90 days, explaining 75% of the variance.
• These findings highlight the prognostic value of specific acute EEG markers.

Conclusions:

• Acute EEG data, particularly DAR and DTR, demonstrate prognostic value for post-stroke cognitive outcomes.
• Single-channel EEG recordings in acute clinical settings offer an efficient and valid method for predicting cognitive function after stroke.
• These findings support the integration of EEG into acute stroke care for early prognostication.