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

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

Symmetry ◽

10.3390/sym11050683 ◽

2019 ◽

Vol 11 (5) ◽

pp. 683 ◽

Cited By ~ 3

Author(s):

Jiahui Cai ◽

Wei Chen ◽

Zhong Yin

Keyword(s):

Feature Selection ◽

Emotion Recognition ◽

Least Square ◽

Recursive Feature Elimination ◽

Support Vector ◽

Affective States ◽

Eeg Signals ◽

Subject Selection ◽

Automation Systems ◽

Eeg Data

Feature selection plays a crucial role in analyzing huge-volume, high-dimensional EEG signals in human-centered automation systems. However, classical feature selection methods pay little attention to transferring cross-subject information for emotions. To perform cross-subject emotion recognition, a classifier able to utilize EEG data to train a general model suitable for different subjects is needed. However, existing methods are imprecise due to the fact that the effective feelings of individuals are personalized. In this work, the cross-subject emotion recognition model on both binary and multi affective states are developed based on the newly designed multiple transferable recursive feature elimination (M-TRFE). M-TRFE manages not only a stricter feature selection of all subjects to discover the most robust features but also a unique subject selection to decide the most trusted subjects for certain emotions. Via a least square support vector machine (LSSVM), the overall multi (joy, peace, anger and depression) classification accuracy of the proposed M-TRFE reaches 0.6513, outperforming all other methods used or referenced in this paper.

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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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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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Effect on speech emotion classification of a feature selection approach using a convolutional neural network

PeerJ Computer Science ◽

10.7717/peerj-cs.766 ◽

2021 ◽

Vol 7 ◽

pp. e766

Author(s):

Ammar Amjad ◽

Lal Khan ◽

Hsien-Tsung Chang

Keyword(s):

Neural Network ◽

Feature Selection ◽

Convolutional Neural Network ◽

Feature Selection Method ◽

Classification Problem ◽

Speech Emotion Recognition ◽

Support Vector ◽

Emotion Classification ◽

K Nearest Neighbors ◽

Feature Selection Technique

Speech emotion recognition (SER) is a challenging issue because it is not clear which features are effective for classification. Emotionally related features are always extracted from speech signals for emotional classification. Handcrafted features are mainly used for emotional identification from audio signals. However, these features are not sufficient to correctly identify the emotional state of the speaker. The advantages of a deep convolutional neural network (DCNN) are investigated in the proposed work. A pretrained framework is used to extract the features from speech emotion databases. In this work, we adopt the feature selection (FS) approach to find the discriminative and most important features for SER. Many algorithms are used for the emotion classification problem. We use the random forest (RF), decision tree (DT), support vector machine (SVM), multilayer perceptron classifier (MLP), and k-nearest neighbors (KNN) to classify seven emotions. All experiments are performed by utilizing four different publicly accessible databases. Our method obtains accuracies of 92.02%, 88.77%, 93.61%, and 77.23% for Emo-DB, SAVEE, RAVDESS, and IEMOCAP, respectively, for speaker-dependent (SD) recognition with the feature selection method. Furthermore, compared to current handcrafted feature-based SER methods, the proposed method shows the best results for speaker-independent SER. For EMO-DB, all classifiers attain an accuracy of more than 80% with or without the feature selection technique.

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Manifold Feature Fusion with Dynamical Feature Selection for Cross-Subject Emotion Recognition

Brain Sciences ◽

10.3390/brainsci11111392 ◽

2021 ◽

Vol 11 (11) ◽

pp. 1392

Author(s):

Yue Hua ◽

Xiaolong Zhong ◽

Bingxue Zhang ◽

Zhong Yin ◽

Jianhua Zhang

Keyword(s):

Feature Selection ◽

Emotion Recognition ◽

Affective Computing ◽

Feature Fusion ◽

Domain Adaptation ◽

Feature Selection Method ◽

Dynamical Feature ◽

Computing Systems ◽

Geometrical Information ◽

The Brain

Affective computing systems can decode cortical activities to facilitate emotional human–computer interaction. However, personalities exist in neurophysiological responses among different users of the brain–computer interface leads to a difficulty for designing a generic emotion recognizer that is adaptable to a novel individual. It thus brings an obstacle to achieve cross-subject emotion recognition (ER). To tackle this issue, in this study we propose a novel feature selection method, manifold feature fusion and dynamical feature selection (MF-DFS), under transfer learning principle to determine generalizable features that are stably sensitive to emotional variations. The MF-DFS framework takes the advantages of local geometrical information feature selection, domain adaptation based manifold learning, and dynamical feature selection to enhance the accuracy of the ER system. Based on three public databases, DEAP, MAHNOB-HCI and SEED, the performance of the MF-DFS is validated according to the leave-one-subject-out paradigm under two types of electroencephalography features. By defining three emotional classes of each affective dimension, the accuracy of the MF-DFS-based ER classifier is achieved at 0.50–0.48 (DEAP) and 0.46–0.50 (MAHNOBHCI) for arousal and valence emotional dimensions, respectively. For the SEED database, it achieves 0.40 for the valence dimension. The corresponding accuracy is significantly superior to several classical feature selection methods on multiple machine learning models.

