---
格式版本: 2
标题: "ProtoGIB-Workload: Learning Workload-Specific Neural Topology Prototypes across Subjects"
原文链接: "https://arxiv.org/abs/2608.10647"
发布日期: "2026-08-11"
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入库时间: "2026-08-12T03:16:52.933Z"
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去重键: "https://arxiv.org/abs/2608.10647"
---

## Computer Science > Human-Computer Interaction

## Title:ProtoGIB-Workload: Learning Workload-Specific Neural Topology Prototypes across Subjects

Authors:[Yuzhe Zhang](https://arxiv.org/search/cs?searchtype=author&query=Zhang,+Y), [Yixi Zhang](https://arxiv.org/search/cs?searchtype=author&query=Zhang,+Y), [Shengdian Jiang](https://arxiv.org/search/cs?searchtype=author&query=Jiang,+S), [Chengxi Xie](https://arxiv.org/search/cs?searchtype=author&query=Xie,+C), [Jihong Wang](https://arxiv.org/search/cs?searchtype=author&query=Wang,+J), [Huan Liu](https://arxiv.org/search/cs?searchtype=author&query=Liu,+H), [Man Yao](https://arxiv.org/search/cs?searchtype=author&query=Yao,+M), [Minnan Luo](https://arxiv.org/search/cs?searchtype=author&query=Luo,+M), [Chao Shen](https://arxiv.org/search/cs?searchtype=author&query=Shen,+C)

[View PDF](https://arxiv.org/pdf/2608.10647) [HTML (experimental)](https://arxiv.org/html/2608.10647v1)

> Abstract:Reliable electroencephalography (EEG)-based mental workload recognition is crucial for adaptive human-centered systems, yet practical deployment requires models to generalize to users unseen during training. Although functional connectivity graphs are widely adopted to capture workload-related neural interactions, they inherently entangle task-relevant structures with subject-specific physiological traits and sample-level noise. This entanglement often leads models to learn structural shortcuts, severely degrading cross-subject generalization. To address this, we propose ProtoGIB-Workload, a novel framework that explicitly regularizes and aligns graph structures for subject-independent workload recognition. Our approach introduces a Stochastic Graph Information Bottleneck (SGIB) to compress dense correlation priors into compact, task-relevant subgraphs, filtering out input-related redundancy. Crucially, to prevent the retention of subject-specific spurious edges, we propose a Class-Conditional Topology Stabilizer (CTS). Leveraging the fixed electrode coordinates of EEG data, CTS operates directly on graph-generation probabilities to encourage consistent edge-generation statistics across different subjects sharing the same workload class. Extensive experiments on two public EEG workload datasets and one in-house EEG cognitive load dataset of air traffic controllers under strict leave-one-subject-out (LOSO) protocols demonstrate that ProtoGIB-Workload significantly outperforms state-of-the-art temporal and graph-based baselines, improving the cross-subject Macro-F1 score by an average of 5.15% (up to 6.34%). Further analyses confirm that our method successfully extracts stable, cross-subject consistent neural connectivity patterns.

| Subjects: | Human-Computer Interaction (cs.HC) |
| --- | --- |
| Cite as: | [arXiv:2608.10647](https://arxiv.org/abs/2608.10647) \[cs.HC\] |
|  | (or [arXiv:2608.10647v1](https://arxiv.org/abs/2608.10647v1) \[cs.HC\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.10647](https://doi.org/10.48550/arXiv.2608.10647) |

## Submission history

From: Yuzhe Zhang \[[view email](https://arxiv.org/show-email/682d560d/2608.10647)\]  
**\[v1\]** Tue, 11 Aug 2026 08:31:31 UTC (1,026 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.10647) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
