EEG-Based Emotion Recognition Using Logistic Regression with Gaussian Kernel and Laplacian Prior and Investigation of Critical Frequency Bands

Emotion plays a nuclear part in human attention, decision-making, and communication. Electroencephalogram (EEG)-based emotion recognition has developed a lot due to the application of Brain-Computer Interface (BCI) and its effectiveness compared to body expressions and other physiological signals. Despite significant progress in affective computing, emotion recognition is still an unexplored problem. This paper introduced Logistic Regression (LR) with Gaussian kernel and Laplacian prior for EEG-based emotion recognition. The Gaussian kernel enhances the EEG data separability in the transformed space. The Laplacian prior promotes the sparsity of learned LR regressors to avoid over-specification. The LR regressors are optimized using the logistic regression via variable splitting and augmented Lagrangian (LORSAL) algorithm. For simplicity, the introduced method is noted as LORSAL. Experiments were conducted on the dataset for emotion analysis using EEG, physiological and video signals (DEAP). Various spectral features and features by combining electrodes (power spectral density (PSD), differential entropy (DE), differential asymmetry (DASM), rational asymmetry (RASM), and differential caudality (DCAU)) were extracted from different frequency bands (Delta, Theta, Alpha, Beta, Gamma, and Total) with EEG signals. The Naive Bayes (NB), support vector machine (SVM), linear LR with L1-regularization (LR_L1), linear LR with L2-regularization (LR_L2) were used for comparison in the binary emotion classification for valence and arousal. LORSAL obtained the best classification accuracies (77.17% and 77.03% for valence and arousal, respectively) on the DE features extracted from total frequency bands. This paper also investigates the critical frequency bands in emotion recognition. The experimental results showed the superiority of Gamma and Beta bands in classifying emotions. It was presented that DE was the most informative and DASM and DCAU had lower computational complexity with relatively ideal accuracies. An analysis of LORSAL and the recently deep learning (DL) methods is included in the discussion. Conclusions and future work are presented in the final section.

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Affective Computing on Machine Learning-Based Emotion Recognition Using a Self-Made EEG Device

Sensors ◽

10.3390/s21155135 ◽

2021 ◽

Vol 21 (15) ◽

pp. 5135

Author(s):

Ngoc-Dau Mai ◽

Boon-Giin Lee ◽

Wan-Young Chung

Keyword(s):

Machine Learning ◽

Emotion Recognition ◽

Temporal Lobe ◽

Affective Computing ◽

Electrode Placement ◽

Entropy Measure ◽

Support Vector ◽

Emotion Classification ◽

Eeg Signals ◽

Computing Method

In this research, we develop an affective computing method based on machine learning for emotion recognition using a wireless protocol and a wearable electroencephalography (EEG) custom-designed device. The system collects EEG signals using an eight-electrode placement on the scalp; two of these electrodes were placed in the frontal lobe, and the other six electrodes were placed in the temporal lobe. We performed experiments on eight subjects while they watched emotive videos. Six entropy measures were employed for extracting suitable features from the EEG signals. Next, we evaluated our proposed models using three popular classifiers: a support vector machine (SVM), multi-layer perceptron (MLP), and one-dimensional convolutional neural network (1D-CNN) for emotion classification; both subject-dependent and subject-independent strategies were used. Our experiment results showed that the highest average accuracies achieved in the subject-dependent and subject-independent cases were 85.81% and 78.52%, respectively; these accuracies were achieved using a combination of the sample entropy measure and 1D-CNN. Moreover, our study investigates the T8 position (above the right ear) in the temporal lobe as the most critical channel among the proposed measurement positions for emotion classification through electrode selection. Our results prove the feasibility and efficiency of our proposed EEG-based affective computing method for emotion recognition in real-world applications.

