发表机构
Florida Atlantic University（佛罗里达大西洋大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出多视图图学习框架M-LINKX，通过构建多视图功能连接图并融合表示，在CAUEEG和AHEAP两类EEG数据集的痴呆分类任务中取得最优性能。
AI 中文摘要
脑电图（EEG）是一种用于检测认知疾病的非侵入式且成本相对较低的程序，可测量脑电活动。基于EEG对痴呆相关病症（包括阿尔茨海默病（AD）、轻度认知障碍（MCI）和额颞叶痴呆（FTD））进行分类仍然具有挑战性，因为EEG信号存在噪声、非平稳性，且会随受试者不同而变化。分段式学习提供了一种实用方法，可将长EEG记录转换为固定长度输入以进行建模。对于每个分段，可通过利用每个通道（即电极）内的信号以及EEG通道间的交互来探索判别信息。本文提出M-LINKX，一种用于基于EEG的痴呆分类的多视图图学习框架。对于每个分段，我们提取通道级节点特征并构建多个功能连接（FC）图视图，每个视图分别由特定的连接度量、频带和拓扑滤波器组合定义。M-LINKX不依赖于在构建的图上进行消息传递，而是采用简单设计对节点特征和基于邻接的连接表示进行建模。图视图表示通过可训练的全局视图权重进行融合，受试者级预测通过对分段级概率取平均得到。在两个具有不同诊断组的三类EEG数据集（CAUEEG（健康对照/轻度认知障碍/痴呆）和AHEAP（健康对照/阿尔茨海默病/额颞叶痴呆））上进行的实验表明，在主要实验设置下，M-LINKX取得了最佳的受试者级性能。我们的研究表明，当多视图功能连接与合适的图学习架构结合时，可改善基于EEG的痴呆分类。代码和数据可在该https URL获取。
英文摘要
Electroencephalogram (EEG) is a non-invasive and relatively low-cost procedure that measures brain electricity for the detection of cognitive diseases. EEG-based classification of dementia-related conditions, including Alzheimer's disease (AD), mild cognitive impairment (MCI), and frontotemporal dementia (FTD), remains challenging because EEG signals are noisy, non-stationary, and vary across subjects. Segment-based learning provides a practical way to model long EEG recordings by converting them into fixed-length inputs. For each segment, discriminative information may be explored by using signals within each channel (i.e. electrode), as well as interactions between EEG channels. In this paper, we propose M-LINKX, a multi-view graph learning framework for EEG-based dementia classification. For each segment, we extract channel-level node features and construct multiple functional-connectivity (FC) graph views, where each view is defined by a specific combination of connectivity metric, frequency band, and topology filter, respectively. Instead of relying on message passing over the constructed graphs, M-LINKX follows a simple design in modeling node features and adjacency-based connectivity representations. The graph-view representations are fused using global trainable view weights, and subject-level prediction is obtained by averaging segment-level probabilities. Experiments on two three-class EEG datasets with different diagnostic groups, CAUEEG (HC/MCI/Dementia) and AHEAP (HC/AD/FTD), show that M-LINKX achieves the best subject-level performance under the main experimental settings. Our study suggests that multi-view functional connectivity can improve EEG-based dementia classification when integrated with an appropriate graph-learning architecture. Code and data are available at https://github.com/anphantt/MLINKX.
CommentsAccepted at the 25th IEEE International Conference on Machine Learning and Applications (ICMLA 2026). 8 pages, 5 figures
