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Sensors 2018, 18(8), 2739; https://doi.org/10.3390/s18082739

EEG-Based Emotion Recognition Using Quadratic Time-Frequency Distribution

1
School of Electrical Engineering and Information Technology, German Jordanian University, Amman 11180, Jordan
2
Faculty of Engineering, University of Freiburg, Freiburg 79098, Germany
*
Author to whom correspondence should be addressed.
Received: 25 June 2018 / Revised: 12 August 2018 / Accepted: 17 August 2018 / Published: 20 August 2018
(This article belongs to the Special Issue Sensor Signal and Information Processing)
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Abstract

Accurate recognition and understating of human emotions is an essential skill that can improve the collaboration between humans and machines. In this vein, electroencephalogram (EEG)-based emotion recognition is considered an active research field with challenging issues regarding the analyses of the nonstationary EEG signals and the extraction of salient features that can be used to achieve accurate emotion recognition. In this paper, an EEG-based emotion recognition approach with a novel time-frequency feature extraction technique is presented. In particular, a quadratic time-frequency distribution (QTFD) is employed to construct a high resolution time-frequency representation of the EEG signals and capture the spectral variations of the EEG signals over time. To reduce the dimensionality of the constructed QTFD-based representation, a set of 13 time- and frequency-domain features is extended to the joint time-frequency-domain and employed to quantify the QTFD-based time-frequency representation of the EEG signals. Moreover, to describe different emotion classes, we have utilized the 2D arousal-valence plane to develop four emotion labeling schemes of the EEG signals, such that each emotion labeling scheme defines a set of emotion classes. The extracted time-frequency features are used to construct a set of subject-specific support vector machine classifiers to classify the EEG signals of each subject into the different emotion classes that are defined using each of the four emotion labeling schemes. The performance of the proposed approach is evaluated using a publicly available EEG dataset, namely the DEAPdataset. Moreover, we design three performance evaluation analyses, namely the channel-based analysis, feature-based analysis and neutral class exclusion analysis, to quantify the effects of utilizing different groups of EEG channels that cover various regions in the brain, reducing the dimensionality of the extracted time-frequency features and excluding the EEG signals that correspond to the neutral class, on the capability of the proposed approach to discriminate between different emotion classes. The results reported in the current study demonstrate the efficacy of the proposed QTFD-based approach in recognizing different emotion classes. In particular, the average classification accuracies obtained in differentiating between the various emotion classes defined using each of the four emotion labeling schemes are within the range of 73.8 % 86.2 % . Moreover, the emotion classification accuracies achieved by our proposed approach are higher than the results reported in several existing state-of-the-art EEG-based emotion recognition studies. View Full-Text
Keywords: emotion recognition; quadratic time-frequency distributions; electroencephalography; time-frequency features; support vector machines; 2D arousal-valence plane emotion recognition; quadratic time-frequency distributions; electroencephalography; time-frequency features; support vector machines; 2D arousal-valence plane
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Alazrai, R.; Homoud, R.; Alwanni, H.; Daoud, M.I. EEG-Based Emotion Recognition Using Quadratic Time-Frequency Distribution. Sensors 2018, 18, 2739.

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