AsEmo: Automatic Approach for EEG-Based Multiple Emotional State Identification

An electroencephalogram (EEG) is the most extensively used physiological signal in emotion recognition using biometric data. However, these EEG data are difficult to analyze, because of their anomalous characteristic where statistical elements vary according to time as well as spatial-temporal corre...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2021-05, Vol.25 (5), p.1508-1518
Main Authors: Kim, Sun-Hee, Yang, Hyung-Jeong, Nguyen, Ngoc Anh Thi, Lee, Seong-Whan
Format: Article
Language:English
Subjects:
Classification
Covariance matrices
Data analysis
Data mining
EEG
Electroencephalogram
Electroencephalography
Emotion recognition
Emotional factors
Emotions
explained variance ratio
Feature extraction
Matrix decomposition
multi-class common spatial pattern
Real time
Real-time systems
Spatial filtering
subject-independent
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Summary:An electroencephalogram (EEG) is the most extensively used physiological signal in emotion recognition using biometric data. However, these EEG data are difficult to analyze, because of their anomalous characteristic where statistical elements vary according to time as well as spatial-temporal correlations. Therefore, new methods that can clearly distinguish emotional states in EEG data are required. In this paper, we propose a new emotion recognition method, named AsEmo. The proposed method extracts effective features boosting classification performance on various emotional states from multi-class EEG data. AsEmo Automatically determines the number of spatial filters needed to extract significant features using the explained variance ratio (EVR) and employs a Subject-independent method for real-time processing of Emotion EEG data. The advantages of this method are as follows: (a) it automatically determines the spatial filter coefficients distinguishing emotional states and extracts the best features; (b) it is very robust for real-time analysis of new data using a subject-independent technique that considers subject sets, and not a specific subject; (c) it can be easily applied to both binary-class and multi-class data. Experimental results on real-world EEG emotion recognition tasks demonstrate that AsEmo outperforms other state-of-the-art methods with a 2-8% improvement in terms of classification accuracy.
ISSN:2168-2194
2168-2208
DOI:10.1109/JBHI.2020.3032678