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A Machine Learning Approach to EEG-based Prediction of Human Affective States Using Recursive Feature Elimination Method

MATEC Web of Conferences ◽

10.1051/matecconf/202133504001 ◽

2021 ◽

Vol 335 ◽

pp. 04001

Author(s):

Didar Dadebayev ◽

Goh Wei Wei ◽

Tan Ee Xion

Keyword(s):

Feature Selection ◽

Fractal Dimension ◽

Emotion Recognition ◽

Affective Computing ◽

Feature Selection Method ◽

Recursive Feature Elimination ◽

Support Vector ◽

Emotional States ◽

Affective States ◽

Emotion Classification

Emotion recognition, as a branch of affective computing, has attracted great attention in the last decades as it can enable more natural brain-computer interface systems. Electroencephalography (EEG) has proven to be an effective modality for emotion recognition, with which user affective states can be tracked and recorded, especially for primitive emotional events such as arousal and valence. Although brain signals have been shown to correlate with emotional states, the effectiveness of proposed models is somewhat limited. The challenge is improving accuracy, while appropriate extraction of valuable features might be a key to success. This study proposes a framework based on incorporating fractal dimension features and recursive feature elimination approach to enhance the accuracy of EEG-based emotion recognition. The fractal dimension and spectrum-based features to be extracted and used for more accurate emotional state recognition. Recursive Feature Elimination will be used as a feature selection method, whereas the classification of emotions will be performed by the Support Vector Machine (SVM) algorithm. The proposed framework will be tested with a widely used public database, and results are expected to demonstrate higher accuracy and robustness compared to other studies. The contributions of this study are primarily about the improvement of the EEG-based emotion classification accuracy. There is a potential restriction of how generic the results can be as different EEG dataset might yield different results for the same framework. Therefore, experimenting with different EEG dataset and testing alternative feature selection schemes can be very interesting for future work.

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Multi-Feature Input Deep Forest for EEG-Based Emotion Recognition

Frontiers in Neurorobotics ◽

10.3389/fnbot.2020.617531 ◽

2021 ◽

Vol 14 ◽

Author(s):

Yinfeng Fang ◽

Haiyang Yang ◽

Xuguang Zhang ◽

Han Liu ◽

Bo Tao

Keyword(s):

Emotion Recognition ◽

Affective Computing ◽

Rapid Development ◽

Support Vector ◽

Emotion Classification ◽

Eeg Signals ◽

K Nearest Neighbors ◽

Power Spectral ◽

Deep Forest ◽

Human Emotions

Due to the rapid development of human–computer interaction, affective computing has attracted more and more attention in recent years. In emotion recognition, Electroencephalogram (EEG) signals are easier to be recorded than other physiological experiments and are not easily camouflaged. Because of the high dimensional nature of EEG data and the diversity of human emotions, it is difficult to extract effective EEG features and recognize the emotion patterns. This paper proposes a multi-feature deep forest (MFDF) model to identify human emotions. The EEG signals are firstly divided into several EEG frequency bands and then extract the power spectral density (PSD) and differential entropy (DE) from each frequency band and the original signal as features. A five-class emotion model is used to mark five emotions, including neutral, angry, sad, happy, and pleasant. With either original features or dimension reduced features as input, the deep forest is constructed to classify the five emotions. These experiments are conducted on a public dataset for emotion analysis using physiological signals (DEAP). The experimental results are compared with traditional classifiers, including K Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Machine (SVM). The MFDF achieves the average recognition accuracy of 71.05%, which is 3.40%, 8.54%, and 19.53% higher than RF, KNN, and SVM, respectively. Besides, the accuracies with the input of features after dimension reduction and raw EEG signal are only 51.30 and 26.71%, respectively. The result of this study shows that the method can effectively contribute to EEG-based emotion classification tasks.

