A Study of Physiological Signals-based Emotion Recognition Systems

Ping, Heng; Abdullah, Lili Nurliyana; Halin, Alfian Abdul; Sulaiman, Puteri Suhaiza

doi:10.24297/ijct.v11i1.1190

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…Emotional changes would be elicited using different physiological signals such as galvanic skin response (GSR) [15], electrodermal activity (EDA) [17], blood volume pressure (BVP) [18], and skin temperature (ST) [19], evoked potentials (EP) [20], electrocardiogram (ECG) [21], electromyogram (EMG) [22], and electroencephalogram (EEG) [23][24][25][26][27][28][29][30]. Clinically, EEG signals have been widely used as useful indicators of different mental states such as epilepsy, Alzheimer's disease (AD) and vascular dementia (VaD) [31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Electroencephalogram Profiles for Emotion Identification over the Brain Regions Using Spectral, Entropy and Temporal Biomarkers

Al-Qazzaz

Sabir

Ali

et al. 2019

Sensors

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Identifying emotions has become essential for comprehending varied human behavior during our daily lives. The electroencephalogram (EEG) has been adopted for eliciting information in terms of waveform distribution over the scalp. The rationale behind this work is twofold. First, it aims to propose spectral, entropy and temporal biomarkers for emotion identification. Second, it aims to integrate the spectral, entropy and temporal biomarkers as a means of developing spectro-spatial ( S S ) , entropy-spatial ( E S ) and temporo-spatial ( T S ) emotional profiles over the brain regions. The EEGs of 40 healthy volunteer students from the University of Vienna were recorded while they viewed seven brief emotional video clips. Features using spectral analysis, entropy method and temporal feature were computed. Three stages of two-way analysis of variance (ANOVA) were undertaken so as to identify the emotional biomarkers and Pearson’s correlations were employed to determine the optimal explanatory profiles for emotional detection. The results evidence that the combination of applied spectral, entropy and temporal sets of features may provide and convey reliable biomarkers for identifying S S , E S and T S profiles relating to different emotional states over the brain areas. EEG biomarkers and profiles enable more comprehensive insights into various human behavior effects as an intervention on the brain.

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Section: Introductionmentioning

confidence: 99%

Electroencephalogram Profiles for Emotion Identification over the Brain Regions Using Spectral, Entropy and Temporal Biomarkers

Al-Qazzaz

Sabir

Ali

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

“…PS-ASM between the 27 pairs of electrodes in the five bands were calculated [36]. Differential entropy (DE) equivalent to the logarithm of the energy spectrum [39,40]. DE can be defined as the entropy of continuous random variables and is used to measure its complexity, and is equivalent to the logarithm of the energy spectrum in a certain frequency band for a fixed length EEG sequence ( [41]).…”

Section: Frequency Domain Featuresmentioning

confidence: 99%

Exploring EEG Characteristics to Identify Emotional Reactions under Videogame Scenarios

et al. 2021

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In this article we present the study of electroencephalography (EEG) traits for emotion recognition process using a videogame as a stimuli tool, and considering two different kind of information related to emotions: arousal–valence self-assesses answers from participants, and game events that represented positive and negative emotional experiences under the videogame context. We performed a statistical analysis using Spearman’s correlation between the EEG traits and the emotional information. We found that EEG traits had strong correlation with arousal and valence scores; also, common EEG traits with strong correlations, belonged to the theta band of the central channels. Then, we implemented a regression algorithm with feature selection to predict arousal and valence scores using EEG traits. We achieved better result for arousal regression, than for valence regression. EEG traits selected for arousal and valence regression belonged to time domain (standard deviation, complexity, mobility, kurtosis, skewness), and frequency domain (power spectral density—PDS, and differential entropy—DE from theta, alpha, beta, gamma, and all EEG frequency spectrum). Addressing game events, we found that EEG traits related with the theta, alpha and beta band had strong correlations. In addition, distinctive event-related potentials where identified in the presence of both types of game events. Finally, we implemented a classification algorithm to discriminate between positive and negative events using EEG traits to identify emotional information. We obtained good classification performance using only two traits related with frequency domain on the theta band and on the full EEG spectrum.

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“…Human physiological information is also an important way of emotion recognition. In literature [7], how to construct emotion recognition system and its main components with physiological signals is introduced. The related concepts and existing problems are discussed.…”

Section: Related Researchmentioning

confidence: 99%

Intelligent Emotion Detection Method Based on Deep Learning in Medical and Health Data

Zou

et al. 2020

IEEE Access

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Emotional abnormality may be brought out by physiological fatigue. In order to solve the problem, an emotion detection method based on deep learning in medical and health data is proposed in this paper. First of all, the related content of emotional fatigue is studied. The concept and the classification of emotional fatigue are introduced. Then, a multi-modal data emotional fatigue detection system is designed. In the system, multi-channel convolutional aotoencoder neural network is used to extract electrocardiograms (ECG) data features and emotional text features for emotional fatigue detection. Secondly, the network structure of learning ECG features by multi-channel convolutional aotoencoder model is introduced in detail. And the network structure of learning emotional text features by convolutional aotoencoder model is also described in detail. Finally, multi-modal data features are combined for emotional detection. It is shown by the experimental results that the proposed model has an average accuracy of more than 85% in predicting emotional fatigue. INDEX TERMS Emotion detection model, multi-channel convolutional aotoencoder (MCAE), medical health, deep learning, emotional text features, intelligent data analysis.

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A Study of Physiological Signals-based Emotion Recognition Systems

Cited by 7 publications

References 23 publications

Electroencephalogram Profiles for Emotion Identification over the Brain Regions Using Spectral, Entropy and Temporal Biomarkers

Electroencephalogram Profiles for Emotion Identification over the Brain Regions Using Spectral, Entropy and Temporal Biomarkers

Exploring EEG Characteristics to Identify Emotional Reactions under Videogame Scenarios

Intelligent Emotion Detection Method Based on Deep Learning in Medical and Health Data

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