EEGBind：通过以脑电为中心的多模态绑定检测源级发作间期癫痫样放电
EEGBind: Detecting Source-Level Interictal Epileptiform Discharges via EEG-Centric Multimodal Binding
查看机构详情
- The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
- Ringgee Smart Technologies Co., Ltd.（瑞捷智能科技有限公司）
机构由 AI 辅助整理，请以论文原文为准。
浏览论文内容
中文总结 AI 辅助
EEGBind提出以脑电为中心的多模态绑定框架，利用视频上下文辅助进行五类源级IED分类，在基准上取得加权F1 0.8395的领先性能。
中文摘要 AI 辅助
源级分析发作间期癫痫样放电（IEDs）与术前评估和治疗计划相关，因为它有助于表征癫痫样活动可能起源的区域。除了检测是否存在IED外，该场景还要求将IED阳性活动分配到具有临床意义的脑区类别中。这一场景具有挑战性，因为短时脑电图（EEG）窗口中的源区证据可能微妙、部分，并受受试者变异性、类别不平衡和不完善的多模态上下文影响。我们提出了EEGBind，一种以脑电为中心的多模态绑定框架，用于五类源级IED分类。EEGBind将EEG作为主要模态，并围绕以脑电为中心的表示绑定同步的视频上下文特征。EEGBind不依赖早期或过强的多模态融合（这可能会扰动对源敏感的EEG表示），而是将视频上下文用作辅助证据以进行稳健分类。进一步使用视图一致性修复阶段来提高隐藏集鲁棒性，同时保留已学习的源类别边界。在NeuroMM 2026 Grand Challenge Track 3 NMM-Source-IED基准上，EEGBind在加权F1上达到0.8395，并优于强竞争者。这些结果支持以脑电为中心的多模态绑定作为源级IED分类的实用策略。开源代码可在该https URL获取。
英文摘要
Source-level analysis of interictal epileptiform discharges (IEDs) is relevant to presurgical evaluation and treatment planning because it helps characterize where epileptiform activity is likely to arise. Beyond detecting whether an IED is present, this setting requires assigning IED-positive activity to clinically meaningful brain-region categories. This setting is challenging because source-region evidence in short electroencephalography (EEG) windows can be subtle, partial, and affected by subject variability, class imbalance, and imperfect multimodal context. We present EEGBind, an EEG-centric multimodal binding framework for five-class source-level IED classification. EEGBind treats EEG as the primary modality and binds synchronized video-context features around an EEG-centric representation. Instead of relying on early or overly strong multimodal fusion, which may perturb the source-sensitive EEG representation, EEGBind uses video context as auxiliary evidence for robust classification. A view-consistent repair stage is further used to improve hidden-set robustness while preserving the learned source-class boundary. On the NeuroMM 2026 Grand Challenge Track 3 NMM-Source-IED benchmark, EEGBind achieves 0.8395 on weighted-F1 and outperforms strong competitors. These results support EEG-centric multimodal binding as a practical strategy for source-level IED classification. The open-source code is available at https://github.com/HKUSTGZ-ML4Health-Lab/NeuroMM2026_IED_Detection.
