Emotion recognition with deep learning using GAMEEMO data set

Emotion recognition is actively used in brain–computer interface, health care, security, e-commerce, education and entertainment applications to increase and control human–machine interaction. Therefore, emotions affect people's lives and decision-making mechanisms throughout their lives. Howev...

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Bibliographic Details
Published in:Electronics letters Vol. 56; no. 25; pp. 1364 - 1367
Main Authors: Alakus, T. B, Turkoglu, I
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 10.12.2020
Subjects:
abstract concept
brain–computer interface
decision making
deep learning
education
EEG signals
electroencephalography
electroencephalography signals
emotion analysis
emotion recognition
entertainment applications
entropy
GAMEEMO data set
health care
human computer interaction
human–machine interaction
internal factors
learning (artificial intelligence)
medical signal processing
negative emotions
positive emotions
Special Issue: Current Trends in Cognitive Science and Brain Computing Research and Applications
electroencephalography
education
electroencephalography signals
negative emotions
GAMEEMO data set
brain–computer interface
emotion recognition
abstract concept
medical signal processing
internal factors
deep learning
entertainment applications
entropy
emotion analysis
decision making
human computer interaction
human–machine interaction
EEG signals
learning (artificial intelligence)
positive emotions
health care
ISSN:0013-5194, 1350-911X, 1350-911X
Online Access:Get full text
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Summary:Emotion recognition is actively used in brain–computer interface, health care, security, e-commerce, education and entertainment applications to increase and control human–machine interaction. Therefore, emotions affect people's lives and decision-making mechanisms throughout their lives. However, the fact that emotions vary from person to person, being an abstract concept and being dependent on internal and external factors makes the studies in this field difficult. In recent years, studies based on electroencephalography (EEG) signals, which perform emotion analysis in a more robust and reliable way, have gained momentum. In this article, emotion analysis based on EEG signals was performed to predict positive and negative emotions. The study consists of four parts. In the first part, EEG signals were obtained from the GAMEEMO data set. In the second stage, the spectral entropy values of the EEG signals of all channels were calculated and these values were classified by the bidirectional long-short term memory architecture in the third stage. In the last stage, the performance of the deep-learning architecture was evaluated with accuracy, sensitivity, specificity and receiver operating characteristic (ROC) curve. With the proposed method, an accuracy of 76.91% and a ROC value of 90% were obtained.
ISSN:0013-5194
1350-911X
1350-911X
DOI:10.1049/el.2020.2460