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

TL;DR

Gamma2Patterns presents a multimodal framework that combines EEG Gamma ($31{-}50$ Hz) and Alpha ($8{-}14$ Hz) power with Eye-tracking to identify cortical regions underpinning deep cognitive attention. Using the SEED-IV dataset, it maps Gamma-dominant activation to frontopolar, frontal, frontotemporal, and parieto-occipital regions and shows Gamma power and burst duration as more discriminative than Alpha power for attention decoding. The approach couples classification with LIME explainability and constructs topographical maps and burst metrics to provide a physiologically grounded brain atlas for deep focus. The findings offer a neurophysiological basis for brain-inspired AI attention mechanisms and the integration of neural and behavioral markers in modeling sustained attention.

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.

Figures (9)

• Figure 1: Overview of the proposed model architecture for identifying brain regions associated with deep cognitive attention. Raw signals undergo artifact removal and preprocessing, including Alpha–Gamma band extraction, power computation, and burst analysis. The framework then performs classification with LIME-based explainability to assess feature relevance. In addition, power intensity visualizations and topographical brain maps are generated to highlight channel-wise feature importance and localize cortical regions associated with deep cognitive focus.
• Figure 2: Alpha vs Gamma power comparison per channel.
• Figure 3: LIME explainability using EEG data.
• Figure 4: LIME explainability using Eye tracking data.
• Figure 5: Channels and their importance for Alpha and Gamma bands.