Multiple Transferable Recursive Feature Elimination Technique for Emotion Recognition Based on EEG Signals

Cai, Jiahui; Chen, Wei; Yin, Zhong

doi:10.3390/sym11050683

Cited by 20 publications

(5 citation statements)

References 41 publications

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“…The DEAP dataset was used [59] New cross-subject emotion recognition model based on the newly designed multiple transferable recursive feature elimination are developed High/low arousal, valence and dominance 32 channel data from DEAP dataset was used to validate the proposed method [60] Presented novel approach based on the multiscale information analysis (MIA) of EEG signals for distinguishing emotional.…”

Section: High/low Arousal Valence and Dominancementioning

confidence: 99%

Human Emotion Recognition: Review of Sensors and Methods

Dzedzickis

Kaklauskas

Bučinskas

2020

Sensors

364

170

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Automated emotion recognition (AEE) is an important issue in various fields of activities which use human emotional reactions as a signal for marketing, technical equipment, or human–robot interaction. This paper analyzes scientific research and technical papers for sensor use analysis, among various methods implemented or researched. This paper covers a few classes of sensors, using contactless methods as well as contact and skin-penetrating electrodes for human emotion detection and the measurement of their intensity. The results of the analysis performed in this paper present applicable methods for each type of emotion and their intensity and propose their classification. The classification of emotion sensors is presented to reveal area of application and expected outcomes from each method, as well as their limitations. This paper should be relevant for researchers using human emotion evaluation and analysis, when there is a need to choose a proper method for their purposes or to find alternative decisions. Based on the analyzed human emotion recognition sensors and methods, we developed some practical applications for humanizing the Internet of Things (IoT) and affective computing systems.

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Section: High/low Arousal Valence and Dominancementioning

confidence: 99%

Human Emotion Recognition: Review of Sensors and Methods

Dzedzickis

Kaklauskas

Bučinskas

2020

Sensors

364

170

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“…As can be observed, the classification accuracy reported by the multiclass scheme in the present work is comparable with the outcomes of other studies, even overcoming these results in some cases. [34][35][36][37] Moreover, this work is focused on the assessment of a single nonlinear metric called CSE, whereas the studies with similar classification results were based on the combination of different nonlinear indices for the detection of the emotions in the four quadrants of the valence/arousal space. [38][39][40] In addition, it is interesting to remark that the classification process presented in this study is based on the reduction of input features in the classifier by means of an SFS scheme.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Brain Functional Connectivity from Electroencephalographic Signals Under Different Emotional States

García-Martínez

Fernández-Caballero

Martínez-Rodrigo

et al. 2022

Int. J. Neur. Syst.

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The identification of the emotional states corresponding to the four quadrants of the valence/arousal space has been widely analyzed in the scientific literature by means of multiple techniques. Nevertheless, most of these methods were based on the assessment of each brain region separately, without considering the possible interactions among different areas. In order to study these interconnections, this study computes for the first time the functional connectivity metric called cross-sample entropy for the analysis of the brain synchronization in four groups of emotions from electroencephalographic signals. Outcomes reported a strong synchronization in the interconnections among central, parietal and occipital areas, while the interactions between left frontal and temporal structures with the rest of brain regions presented the lowest coordination. These differences were statistically significant for the four groups of emotions. All emotions were simultaneously classified with a 95.43% of accuracy, overcoming the results reported in previous studies. Moreover, the differences between high and low levels of valence and arousal, taking into account the state of the counterpart dimension, also provided notable findings about the degree of synchronization in the brain within different emotional conditions and the possible implications of these outcomes from a psychophysiological point of view.

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“…The emotion classification problem has been done in one of three ways: (i) identification of discrete emotions such as happiness, scared or disgust [ 24 , 27 , 34 , 40 , 41 , 42 ]; (ii) distinction between high/low arousal and high/low valence [ 2 , 3 , 4 , 19 , 29 , 31 , 43 ]; and (iii) finding the quadrant, in the valence/arousal space [ 13 , 14 , 19 , 21 , 44 , 45 ]. In the last two cases, researchers create two classifiers, one to discern between high/low valence and the other for high/low arousal.…”

Section: Background and Related Workmentioning

confidence: 99%

Predicting Exact Valence and Arousal Values from EEG

Galvão

Alarcão

Fonseca

2021

Sensors

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Recognition of emotions from physiological signals, and in particular from electroencephalography (EEG), is a field within affective computing gaining increasing relevance. Although researchers have used these signals to recognize emotions, most of them only identify a limited set of emotional states (e.g., happiness, sadness, anger, etc.) and have not attempted to predict exact values for valence and arousal, which would provide a wider range of emotional states. This paper describes our proposed model for predicting the exact values of valence and arousal in a subject-independent scenario. To create it, we studied the best features, brain waves, and machine learning models that are currently in use for emotion classification. This systematic analysis revealed that the best prediction model uses a KNN regressor (K = 1) with Manhattan distance, features from the alpha, beta and gamma bands, and the differential asymmetry from the alpha band. Results, using the DEAP, AMIGOS and DREAMER datasets, show that our model can predict valence and arousal values with a low error (MAE < 0.06, RMSE < 0.16) and a strong correlation between predicted and expected values (PCC > 0.80), and can identify four emotional classes with an accuracy of 84.4%. The findings of this work show that the features, brain waves and machine learning models, typically used in emotion classification tasks, can be used in more challenging situations, such as the prediction of exact values for valence and arousal.

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Multiple Transferable Recursive Feature Elimination Technique for Emotion Recognition Based on EEG Signals

Cited by 20 publications

References 41 publications

Human Emotion Recognition: Review of Sensors and Methods

Human Emotion Recognition: Review of Sensors and Methods

Evaluation of Brain Functional Connectivity from Electroencephalographic Signals Under Different Emotional States

Predicting Exact Valence and Arousal Values from EEG

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