Multimodal Affective States Recognition Based on Multiscale CNNs and Biologically Inspired Decision Fusion Model

doi:10.1109/TAFFC.2021.3093923

CORC > 自动化研究所 > 中国科学院自动化研究所 > 多模态人工智能系统全国重点实验室

	Multimodal Affective States Recognition Based on Multiscale CNNs and Biologically Inspired Decision Fusion Model
	Zhao, Yuxuan 1,2; Cao, Xinyan 2; Lin, Jinlong 2; Yu, Dunshan 2; Cao, Xixin 2
刊名	IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
	2023-04-01
卷号	14 期号:2 页码:1391-1403
关键词	Physiology Brain modeling Feature extraction Electroencephalography Biological system modeling Convolution Reliability Multimodal affective states recognition convolutional neural network decision fusion model physiological signals
ISSN号	1949-3045
DOI	10.1109/TAFFC.2021.3093923
通讯作者	Zhao, Yuxuan(yuxuan.zhao@ia.ac.cn)
英文摘要	There has been an encouraging progress in the affective states recognition models based on the single-modality signals as electroencephalogram (EEG) signals or peripheral physiological signals in recent years. However, multimodal physiological signals-based affective states recognition methods have not been thoroughly exploited yet. Here we propose Multiscale Convolutional Neural Networks (Multiscale CNNs) and a biologically inspired decision fusion model for multimodal affective states recognition. First, the raw signals are pre-processed with baseline signals. Then, the High Scale CNN and Low Scale CNN in Multiscale CNNs are utilized to predict the probability of affective states output for EEG and each peripheral physiological signal respectively. Finally, the fusion model calculates the reliability of each single-modality signals by the euclidean distance between various class labels and the classification probability from Multiscale CNNs, and the decision is made by the more reliable modality information while other modalities information is retained. We use this model to classify four affective states from the arousal valence plane in the DEAP and AMIGOS dataset. The results show that the fusion model improves the accuracy of affective states recognition significantly compared with the result on single-modality signals, and the recognition accuracy of the fusion result achieve 98.52 and 99.89 percent in the DEAP and AMIGOS dataset respectively.
WOS关键词	EMOTION RECOGNITION ; PHYSIOLOGICAL SIGNALS ; CUE INTEGRATION ; CLASSIFICATION ; INFORMATION ; TEXTURE ; BRAIN
WOS研究方向	Computer Science
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:001000299100040
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/53477]
专题	多模态人工智能系统全国重点实验室
通讯作者	Zhao, Yuxuan
作者单位	1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Peking Univ, Sch Software & Microelect, Beijing 100871, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Yuxuan,Cao, Xinyan,Lin, Jinlong,et al. Multimodal Affective States Recognition Based on Multiscale CNNs and Biologically Inspired Decision Fusion Model[J]. IEEE TRANSACTIONS ON AFFECTIVE COMPUTING,2023,14(2):1391-1403.
APA	Zhao, Yuxuan,Cao, Xinyan,Lin, Jinlong,Yu, Dunshan,&Cao, Xixin.(2023).Multimodal Affective States Recognition Based on Multiscale CNNs and Biologically Inspired Decision Fusion Model.IEEE TRANSACTIONS ON AFFECTIVE COMPUTING,14(2),1391-1403.
MLA	Zhao, Yuxuan,et al."Multimodal Affective States Recognition Based on Multiscale CNNs and Biologically Inspired Decision Fusion Model".IEEE TRANSACTIONS ON AFFECTIVE COMPUTING 14.2(2023):1391-1403.