Emotion Recognition Based on Photoplethysmogram and Electroencephalogram

Жао, Тонг; Chen, XianXiang; He, Zhengling; Tong, Kai; Fang, Zhen; Wang, Xianlong

doi:10.1109/compsac.2018.10266

Cited by 22 publications

(22 citation statements)

References 11 publications

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“…The results obtained had an accuracy of around 55% for peripheral features and 48% for EEG features. In a recent study by Tong et al [69] in 2018, five channel EEG signals and PPG signal were obtained from the DEAP database. The logistic regression algorithm and the Adaboost algorithm were used to classify arousal and valence and the results showed an accuracy of 68% and 66% for arousal and valence respectively.…”

Section: E Physiological Modalitiesmentioning

confidence: 99%

Multimodal Emotion and Sentiment Modeling From Unstructured Big Data: Challenges, Architecture, & Techniques

Seng

Ang

2019

IEEE Access

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The exponential growth of multimodal content in today's competitive business environment leads to a huge volume of unstructured data. Unstructured big data has no particular format or structure and can be in any form, such as text, audio, images, and video. In this paper, we address the challenges of emotion and sentiment modeling due to unstructured big data with different modalities. We first include an up-to-date review on emotion and sentiment modeling including the state-of-the-art techniques. We then propose a new architecture of multimodal emotion and sentiment modeling for big data. The proposed architecture consists of five essential modules: data collection module, multimodal data aggregation module, multimodal data feature extraction module, fusion and decision module, and application module. Novel feature extraction techniques called the divide-and-conquer principal component analysis (Div-ConPCA) and the divide-andconquer linear discriminant analysis (Div-ConLDA) are proposed for the multimodal data feature extraction module in the architecture. The experiments on a multicore machine architecture are performed to validate the performance of the proposed techniques.INDEX TERMS Big data, affective analytics, emotion recognition, sentiment modeling, unstructured data.

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Section: E Physiological Modalitiesmentioning

confidence: 99%

Multimodal Emotion and Sentiment Modeling From Unstructured Big Data: Challenges, Architecture, & Techniques

Seng

Ang

2019

IEEE Access

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“…Studies have been conducted to recognize emotional status using electroencephalograms (EEGs). Tong et al presented a method to classify emotional states using linear regression and the AdaBoost algorithm [22]. They achieved a 66.03% accuracy for the binary classification of low and high valence and a 68.68% accuracy for arousal classification.…”

Section: Introductionmentioning

confidence: 99%

A Photoplethysmogram Dataset for Emotional Analysis

et al. 2022

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In recent years, research on emotion classification based on physiological signals has actively attracted scholars’ attention worldwide. Several studies and experiments have been conducted to analyze human emotions based on physiological signals, including the use of electrocardiograms (ECGs), electroencephalograms (EEGs), and photoplethysmograms (PPGs). Although the achievements with ECGs and EEGs are progressive, reaching higher accuracies over 90%, the number of studies utilizing PPGs are limited and their accuracies are relatively lower than other signals. One of the difficulties in studying PPGs for emotional analysis is the lack of open datasets (there is a single dataset to the best of the authors). This study introduces a new PPG dataset for emotional analysis. A total of 72 PPGs were recorded from 18 participants while watching short video clips and analyzed in time and frequency domains. Moreover, emotional classification accuracies with the presented dataset were presented with various neural network structures. The results prove that this dataset can be used for further emotional analysis with PPGs.

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“…Another study that used EEG and HRV indexes as features is that of Tong et al [ 28 ]. They used a total of 34 physiological indexes: 9 HRV indexes from a photoplethysmogram (PPG) data and 25 EEG indexes from a five-channel EEG data.…”

Section: Introductionmentioning

confidence: 99%

Constructing an Emotion Estimation Model Based on EEG/HRV Indexes Using Feature Extraction and Feature Selection Algorithms

Suzuki

Laohakangvalvit

Matsubara

et al. 2021

Sensors

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In human emotion estimation using an electroencephalogram (EEG) and heart rate variability (HRV), there are two main issues as far as we know. The first is that measurement devices for physiological signals are expensive and not easy to wear. The second is that unnecessary physiological indexes have not been removed, which is likely to decrease the accuracy of machine learning models. In this study, we used single-channel EEG sensor and photoplethysmography (PPG) sensor, which are inexpensive and easy to wear. We collected data from 25 participants (18 males and 7 females) and used a deep learning algorithm to construct an emotion classification model based on Arousal–Valence space using several feature combinations obtained from physiological indexes selected based on our criteria including our proposed feature selection methods. We then performed accuracy verification, applying a stratified 10-fold cross-validation method to the constructed models. The results showed that model accuracies are as high as 90% to 99% by applying the features selection methods we proposed, which suggests that a small number of physiological indexes, even from inexpensive sensors, can be used to construct an accurate emotion classification model if an appropriate feature selection method is applied. Our research results contribute to the improvement of an emotion classification model with a higher accuracy, less cost, and that is less time consuming, which has the potential to be further applied to various areas of applications.

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Emotion Recognition Based on Photoplethysmogram and Electroencephalogram

Cited by 22 publications

References 11 publications

Multimodal Emotion and Sentiment Modeling From Unstructured Big Data: Challenges, Architecture, & Techniques

Multimodal Emotion and Sentiment Modeling From Unstructured Big Data: Challenges, Architecture, & Techniques

A Photoplethysmogram Dataset for Emotional Analysis

Constructing an Emotion Estimation Model Based on EEG/HRV Indexes Using Feature Extraction and Feature Selection Algorithms

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