Maximum Marginal Approach on EEG Signal Preprocessing for Emotion Detection
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 featur...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/21/7677 |
| _version_ | 1797549330343657472 |
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| author | Gen Li Jason J. Jung |
| author_facet | Gen Li Jason J. Jung |
| author_sort | Gen Li |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T15:13:09Z |
| format | Article |
| id | doaj.art-9beacca5ee3f45508682c3282b4c1690 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T15:13:09Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-9beacca5ee3f45508682c3282b4c16902023-11-20T19:09:28ZengMDPI AGApplied Sciences2076-34172020-10-011021767710.3390/app10217677Maximum Marginal Approach on EEG Signal Preprocessing for Emotion DetectionGen Li0Jason J. Jung1Department of Computer Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, KoreaDepartment of Computer Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, KoreaEmotion 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.https://www.mdpi.com/2076-3417/10/21/7677signal preprocessingsignal similarityemotion detection |
| spellingShingle | Gen Li Jason J. Jung Maximum Marginal Approach on EEG Signal Preprocessing for Emotion Detection Applied Sciences signal preprocessing signal similarity emotion detection |
| title | Maximum Marginal Approach on EEG Signal Preprocessing for Emotion Detection |
| title_full | Maximum Marginal Approach on EEG Signal Preprocessing for Emotion Detection |
| title_fullStr | Maximum Marginal Approach on EEG Signal Preprocessing for Emotion Detection |
| title_full_unstemmed | Maximum Marginal Approach on EEG Signal Preprocessing for Emotion Detection |
| title_short | Maximum Marginal Approach on EEG Signal Preprocessing for Emotion Detection |
| title_sort | maximum marginal approach on eeg signal preprocessing for emotion detection |
| topic | signal preprocessing signal similarity emotion detection |
| url | https://www.mdpi.com/2076-3417/10/21/7677 |
| work_keys_str_mv | AT genli maximummarginalapproachoneegsignalpreprocessingforemotiondetection AT jasonjjung maximummarginalapproachoneegsignalpreprocessingforemotiondetection |