Interpretable EEG cognitive state assessment using fuzzy modeling and case-based neurophysiological evidence

Cognitive-state assessment in real-time is a key part of adaptive learning, neuroergonomics, and safety-critical systems. Electroencephalography (EEG) is helpful in monitoring neural oscillations, as it allows fast monitoring of changes in attention and working memory status of neural fluctuations that can respond to changes in attention and working memory. In this paper, an interpretable EEG-based framework is proposed to use sensitive fronto-temporal and temporo-parietal measures of dynamic working-memory volumes, profile attentional functions of target regions, and a transparent fuzzy-logic mechanism with detailed case-study analysis to represent the perception of cognitive status. EEG signals from bipolar pairs F7-T3 and F8-T4 capture verbal and visuospatial working-memory activities, and T4-T6 monitors attentional engagement across multimedia and text-type activities. The dynamic weighting model generates a composite working-memory index which is integrated with the attention measure to generate a continuous cognitive state index (CSI) indicative of passive, moderate and focused engagement. Case studies depict temporal EEG behavior, working memory shifts, changes in attention and the respective CSI trajectories. This indicates clear neurophysiological patterns, with pronounced right fronto-temporal fluctuation for high cognitive load, stable temporo-parietal activity for memory maintenance and consistently lower level steady activity on the left frontal regions.