Mohammed Yeasin

The paper uses Bayesian nonparametrics and machine learning to analyze neural microstates during speech perception, finding that specific microstates and brain regions predict speech categorization accuracy and perceptual gradiency.

This study shows that lateral orbitofrontal cortex encodes environmental risk and subjective risk preference during decision-making in rats, with risk-preferring rats showing reduced risk encoding and increased reward magnitude encoding.

The paper uses a convolutional neural network to decode brain regions and frequency bands associated with response time variation in speech categorization tasks.

This paper uses Bayesian non-parametrics to decode speech categorization by analyzing EEG microstates and their relationship to response times.

The paper uses machine learning to decode speech categorization from EEG data, finding that speech categories are decoded with high accuracy as early as 120 ms.

The paper shows induced gamma-band oscillations better decode speech categories than evoked beta-band activity, suggesting induced rhythms dominate speech categorization.

This study uses EEG and computational methods to identify brain connectivity states that predict speech categorization decision speed, finding strong correlations between neural patterns and response times.