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

doi:10.1145/3617503

	GJFusion: A Channel-Level Correlation Construction Method for Multimodal Physiological Signal Fusion
	Huang, Wuliang; Chen, Yiqiang; Jiang, Xinlong; Zhang, Teng; Chen, Qian
	2024-02-01
发表期刊	ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
ISSN	1551-6857
卷号	20 期号:2 页码:23
摘要	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.
关键词	Multimodal physiological signal graph neural network emotion state recognition ubiquitous computing
DOI	10.1145/3617503
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Plan of China[2021YFC2501202] ; Beijing Municipal Science & Technology Commission[Z221100002722009] ; Hunan Provincial Natural Science Foundation of China[2023JJ70034] ; Youth Innovation Promotion Association CAS
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods
WOS记录号	WOS:001092595800030
出版者	ASSOC COMPUTING MACHINERY
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/38102
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Chen, Yiqiang; Jiang, Xinlong
作者单位	Chinese Acad Sci, Inst Comp Technol, 6 Kexueyuan South Rd Zhongguancun, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Huang, Wuliang,Chen, Yiqiang,Jiang, Xinlong,et al. GJFusion: A Channel-Level Correlation Construction Method for Multimodal Physiological Signal Fusion[J]. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,2024,20(2):23.
APA	Huang, Wuliang,Chen, Yiqiang,Jiang, Xinlong,Zhang, Teng,&Chen, Qian.(2024).GJFusion: A Channel-Level Correlation Construction Method for Multimodal Physiological Signal Fusion.ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,20(2),23.
MLA	Huang, Wuliang,et al."GJFusion: A Channel-Level Correlation Construction Method for Multimodal Physiological Signal Fusion".ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 20.2(2024):23.