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Applications of multimodal neuroimaging techniques, including electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have gained prominence in recent years, and they are widely practiced in brain–computer interface (BCI) and neuro-pathological diagnosis applications. Most existing approaches assume observations are independent and identically distributed (i.i.d.), as shown in the top section of the right figure, yet ignore the difference among subjects. It has been challenging to model subject groups to maintain topological information (e.g., patient graphs) while fusing BCI signals for discriminant feature learning. In this article, we introduce a topology-aware graph-based multimodal fusion (TaGMF) framework to classify amyotrophic lateral sclerosis (ALS) and healthy subjects, illustrated in the lower section of the right image. Our framework is built on graph neural networks (GNNs) but with two unique contributions. First, a novel topology-aware graph (TaG) is proposed to model subject groups by considering: 1) intersubject; 2) intrasubject; and 3) intergroup relations. Second, the learned representation of EEG and fNIRS signals of each subject allows for explorations of different fusion strategies along with the TaGMF optimizations. Our analysis demonstrates the effectiveness of our graph-based fusion approach in multimodal classification by achieving a 22.6% performance improvement over classical approaches.