Next Article in Journal
Raw Data-Based Motion Compensation for High-Resolution Sliding Spotlight Synthetic Aperture Radar
Next Article in Special Issue
Acquiring Respiration Rate from Photoplethysmographic Signal by Recursive Bayesian Tracking of Intrinsic Modes in Time-Frequency Spectra
Previous Article in Journal
Integrated Optical Mach-Zehnder Interferometer Based on Organic-Inorganic Hybrids for Photonics-on-a-Chip Biosensing Applications
Previous Article in Special Issue
Detection-Response Task—Uses and Limitations
Open AccessArticle

Investigating Patterns for Self-Induced Emotion Recognition from EEG Signals

by Ning Zhuang 1, Ying Zeng 1,2, Kai Yang 1, Chi Zhang 1, Li Tong 1 and Bin Yan 1,*
China National Digital Switching System Engineering and Technological Research Center, Zhengzhou 450002, China
Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
Author to whom correspondence should be addressed.
Sensors 2018, 18(3), 841;
Received: 9 February 2018 / Revised: 4 March 2018 / Accepted: 7 March 2018 / Published: 12 March 2018
(This article belongs to the Special Issue Advanced Physiological Sensing)
Most current approaches to emotion recognition are based on neural signals elicited by affective materials such as images, sounds and videos. However, the application of neural patterns in the recognition of self-induced emotions remains uninvestigated. In this study we inferred the patterns and neural signatures of self-induced emotions from electroencephalogram (EEG) signals. The EEG signals of 30 participants were recorded while they watched 18 Chinese movie clips which were intended to elicit six discrete emotions, including joy, neutrality, sadness, disgust, anger and fear. After watching each movie clip the participants were asked to self-induce emotions by recalling a specific scene from each movie. We analyzed the important features, electrode distribution and average neural patterns of different self-induced emotions. Results demonstrated that features related to high-frequency rhythm of EEG signals from electrodes distributed in the bilateral temporal, prefrontal and occipital lobes have outstanding performance in the discrimination of emotions. Moreover, the six discrete categories of self-induced emotion exhibit specific neural patterns and brain topography distributions. We achieved an average accuracy of 87.36% in the discrimination of positive from negative self-induced emotions and 54.52% in the classification of emotions into six discrete categories. Our research will help promote the development of comprehensive endogenous emotion recognition methods. View Full-Text
Keywords: self-induced emotion recognition; electroencephalogram (EEG); features; electrodes; neural patterns self-induced emotion recognition; electroencephalogram (EEG); features; electrodes; neural patterns
Show Figures

Figure 1

MDPI and ACS Style

Zhuang, N.; Zeng, Y.; Yang, K.; Zhang, C.; Tong, L.; Yan, B. Investigating Patterns for Self-Induced Emotion Recognition from EEG Signals. Sensors 2018, 18, 841.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop