Abstract
Background: Collaboration between humans and computers has become pervasive and ubiquitous, however current computer systems are limited in that they fail to address the emotional component. An accurate understanding of human emotions is necessary for these computers to trigger proper feedback. Among multiple emotional channels, physiological signals are synchronous with emotional responses; therefore, analyzing physiological changes is a recognized way to estimate human emotions. In this paper, a three-stage decision method is proposed to recognize four emotions based on physiological signals in the multi-subject context. Emotion detection is achieved by using a stage-divided strategy in which each stage deals with a fine-grained goal.
Methods: The decision method consists of three stages. During the training process, the initial stage transforms mixed training subjects to separate groups, thus eliminating the effect of individual differences. The second stage categorizes four emotions into two emotion pools in order to reduce recognition complexity. The third stage trains a classifier based on emotions in each emotion pool. During the testing process, a test case or test trial will be initially classified to a group followed by classification into an emotion pool in the second stage. An emotion will be assigned to the test trial in the final stage. In this paper we consider two different ways of allocating four emotions into two emotion pools. A comparative analysis is also carried out between the proposal and other methods.
Results: An average recognition accuracy of 77.57% was achieved on the recognition of four emotions with the best accuracy of 86.67% to recognize the positive and excited emotion. Using differing ways of allocating four emotions into two emotion pools, we found there is a difference in the effectiveness of a classifier on learning each emotion. When compared to other methods, the proposed method demonstrates a significant improvement in recognizing four emotions in the multi-subject context.
Conclusions: The proposed three-stage decision method solves a crucial issue which is 'individual differences' in multi-subject emotion recognition and overcomes the suboptimal performance with respect to direct classification of multiple emotions. Our study supports the observation that the proposed method represents a promising methodology for recognizing multiple emotions in the multi-subject context.
Document Type
Article
Publication Date
12-20-2017
Digital Object Identifier (DOI)
https://doi.org/10.1186/s12911-017-0562-x
Funding Information
The publication costs were funded by the National Basic Research Program of China (973 Program) (No.2014CB744600), the National Natural Science Foundation of China (No.61210010, No.61632014 and No.61402211).
Related Content
The data used during this study are available from DEAPdataset http://www.eecs.qmul.ac.uk/mmv/datasets/deap/index.html but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available with permission of DEAPdataset.
Repository Citation
Chen, Jing; Hu, Bin; Wang, Yue; Moore, Philip; Dai, Yongqiang; Feng, Lei; and Ding, Zhijie, "Subject-Independent Emotion Recognition Based on Physiological Signals: A Three-Stage Decision Method" (2017). Biomedical Engineering Faculty Publications. 25.
https://uknowledge.uky.edu/cbme_facpub/25
Notes/Citation Information
Published in BMC Medical Informatics and Decision Making, v. 17, suppl 3, 167, p. 45-66.
© The Author(s) 2017
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