Gamma2Patterns: Deep Cognitive Attention Region Identification and Gamma-Alpha Pattern Analysis

TL;DR

This study introduces Gamma2Patterns, a multimodal EEG and eye-tracking framework that identifies neural regions and oscillatory signatures associated with deep cognitive attention, advancing understanding of focus mechanisms.

Contribution

The paper presents Gamma2Patterns, a novel multimodal approach combining EEG and eye-tracking data to map cortical regions and oscillatory patterns linked to deep focus.

Findings

Gamma power and burst rates are highest in frontopolar, temporal, and parieto-occipital regions during deep focus.

Gamma activity provides more discriminative markers of attention than Alpha oscillations.

Eye-tracking signals complement EEG data in identifying attention-related neural activity.

Abstract

Deep cognitive attention is characterized by heightened gamma oscillations and coordinated visual behavior. Despite the physiological importance of these mechanisms, computational studies rarely synthesize these modalities or identify the neural regions most responsible for sustained focus. To address this gap, this work introduces Gamma2Patterns, a multimodal framework that characterizes deep cognitive attention by leveraging complementary Gamma and Alpha band EEG activity alongside Eye-tracking measurements. Using the SEED-IV dataset [1], we extract spectral power, burst-based temporal dynamics, and fixation-saccade-pupil signals across 62 channels or electrodes to analyze how neural activation differs between high-focus (Gamma-dominant) and low-focus (Alpha-dominant) states. Our findings reveal that frontopolar, temporal, anterior frontal, and parieto-occipital regions exhibit the strongest Gamma power and burst rates, indicating their dominant role in deep attentional engagement, while Eye-tracking signals confirm complementary contributions from frontal, frontopolar, and frontotemporal regions. Furthermore, we show that Gamma power and burst duration provide more discriminative markers of deep focus than Alpha power alone, demonstrating their value for attention decoding. Collectively, these results establish a multimodal, evidence-based map of cortical regions and oscillatory signatures underlying deep focus, providing a neurophysiological foundation for future brain-inspired attention mechanisms in AI systems.