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Article

Maximum Marginal Approach on EEG Signal Preprocessing for Emotion Detection

Department of Computer Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, Korea
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Appl. Sci. 2020, 10(21), 7677; https://doi.org/10.3390/app10217677
Received: 6 October 2020 / Revised: 28 October 2020 / Accepted: 28 October 2020 / Published: 30 October 2020
(This article belongs to the Special Issue Emotion-Aware Intelligent Environments)
Emotion detection is an important research issue in electroencephalogram (EEG). Signal preprocessing and feature selection are parts of feature engineering, which determines the performance of emotion detection and reduces the training time of the deep learning models. To select the efficient features for emotion detection, we propose a maximum marginal approach on EEG signal preprocessing. The approach selects the least similar segments between two EEG signals as features that can represent the difference between EEG signals caused by emotions. The method defines a signal similarity described as the distance between two EEG signals to find the features. The frequency domain of EEG is calculated by using a wavelet transform that exploits a wavelet to calculate EEG components in a different frequency. We have conducted experiments by using the selected feature from real EEG data recorded from 10 college students. The experimental results show that the proposed approach performs better than other feature selection methods by 17.9% on average in terms of accuracy. The maximum marginal approach-based models achieve better performance than the models without feature selection by 21% on average in terms of accuracy. View Full-Text
Keywords: signal preprocessing; signal similarity; emotion detection signal preprocessing; signal similarity; emotion detection
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MDPI and ACS Style

Li, G.; Jung, J.J. Maximum Marginal Approach on EEG Signal Preprocessing for Emotion Detection. Appl. Sci. 2020, 10, 7677. https://doi.org/10.3390/app10217677

AMA Style

Li G, Jung JJ. Maximum Marginal Approach on EEG Signal Preprocessing for Emotion Detection. Applied Sciences. 2020; 10(21):7677. https://doi.org/10.3390/app10217677

Chicago/Turabian Style

Li, Gen, and Jason J. Jung 2020. "Maximum Marginal Approach on EEG Signal Preprocessing for Emotion Detection" Applied Sciences 10, no. 21: 7677. https://doi.org/10.3390/app10217677

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