arXiv：cs.LG· Ramya Sundaram, Muhammad Aslam Jarwar, M Uma, Sunder Ali Khowaja, Qurat-Ul-Ain Mastoi·· 7 小时前
面向模态无关的癫痫发作分类：统一 EEG 与 iEEG 信号的深度学习框架
Towards Modality-Agnostic Seizure Classification: A Unified Deep Learning Framework for Heterogeneous EEG and Intracranial EEG Signals
AI 导读
研究提出统一多模态信号协调框架 UMSH，将不同采样率和记录条件下的 EEG 与 iEEG 信号整合进同一癫痫发作分类流程。该方法用 cGAN 生成合成样本以缓解数据类别极度不平衡，并采用 EEGNet-ViT 同时学习局部判别模式与全局上下文信息。实验结果显示该框架在癫痫分类上表现稳健，展示了 EEG-iEEG 多模态学习在下一代癫痫监测与临床决策支持中的潜力。
正文
Abstract:Seizure prediction and detection, essential for clinical treatment of epilepsy, remain the crucial task of this neurological disease. EEG and iEEG are widely used to analyze epilepsy due to its capability of capturing discriminative brain activities at high time resolution. However, existing works generally analyze either EEG or iEEG seizure activity separately, where EEG analysis has been well-researched, while more and more works consider iEEG for its higher specificity at the spatial level and signal quality. Currently, most of the existing seizure prediction systems are single-modal in nature, which limits its application to a variety of clinical conditions. Therefore, a common framework to analyze seizures with both EEG and iEEG signals needs to be developed. This paper presents a new framework of multimodal seizure classification integrating the heterogeneous EEG and iEEG recordings from different subjects and sources. A specific framework, called Unified Multimodal Signal Harmonization (UMSH), is developed for harmonizing signals with varied sampling rates and recording conditions. Moreover, to handle the extremely class-imbalanced problem in the data sets, we use a Conditional Generative Adversarial Network (cGAN) based data augmentation technique to generate synthetic samples to balance the class distribution. The proposed framework uses the EEGNet-Vision Transformer (EEGNet-ViT) as the feature extraction and classification network to learn the local discriminative patterns as well as global contextual information in the multimodal seizure data. Experimental results demonstrate the effectiveness of the proposed framework for robust seizure classification, emphasizing the potential of integrated EEG-iEEG multimodal learning in advancing next-generation epilepsy monitoring and clinical decision-support systems.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.16596 [cs.LG] |
| (or arXiv:2610.16596v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.16596 arXiv-issued DOI via DataCite |
Submission history
From: Muhammad Aslam Jarwar [view email]
[v1]
Mon, 12 Oct 2026 11:18:18 UTC (517 KB)
[v2]
Wed, 14 Oct 2026 10:13:15 UTC (517 KB)
来源：arXiv：cs.LG · arxiv.org