GJFusion: A Channel-Level Correlation Construction Method for Multimodal Physiological Signal Fusion

Physiological signal based ubiquitous computing has garnered significant attention. However, the heterogeneity among multimodal physiological signals poses a critical challenge to practical applications. To traverse this heterogeneity gap, recent studies have focused on establishing inter-modality correlations. Early works only consider coarse-level correlations between the embeddings of each modality. More recent graph-based approaches incorporate prior knowledge-based correlations, although they may not be entirely accurate. In this article, we propose the Graph Joint Fusion (GJFusion) network, which leverages channel-level inter-modality correlations based on a graph joint to mitigate the heterogeneous gap. Our proposed GJFusion first represents each modality as a graph, with each vertex corresponding to a signal channel, and the edges denoting their functional connectivity. We then join each modality by constructing inter-modality correlations for each salient channel using a sampling-based matching method. Discarded channels are transformed into a virtual vertex through a lightweight pooling operation. Subsequently, the fusion network integrates intra- and inter-modality features, enabling multimodal physiological signal fusion. To validate the effectiveness of our method, we select emotional state recognition as the downstream task and conduct comprehensive experiments on two benchmark datasets. The results demonstrate that our proposed GJFusion network surpasses the latest state-of-the-art methods, achieving relative accuracy improvements of 1.22% and 0.81% on the DEAP and MAHNOB-HCI datasets, respectively. Furthermore, visualization experiments of the salient brain regions reveal the presence of interpretable knowledge within the proposed GJFusion model.