Electroencephalogram Emotion Recognition Based on Dispersion Entropy Feature Extraction Using Random Oversampling Imbalanced Data Processing

Ding, Xuewen; Liu, Zhentao; Li, Danyun; He, Yong; Wu, Min

doi:10.1109/tcds.2021.3074811

Cited by 23 publications

(6 citation statements)

References 65 publications

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“…On the other hand, the oversampling process on imbalanced data using the Radius SMOTE method produces higher accuracy than the Random Oversampling method [36]. In general, the application of the Differential Entropy, Radius SMOTE, 3D Cube, and Convolutional Neural Network for feature extraction, imbalanced data, representation, and classification in this study led to higher accuracy compared to some of the previous studies [36,[46][47][48]52].…”

Section: Figure 13 Comparison Of Valence Accuracy For Using Decision ...mentioning

confidence: 54%

“…Although the emotion classification process has been improved using the Capsule Network method and the combination of Convolutional Neural Network and Long Short Term Memory methods; however, the accuracy is achieved still lower than this research proposal [47,48,52]. On the other hand, the oversampling process on imbalanced data using the Radius SMOTE method produces higher accuracy than the Random Oversampling method [36]. In general, the application of the Differential Entropy, Radius SMOTE, 3D Cube, and Convolutional Neural Network for feature extraction, imbalanced data, representation, and classification in this study led to higher accuracy compared to some of the previous studies [36,[46][47][48]52].…”

Section: Figure 13 Comparison Of Valence Accuracy For Using Decision ...mentioning

confidence: 68%

“…There are two oversampling strategies, namely random and synthetic. Random oversampling is a non-heuristic method used to add data to a small portion of minor classes [33,35,36]. Ding et al (2021) [36] conducted an oversampling study using the Random Oversampling method for the DEAP dataset.…”

Section: -Literature Reviewmentioning

confidence: 99%

“…Random oversampling is a non-heuristic method used to add data to a small portion of minor classes [33,35,36]. Ding et al (2021) [36] conducted an oversampling study using the Random Oversampling method for the DEAP dataset. This method increased the accuracy of the recognition of arousal and valence emotions.…”

Section: -Literature Reviewmentioning

confidence: 99%

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Oversampling Approach Using Radius-SMOTE for Imbalance Electroencephalography Datasets

2022

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Several studies related to emotion recognition based on Electroencephalogram signals have been carried out in feature extraction, feature representation, and classification. However, emotion recognition is strongly influenced by the distribution or balance of Electroencephalogram data. On the other hand, the limited data obtained significantly affects the imbalance condition of the resulting Electroencephalogram signal data. It has an impact on the low accuracy of emotion recognition. Therefore, based on these problems, the contribution of this research is to propose the Radius SMOTE method to overcome the imbalance of the DEAP dataset in the emotion recognition process. In addition to the EEG data oversampling process, there are several vital processes in emotion recognition based on EEG signals, including the feature extraction process and the emotion classification process. This study uses the Differential Entropy (DE) method in the EEG feature extraction process. The classification process in this study compares two classification methods, namely the Decision Tree method and the Convolutional Neural Network method. Based on the classification process using the Decision Tree method, the application of oversampling with the Radius SMOTE method resulted in the accuracy of recognizing arousal and valence emotions of 78.78% and 75.14%, respectively. Meanwhile, the Convolutional Neural Network method can accurately identify the arousal and valence emotions of 82.10% and 78.99%, respectively. Doi: 10.28991/ESJ-2022-06-02-013 Full Text: PDF

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Section: Figure 13 Comparison Of Valence Accuracy For Using Decision ...mentioning

confidence: 54%

Section: Figure 13 Comparison Of Valence Accuracy For Using Decision ...mentioning

confidence: 68%

Section: -Literature Reviewmentioning

confidence: 99%

Section: -Literature Reviewmentioning

confidence: 99%

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Oversampling Approach Using Radius-SMOTE for Imbalance Electroencephalography Datasets

2022

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“…For all subjects, there are 1512 and 1008 data for positive and negative emotions, respectively. Ding et al performed random over-sampling on EEG imblanced data in emotion recognition [ 59 ]. Lu et al reported higher accuracy in random over-sampling than other method, random under-sampling, and cluster centroid [ 60 ].…”

Section: Discussionmentioning

confidence: 99%

Emotion recognition while applying cosmetic cream using deep learning from EEG data; cross-subject analysis

Kim

Hwang²,

Son³

et al. 2022

PLoS ONE

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We report a deep learning-based emotion recognition method using EEG data collected while applying cosmetic creams. Four creams with different textures were randomly applied, and they were divided into two classes, “like (positive)” and “dislike (negative)”, according to the preference score given by the subject. We extracted frequency features using well-known frequency bands, i.e., alpha, beta and low and high gamma bands, and then we created a matrix including frequency and spatial information of the EEG data. We developed seven CNN-based models: (1) inception-like CNN with four-band merged input, (2) stacked CNN with four-band merged input, (3) stacked CNN with four-band parallel input, and stacked CNN with single-band input of (4) alpha, (5) beta, (6) low gamma, and (7) high gamma. The models were evaluated by the Leave-One-Subject-Out Cross-Validation method. In like/dislike two-class classification, the average accuracies of all subjects were 73.2%, 75.4%, 73.9%, 68.8%, 68.0%, 70.7%, and 69.7%, respectively. We found that the classification performance is higher when using multi-band features than when using single-band feature. This is the first study to apply a CNN-based deep learning method based on EEG data to evaluate preference for cosmetic creams.

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Effective affective EEG-based indicators in emotion-evoking VR environments: an evidence from machine learning

Castiblanco Jimenez,

Olivetti,

Vezzetti

et al. 2024

Neural Comput & Applic

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This study investigates the use of electroencephalography (EEG) to characterize emotions and provides insights into the consistency between self-reported and machine learning outcomes. Thirty participants engaged in five virtual reality environments designed to elicit specific emotions, while their brain activity was recorded. The participants self-assessed their ground truth emotional state in terms of Arousal and Valence through a Self-Assessment Manikin. Gradient Boosted Decision Tree was adopted as a classification algorithm to test the EEG feasibility in the characterization of emotional states. Distinctive patterns of neural activation corresponding to different levels of Valence and Arousal emerged, and a noteworthy correspondence between the outcomes of the self-assessments and the classifier suggested that EEG-based affective indicators can be successfully applied in emotional characterization, shedding light on the possibility of using them as ground truth measurements. These findings provide compelling evidence for the validity of EEG as a tool for emotion characterization and its contribution to a better understanding of emotional activation.

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Electroencephalogram Emotion Recognition Based on Dispersion Entropy Feature Extraction Using Random Oversampling Imbalanced Data Processing

Cited by 23 publications

References 65 publications

Oversampling Approach Using Radius-SMOTE for Imbalance Electroencephalography Datasets

Oversampling Approach Using Radius-SMOTE for Imbalance Electroencephalography Datasets

Emotion recognition while applying cosmetic cream using deep learning from EEG data; cross-subject analysis

Effective affective EEG-based indicators in emotion-evoking VR environments: an evidence from machine learning

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