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Emotions Recognition and Signal Classification

International Journal of Synthetic Emotions ◽

10.4018/ijse.2020010101 ◽

2020 ◽

Vol 11 (1) ◽

pp. 1-16

Author(s):

Rana Seif Fathalla ◽

Wafa Saad Alshehri

Keyword(s):

Emotion Recognition ◽

Affective Computing ◽

Support Vector ◽

Decomposition Technique ◽

Emotion Classification ◽

Eeg Signals ◽

Smart Systems ◽

Emotion Elicitation ◽

Mode Decomposition ◽

Elicitation Process

Affective computing aims to create smart systems able to interact emotionally with users. For effective affective computing experiences, emotions should be detected accurately. The emotion influences appear in all the modalities of humans, such as the facial expression, voice, and body language, as well as in the different bio-parameters of the agents, such as the electro-dermal activity (EDA), the respiration patterns, the skin conductance, and the temperature as well as the brainwaves, which is called electroencephalography (EEG). This review provides an overview of the emotion recognition process, its methodology, and methods. It also explains the EEG-based emotion recognition as an example of emotion recognition methods demonstrating the required steps starting from capturing the EEG signals during the emotion elicitation process, then feature extraction using different techniques, such as empirical mode decomposition technique (EMD) and variational mode decomposition technique (VMD). Finally, emotion classification using different classifiers including the support vector machine (SVM) and deep neural network (DNN) is also highlighted.

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Improved Deep Feature Learning by Synchronization Measurements for Multi-Channel EEG Emotion Recognition

Complexity ◽

10.1155/2020/6816502 ◽

2020 ◽

Vol 2020 ◽

pp. 1-15 ◽

Cited By ~ 2

Author(s):

Hao Chao ◽

Liang Dong ◽

Yongli Liu ◽

Baoyun Lu

Keyword(s):

Deep Learning ◽

Emotion Recognition ◽

Affective Computing ◽

Learning Model ◽

Support Vector ◽

Eeg Signals ◽

Global Synchronization ◽

Spatial Characteristics ◽

Maximal Information Coefficient ◽

Deep Learning Model

Emotion recognition based on multichannel electroencephalogram (EEG) signals is a key research area in the field of affective computing. Traditional methods extract EEG features from each channel based on extensive domain knowledge and ignore the spatial characteristics and global synchronization information across all channels. This paper proposes a global feature extraction method that encapsulates the multichannel EEG signals into gray images. The maximal information coefficient (MIC) for all channels was first measured. Subsequently, an MIC matrix was constructed according to the electrode arrangement rules and represented by an MIC gray image. Finally, a deep learning model designed with two principal component analysis convolutional layers and a nonlinear transformation operation extracted the spatial characteristics and global interchannel synchronization features from the constructed feature images, which were then input to support vector machines to perform the emotion recognition tasks. Experiments were conducted on the benchmark dataset for emotion analysis using EEG, physiological, and video signals. The experimental results demonstrated that the global synchronization features and spatial characteristics are beneficial for recognizing emotions and the proposed deep learning model effectively mines and utilizes the two salient features.

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Analyzing Emotional Oscillatory Brain Network for Valence and Arousal-Based Emotion Recognition Using EEG Data

International Journal of Information Technology & Decision Making ◽

10.1142/s0219622019500238 ◽

2019 ◽

Vol 18 (04) ◽

pp. 1359-1378

Author(s):

Jianzhuo Yan ◽

Hongzhi Kuai ◽

Jianhui Chen ◽

Ning Zhong

Keyword(s):

