EEG based emotion classification using “Correlation Based Subset Selection”

Chakladar, Debashis Das; Chakraborty, Sanjay

doi:10.1016/j.bica.2018.04.012

Cited by 39 publications

(14 citation statements)

References 21 publications

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“…For example, when the temporal features of Hjorth complexity are considered, the left frontal region at ‘Fp1’ and ‘F3’ is highly reduced under stress. This reduction is consistent with the previous emotion study that utilized videos to induce negative emotions in the participants [ 60 ]. Likewise, when the complexity and skewness are considered, the right frontal region at ‘FP2’ and ‘F4’ is highly reduced.…”

Section: Discussionsupporting

confidence: 92%

EEG Mental Stress Assessment Using Hybrid Multi-Domain Feature Sets of Functional Connectivity Network and Time-Frequency Features

Hag

Handayani

Pillai

et al. 2021

Sensors

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Exposure to mental stress for long period leads to serious accidents and health problems. To avoid negative consequences on health and safety, it is very important to detect mental stress at its early stages, i.e., when it is still limited to acute or episodic stress. In this study, we developed an experimental protocol to induce two different levels of stress by utilizing a mental arithmetic task with time pressure and negative feedback as the stressors. We assessed the levels of stress on 22 healthy subjects using frontal electroencephalogram (EEG) signals, salivary alpha-amylase level (AAL), and multiple machine learning (ML) classifiers. The EEG signals were analyzed using a fusion of functional connectivity networks estimated by the Phase Locking Value (PLV) and temporal and spectral domain features. A total of 210 different features were extracted from all domains. Only the optimum multi-domain features were used for classification. We then quantified stress levels using statistical analysis and seven ML classifiers. Our result showed that the AAL level was significantly increased (p < 0.01) under stress condition in all subjects. Likewise, the functional connectivity network demonstrated a significant decrease under stress, p < 0.05. Moreover, we achieved the highest stress classification accuracy of 93.2% using the Support Vector Machine (SVM) classifier. Other classifiers produced relatively similar results.

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Section: Discussionsupporting

confidence: 92%

EEG Mental Stress Assessment Using Hybrid Multi-Domain Feature Sets of Functional Connectivity Network and Time-Frequency Features

Hag

Handayani

Pillai

et al. 2021

Sensors

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“…In a BCI system, specific patterns of brain activity are translated into control commands in the purpose of particular devices operation [2]. Mind-controlled wheelchair [3], home appliances [4], prosthetic arm controlling [5], spelling system [6], emotion detection system [7] and biometrics [8] are the popular BCI applications [9]. Currently, BCI applications have been widened from medical to non-medical fields, for example, BCI based games and virtual reality [9].…”

Section: Introductionmentioning

confidence: 99%

Wink based facial expression classification using machine learning approach

et al. 2020

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Facial expression may establish communication between physically disabled people and assistive devices. Different types of facial expression including eye wink, smile, eye blink, looking up and looking down can be extracted from the brain signal. In this study, the possibility of controlling assistive devices using the individual's wink has been investigated. Brain signals from the five subjects have been captured to recognize the left wink, right wink, and no wink. The brain signals have been captured using Emotiv Insight which consists of five channels. Fast Fourier transform and the sample range have been computed to extract the features. The extracted features have been classified with the help of different machine learning algorithms. Here, support vector machine (SVM), linear discriminant analysis (LDA) and K-nearest neighbor (K-NN) have been employed to classify the features sets. The performance of the classifier in terms of accuracy, confusion matrix, true positive and false positive rate and the area under curve (AUC)-receiver operating characteristics (ROC) have been evaluated. In the case of sample range, the highest training and testing accuracies are 98.9% and 96.7% respectively which have been achieved by two classifiers namely, SVM and K-NN. The achieved results indicate that the person's wink can be utilized in controlling assistive devices.

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“…Samples with number of subjects below this threshold were considered not statistically significant. Studies claiming the best accuracy on emotional valence assessment are based on public EEG signal datasets: SEED [24][25][26][27][28][29] , DEAP [25][26][27][28][30][31][32][33][34][35][36][37][38][39][40] , and DREAMER 29,37,38 .…”

Section: Related Workmentioning

confidence: 99%

High-wearable EEG-Based Detection of Emotional Valence for Scientific Measurement of Emotions

Apicella

Arpaïa

Mastrati

et al. 2021

Preprint

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An emotional-valence detection method for a very–high wearable EEG-based system is proposed. Valence detection occurs along the interval scale theorized by the circumplex model of emotions. The binary choice, positive valence vs negative valence, represents a first step towards the adoption of a metric scale with a finer resolution. Wearability is guaranteed by a wireless cap with conductive-rubber dry electrodes and 8 data acquisition channels. Experimental validation was realized on 25 volunteers without depressive disorders. The metrological reference was built by combining the rating of a standardized set of pictures from dataset Oasis and results from the Self Assessment Manikin questionnaire. Two different strategies for feature extraction were compared: (i) based on a-priory knowledge (i.e. Hemispheric Asymmetry Theories) and (ii) automated. A pipeline of a custom 12-band Filter Bank and Common Spatial Pattern algorithm is the method proposed for automated feature extraction. Four machine learning classifiers were tested for validating the proposed method in discriminating two classes (high or low emotional valence). An intra-individual average accuracy, 96.2 %, was obtained by a shallow artificial neural network, while K-Nearest Neighbors allowed obtaining 80.3 % of inter-individual accuracy. Considering the wearability, and the well-founded metrological reference, results are the state of the art in emotional valence detection.

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EEG based emotion classification using “Correlation Based Subset Selection”

Cited by 39 publications

References 21 publications

EEG Mental Stress Assessment Using Hybrid Multi-Domain Feature Sets of Functional Connectivity Network and Time-Frequency Features

EEG Mental Stress Assessment Using Hybrid Multi-Domain Feature Sets of Functional Connectivity Network and Time-Frequency Features

Wink based facial expression classification using machine learning approach

High-wearable EEG-Based Detection of Emotional Valence for Scientific Measurement of Emotions

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