Human emotion classification based on multiple physiological signals by wearable system

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine
Xin LiuJinwei Sun

Abstract

Human emotion classification is traditionally achieved using multi-channel electroencephalogram (EEG) signal, which requires costly equipment and complex classification algorithms. The experiments can be implemented in the laboratory environment equipped with high-performance computers for the online analysis; this will hinder the usability in practical applications. Considering that other physiological signals are also associated with emotional changes, this paper proposes to use a wearable, wireless system to acquire a single-channel electroencephalogram signal, respiration, electrocardiogram (ECG) signal, and body postures to explore the relationship between these signals and the human emotions. Compared with traditional emotion classification method, the presented method was able to extract a small number of key features associated with human emotions from multiple physiological signals, where the algorithm complexity was greatly reduced when incorporated with the support vector machine classification. The proposed method can support an embedded on-line analysis and may enhance the usability of emotion classification.

References

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Apr 21, 1999·Brain Research. Brain Research Reviews·W Klimesch
Apr 3, 2001·American Journal of Otolaryngology·C P WinslowK A McGuire
Feb 23, 2010·Journal of Behavior Therapy and Experimental Psychiatry·Ivar SnorrasonRagnar P Olafsson
Nov 9, 2011·Emotion·Nele DaelKlaus R Scherer
Jun 12, 2013·Biological Psychology·Sandra PaulTanja Endrass
Sep 12, 2015·Computer Methods and Programs in Biomedicine·Daniela IacovielloGiuseppe Placidi

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Citations

Jan 22, 2019·Technology and Health Care : Official Journal of the European Society for Engineering and Medicine·Dahua LiChuhan Wang
Sep 26, 2020·Sensors·Erik VanegasInmaculada Plaza
Jan 29, 2021·Frontiers in Human Neuroscience·Julia C BassoRachel Rugh

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Methods Mentioned

BETA
features extraction
feature extraction

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