Emotion Recognition ◽

Affective Computing ◽

Affective State ◽

Feature Selection Method ◽

Emotional Valence ◽

Brain Network ◽

Previous Method ◽

Emotional States ◽

Eeg Data ◽

Valence And Arousal

Emotion recognition is a highly noteworthy and challenging work in both cognitive science and affective computing. Currently, neurobiology studies have revealed the partially synchronous oscillating phenomenon within brain, which needs to be analyzed from oscillatory synchronization. This combination of oscillations and synchronism is worthy of further exploration to achieve inspiring learning of the emotion recognition models. In this paper, we propose a novel approach of valence and arousal-based emotion recognition using EEG data. First, we construct the emotional oscillatory brain network (EOBN) inspired by the partially synchronous oscillating phenomenon for emotional valence and arousal. And then, a coefficient of variation and Welch’s [Formula: see text]-test based feature selection method is used to identify the core pattern (cEOBN) within EOBN for different emotional dimensions. Finally, an emotional recognition model (ERM) is built by combining cEOBN-inspired information obtained in the above process and different classifiers. The proposed approach can combine oscillation and synchronization characteristics of multi-channel EEG signals for recognizing different emotional states under the valence and arousal dimensions. The cEOBN-based inspired information can effectively reduce the dimensionality of the data. The experimental results show that the previous method can be used to detect affective state at a reasonable level of accuracy.

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A combined cepstral distance method for emotional speech recognition

International Journal of Advanced Robotic Systems ◽

10.1177/1729881417719836 ◽

2017 ◽

Vol 14 (4) ◽

pp. 172988141771983 ◽

Cited By ~ 2

Author(s):

Changqin Quan ◽

Bin Zhang ◽

Xiao Sun ◽

Fuji Ren

Keyword(s):

Artificial Intelligence ◽

Speech Recognition ◽

Affective Computing ◽

Recognition Rate ◽

Support Vector ◽

Emotional Speech ◽

Emotion Classification ◽

Distance Method ◽

Speech Database ◽

Emotional Speech Recognition

Affective computing is not only the direction of reform in artificial intelligence but also exemplification of the advanced intelligent machines. Emotion is the biggest difference between human and machine. If the machine behaves with emotion, then the machine will be accepted by more people. Voice is the most natural and can be easily understood and accepted manner in daily communication. The recognition of emotional voice is an important field of artificial intelligence. However, in recognition of emotions, there often exists the phenomenon that two emotions are particularly vulnerable to confusion. This article presents a combined cepstral distance method in two-group multi-class emotion classification for emotional speech recognition. Cepstral distance combined with speech energy is well used as speech signal endpoint detection in speech recognition. In this work, the use of cepstral distance aims to measure the similarity between frames in emotional signals and in neutral signals. These features are input for directed acyclic graph support vector machine classification. Finally, a two-group classification strategy is adopted to solve confusion in multi-emotion recognition. In the experiments, Chinese mandarin emotion database is used and a large training set (1134 + 378 utterances) ensures a powerful modelling capability for predicting emotion. The experimental results show that cepstral distance increases the recognition rate of emotion sad and can balance the recognition results with eliminating the over fitting. And for the German corpus Berlin emotional speech database, the recognition rate between sad and boring, which are very difficult to distinguish, is up to 95.45%.

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Feasibility Testing of a Wearable Behavioral Aid for Social Learning in Children with Autism

Applied Clinical Informatics ◽

10.1055/s-0038-1626727 ◽

2018 ◽

Vol 09 (01) ◽

pp. 129-140 ◽

Cited By ~ 13

Author(s):

Jena Daniels ◽

Nick Haber ◽

Catalin Voss ◽

Jessey Schwartz ◽

Serena Tamura ◽

...

Keyword(s):

