Design of subject independent 3D VAD emotion detection system using EEG signals and machine learning algorithms

Nandini, Durgesh; Yadav, Jyoti; Rani, Asha; Singh, Vijander

doi:10.1016/j.bspc.2023.104894

Cited by 17 publications

(4 citation statements)

References 51 publications

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“…We chose to use SMOTE because it is a simple and effective oversampling technique that has been shown to improve the performance of machine learning algorithms on imbalanced datasets. However, it can potentially increase minority class clusters and lead to overfitting ( Nandini et al, 2023 ). To address this concern, we compared the performance of machine learning algorithms with and without SMOTE.…”

Section: Methodsmentioning

confidence: 99%

A comprehensive exploration of machine learning techniques for EEG-based anxiety detection

Aldayel,

Al-Nafjan

2024

PeerJ Computer Science

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The performance of electroencephalogram (EEG)-based systems depends on the proper choice of feature extraction and machine learning algorithms. This study highlights the significance of selecting appropriate feature extraction and machine learning algorithms for EEG-based anxiety detection. We explored different annotation/labeling, feature extraction, and classification algorithms. Two measurements, the Hamilton anxiety rating scale (HAM-A) and self-assessment Manikin (SAM), were used to label anxiety states. For EEG feature extraction, we employed the discrete wavelet transform (DWT) and power spectral density (PSD). To improve the accuracy of anxiety detection, we compared ensemble learning methods such as random forest (RF), AdaBoost bagging, and gradient bagging with conventional classification algorithms including linear discriminant analysis (LDA), support vector machine (SVM), and k-nearest neighbor (KNN) classifiers. We also evaluated the performance of the classifiers using different labeling (SAM and HAM-A) and feature extraction algorithms (PSD and DWT). Our findings demonstrated that HAM-A labeling and DWT-based features consistently yielded superior results across all classifiers. Specifically, the RF classifier achieved the highest accuracy of 87.5%, followed by the Ada boost bagging classifier with an accuracy of 79%. The RF classifier outperformed other classifiers in terms of accuracy, precision, and recall.

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Section: Methodsmentioning

confidence: 99%

A comprehensive exploration of machine learning techniques for EEG-based anxiety detection

Aldayel,

Al-Nafjan

2024

PeerJ Computer Science

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“…Fourier transformations are used in this study to break the EEG signal down into its frequency components. The FFT (Fast Fourier Transform) technique, which calculates a sequence's DFT (Discrete Fourier Transform), is a widely used method for computing the Fourier transform 46 . It yields the same results as evaluating the DFT definition explicitly, except that it is significantly faster.…”

Section: Proposed Methodsmentioning

confidence: 99%

Detecting emotions through EEG signals based on modified convolutional fuzzy neural network

Ahmadzadeh Nobari Azar,

Cavus,

Esmaili

et al. 2024

Sci Rep

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Emotion is a human sense that can influence an individual’s life quality in both positive and negative ways. The ability to distinguish different types of emotion can lead researchers to estimate the current situation of patients or the probability of future disease. Recognizing emotions from images have problems concealing their feeling by modifying their facial expressions. This led researchers to consider Electroencephalography (EEG) signals for more accurate emotion detection. However, the complexity of EEG recordings and data analysis using conventional machine learning algorithms caused inconsistent emotion recognition. Therefore, utilizing hybrid deep learning models and other techniques has become common due to their ability to analyze complicated data and achieve higher performance by integrating diverse features of the models. However, researchers prioritize models with fewer parameters to achieve the highest average accuracy. This study improves the Convolutional Fuzzy Neural Network (CFNN) for emotion recognition using EEG signals to achieve a reliable detection system. Initially, the pre-processing and feature extraction phases are implemented to obtain noiseless and informative data. Then, the CFNN with modified architecture is trained to classify emotions. Several parametric and comparative experiments are performed. The proposed model achieved reliable performance for emotion recognition with an average accuracy of 98.21% and 98.08% for valence (pleasantness) and arousal (intensity), respectively, and outperformed state-of-the-art methods.

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“…However, computational complexity can be considered a disadvantage of this research. Nandini et al [25] used multi-domain feature extraction and different time-frequency domain techniques and wavelet-based atomic function to automatically detect emotions from EEG signals. These researchers have used the DEAP database to evaluate their algorithm.…”

Section: Introductionmentioning

confidence: 99%

Customized 2D CNN Model for the Automatic Emotion Recognition Based on EEG Signals

et al. 2023

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Automatic emotion recognition from electroencephalogram (EEG) signals can be considered as the main component of brain–computer interface (BCI) systems. In the previous years, many researchers in this direction have presented various algorithms for the automatic classification of emotions from EEG signals, and they have achieved promising results; however, lack of stability, high error, and low accuracy are still considered as the central gaps in this research. For this purpose, obtaining a model with the precondition of stability, high accuracy, and low error is considered essential for the automatic classification of emotions. In this research, a model based on Deep Convolutional Neural Networks (DCNNs) is presented, which can classify three positive, negative, and neutral emotions from EEG signals based on musical stimuli with high reliability. For this purpose, a comprehensive database of EEG signals has been collected while volunteers were listening to positive and negative music in order to stimulate the emotional state. The architecture of the proposed model consists of a combination of six convolutional layers and two fully connected layers. In this research, different feature learning and hand-crafted feature selection/extraction algorithms were investigated and compared with each other in order to classify emotions. The proposed model for the classification of two classes (positive and negative) and three classes (positive, neutral, and negative) of emotions had 98% and 96% accuracy, respectively, which is very promising compared with the results of previous research. In order to evaluate more fully, the proposed model was also investigated in noisy environments; with a wide range of different SNRs, the classification accuracy was still greater than 90%. Due to the high performance of the proposed model, it can be used in brain–computer user environments.

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Design of subject independent 3D VAD emotion detection system using EEG signals and machine learning algorithms

Cited by 17 publications

References 51 publications

A comprehensive exploration of machine learning techniques for EEG-based anxiety detection

A comprehensive exploration of machine learning techniques for EEG-based anxiety detection

Detecting emotions through EEG signals based on modified convolutional fuzzy neural network

Customized 2D CNN Model for the Automatic Emotion Recognition Based on EEG Signals

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