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A Hybrid Approach for Feature Selection Based on Genetic Algorithm and Recursive Feature Elimination

International Journal of Information System Modeling and Design ◽

10.4018/ijismd.2021040102 ◽

2021 ◽

Vol 12 (2) ◽

pp. 17-38

Author(s):

Pooja Rani ◽

Rajneesh Kumar ◽

Anurag Jain ◽

Sunil Kumar Chawla

Keyword(s):

Machine Learning ◽

Genetic Algorithm ◽

Feature Selection ◽

Hybrid Approach ◽

Feature Selection Method ◽

Classification Systems ◽

Recursive Feature Elimination ◽

Support Vector ◽

Sensitivity Specificity ◽

Selection Of

Machine learning has become an integral part of our life in today's world. Machine learning when applied to real-world applications suffers from the problem of high dimensional data. Data can have unnecessary and redundant features. These unnecessary features affect the performance of classification systems used in prediction. Selection of important features is the first step in developing any decision support system. In this paper, the authors have proposed a hybrid feature selection method GARFE by integrating GA (genetic algorithm) and RFE (recursive feature elimination) algorithms. Efficiency of proposed method is analyzed using support vector machine classifier on the scale of accuracy, sensitivity, specificity, precision, F-measure, and execution time parameters. Proposed GARFE method is also compared to eight other feature selection methods. Results demonstrate that the proposed GARFE method has increased the performance of classification systems by removing irrelevant and redundant features.

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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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Predicting 30-Day Mortality after an Acute Coronary Syndrome (ACS) using Machine Learning Methods for Feature Selection, Classification and Visualisation

Sains Malaysiana ◽

10.17576/jsm-2021-5003-17 ◽

2021 ◽

Vol 50 (3) ◽

pp. 753-768

Author(s):

NANYONGA AZIIDA ◽

SORAYYA MALEK ◽

FIRDAUS AZIZ ◽

KHAIRUL SHAFIQ IBRAHIM ◽

SAZZLI KASIM

Keyword(s):

Machine Learning ◽

Cardiovascular Disease ◽

Feature Selection ◽

High Performance ◽

Feature Selection Method ◽

Mortality Prediction ◽

Recursive Feature Elimination ◽

Support Vector ◽

Related Factors ◽

Coronary Syndrome

Hybrid combinations of feature selection, classification and visualisation using machine learning (ML) methods have the potential for enhanced understanding and 30-day mortality prediction of patients with cardiovascular disease using population-specific data. Identifying a feature selection method with a classifier algorithm that produces high performance in mortality studies is essential and has not been reported before. Feature selection methods such as Boruta, Random Forest (RF), Elastic Net (EN), Recursive Feature Elimination (RFE), learning vector quantization (LVQ), Genetic Algorithm (GA), Cluster Dendrogram (CD), Support Vector Machine (SVM) and Logistic Regression (LR) were combined with RF, SVM, LR, and EN classifiers for 30-day mortality prediction. ML models were constructed using 302 patients and 54 input variables from the Malaysian National Cardiovascular Disease Database. Validation of the best ML model was performed against Thrombolysis in Myocardial Infarction (TIMI) using an additional dataset of 102 patients. The Self-Organising Feature Map (SOM) was used to visualise mortality-related factors post-ACS. The performance of MLmodels using the area under the curve (AUC) ranged from 0.48 to 0.80. The best-performing model (AUC = 0.80) was a hybrid combination of the RF variable importance method, the sequential backward selection and the RF classifier using five predictors (age, triglyceride, creatinine, troponin, and total cholesterol). Comparison with TIMI using an additional dataset resulted in the best ML model outperforming the TIMI score (AUC = 0.75 vs. AUC = 0.60). The findings of this study will provide a basis for developing an online ML-based population-specific risk scoring calculator.

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An EEG Database and Its Initial Benchmark Emotion Classification Performance

Computational and Mathematical Methods in Medicine ◽

10.1155/2020/8303465 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Ayan Seal ◽

Puthi Prem Nivesh Reddy ◽

Pingali Chaithanya ◽

Arramada Meghana ◽

Kamireddy Jahnavi ◽

...

Keyword(s):

Emotion Recognition ◽

Affective Computing ◽

Industrial Applications ◽

Classification Performance ◽

Discrete Wavelet ◽

Emotional States ◽

Emotion Classification ◽

Eeg Signals ◽

Learning Machine ◽

Facial Images

Human emotion recognition has been a major field of research in the last decades owing to its noteworthy academic and industrial applications. However, most of the state-of-the-art methods identified emotions after analyzing facial images. Emotion recognition using electroencephalogram (EEG) signals has got less attention. However, the advantage of using EEG signals is that it can capture real emotion. However, very few EEG signals databases are publicly available for affective computing. In this work, we present a database consisting of EEG signals of 44 volunteers. Twenty-three out of forty-four are females. A 32 channels CLARITY EEG traveler sensor is used to record four emotional states namely, happy, fear, sad, and neutral of subjects by showing 12 videos. So, 3 video files are devoted to each emotion. Participants are mapped with the emotion that they had felt after watching each video. The recorded EEG signals are considered further to classify four types of emotions based on discrete wavelet transform and extreme learning machine (ELM) for reporting the initial benchmark classification performance. The ELM algorithm is used for channel selection followed by subband selection. The proposed method performs the best when features are captured from the gamma subband of the FP1-F7 channel with 94.72% accuracy. The presented database would be available to the researchers for affective recognition applications.

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

Applied Sciences ◽

10.3390/app10051619 ◽

2020 ◽

Vol 10 (5) ◽

pp. 1619 ◽

Cited By ~ 4

Author(s):

Chao Pan ◽

Cheng Shi ◽

Honglang Mu ◽

Jie Li ◽

Xinbo Gao

Keyword(s):

Logistic Regression ◽

Emotion Recognition ◽

Affective Computing ◽

Critical Frequency ◽

Gaussian Kernel ◽

Support Vector ◽

Emotion Classification ◽

Frequency Bands ◽

Video Signals ◽

Valence And Arousal

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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