Martyna Beata Płomecka

EEG-GAN generates synthetic EEG data to improve classification accuracy across multiple datasets and classifiers, achieving up to 16% improvement in decoding accuracy.

The paper introduces a machine learning benchmark for classifying reading tasks using EEG and eye-tracking data, providing a public dataset, baselines, and an evaluation platform.

The paper develops a population model of decision making that demonstrates how criticality in cortical networks can lead to perceptual ambiguity and switches, validated with fMRI data.

The paper investigates EEG signal diversity during propofol sedation, finding increased diversity in responsive subjects and decreased diversity in unresponsive subjects, suggesting EEG diversity as an indirect index of consciousness.