Logistic Regression ◽

Emotion Recognition ◽

Recognition Task ◽

Affective Learning ◽

Autism Spectrum ◽

Therapeutic Interventions ◽

Emotion Classification ◽

Google Glass ◽

Children With Asd ◽

Logistic Regression Classifier

Background Recent advances in computer vision and wearable technology have created an opportunity to introduce mobile therapy systems for autism spectrum disorders (ASD) that can respond to the increasing demand for therapeutic interventions; however, feasibility questions must be answered first. Objective We studied the feasibility of a prototype therapeutic tool for children with ASD using Google Glass, examining whether children with ASD would wear such a device, if providing the emotion classification will improve emotion recognition, and how emotion recognition differs between ASD participants and neurotypical controls (NC). Methods We ran a controlled laboratory experiment with 43 children: 23 with ASD and 20 NC. Children identified static facial images on a computer screen with one of 7 emotions in 3 successive batches: the first with no information about emotion provided to the child, the second with the correct classification from the Glass labeling the emotion, and the third again without emotion information. We then trained a logistic regression classifier on the emotion confusion matrices generated by the two information-free batches to predict ASD versus NC. Results All 43 children were comfortable wearing the Glass. ASD and NC participants who completed the computer task with Glass providing audible emotion labeling (n = 33) showed increased accuracies in emotion labeling, and the logistic regression classifier achieved an accuracy of 72.7%. Further analysis suggests that the ability to recognize surprise, fear, and neutrality may distinguish ASD cases from NC. Conclusion This feasibility study supports the utility of a wearable device for social affective learning in ASD children and demonstrates subtle differences in how ASD and NC children perform on an emotion recognition task.

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Investigating the Role of Culture on Negative Emotion Expressions in the Wild

Frontiers in Integrative Neuroscience ◽

10.3389/fnint.2021.699667 ◽

2021 ◽

Vol 15 ◽

Author(s):

Emma Hughson ◽

Roya Javadi ◽

James Thompson ◽

Angelica Lim

Keyword(s):

Emotion Recognition ◽

North American ◽

Affective Computing ◽

Negative Emotion ◽

Negative Emotions ◽

Support Vector ◽

Data Sets ◽

Social Signals ◽

Data Set ◽

In The Wild

Even though culture has been found to play some role in negative emotion expression, affective computing research primarily takes on a basic emotion approach when analyzing social signals for automatic emotion recognition technologies. Furthermore, automatic negative emotion recognition systems still train data that originates primarily from North America and contains a majority of Caucasian training samples. As such, the current study aims to address this problem by analyzing what the differences are of the underlying social signals by leveraging machine learning models to classify 3 negative emotions, contempt, anger and disgust (CAD) amongst 3 different cultures: North American, Persian, and Filipino. Using a curated data set compiled from YouTube videos, a support vector machine (SVM) was used to predict negative emotions amongst differing cultures. In addition a one-way ANOVA was used to analyse the differences that exist between each culture group in-terms of level of activation of underlying social signal. Our results not only highlighted the significant differences in the associated social signals that were activated for each culture, but also indicated the specific underlying social signals that differ in our cross-cultural data sets. Furthermore, the automatic classification methods showed North American expressions of CAD to be well-recognized, while Filipino and Persian expressions were recognized at near chance levels.

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Weighted Feature Gaussian Kernel SVM for Emotion Recognition

Computational Intelligence and Neuroscience ◽

10.1155/2016/7696035 ◽

2016 ◽

Vol 2016 ◽

pp. 1-7 ◽

Cited By ~ 8

Author(s):

Wei Wei ◽

Qingxuan Jia

Keyword(s):

Facial Expression ◽

Emotion Recognition ◽

Kernel Function ◽

Recognition Rate ◽

Gaussian Kernel ◽

Support Vector ◽

The Past ◽

Gaussian Kernel Function ◽

Feature Based ◽

Novel Method

Emotion recognition with weighted feature based on facial expression is a challenging research topic and has attracted great attention in the past few years. This paper presents a novel method, utilizing subregion recognition rate to weight kernel function. First, we divide the facial expression image into some uniform subregions and calculate corresponding recognition rate and weight. Then, we get a weighted feature Gaussian kernel function and construct a classifier based on Support Vector Machine (SVM). At last, the experimental results suggest that the approach based on weighted feature Gaussian kernel function has good performance on the correct rate in emotion recognition. The experiments on the extended Cohn-Kanade (CK+) dataset show that our method has achieved encouraging recognition results compared to the state-of-the-art methods.

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