dorsal/arxiv
View SchemaGamma2Patterns: Deep Cognitive Attention Region Identification and Gamma-Alpha Pattern Analysis
| Authors | Sobhana Jahan, Saydul Akbar Murad, Nick Rahimi, Noorbakhsh Amiri Golilarz |
|---|---|
| Categories | |
| ArXiv ID | 2601.06257vv1 |
| URL | https://arxiv.org/abs/2601.06257 |
| License | http://creativecommons.org/licenses/by/4.0/ |
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.
{
"annotation_id": "baa35d40-4763-40ff-9d43-e7f1491ba333",
"date_created": "2026-02-17T05:53:07.512000Z",
"date_modified": "2026-02-17T05:53:07.512000Z",
"file_hash": "a245405382f9007fcbf32c0130fc4ee10b51fcf76df4a43a21233d3f6607dbc4",
"private": false,
"record": {
"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.",
"arxiv_id": "2601.06257",
"authors": [
"Sobhana Jahan",
"Saydul Akbar Murad",
"Nick Rahimi",
"Noorbakhsh Amiri Golilarz"
],
"categories": [
"q-bio.NC",
"cs.AI",
"cs.CV"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "Gamma2Patterns: Deep Cognitive Attention Region Identification and Gamma-Alpha Pattern Analysis",
"url": "https://arxiv.org/abs/2601.06257",
"version": "v1"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "9c00d9e5-6db2-4fcd-81a1-cfcb96ae2018",
"id": "arXiv Dataset",
"type": "Model",
"variant": "snapshot-2026-01-17",
"version": "0.1.0"
},
"user_id": 1000002
}