EEG-Based Emotion Recognition Using Deep Learning and M3GP

Aguiñaga, Adrian Rodriguez; Muñoz, Luis; López-López, Víctor R.; Téllez, Andrés Calvillo

doi:10.3390/app12052527

Cited by 16 publications

(2 citation statements)

References 44 publications

(55 reference statements)

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“…Some popular feature extraction methods from EEG signals include, Differential Entropy (DE) [16], Power Spectral Density (PSD), Short-Time Fourier Transform (STFT) [6], Smoothing Pseudo Wigner-Ville Distribution (SPVWD) [7], and multiclass genetic programming with multidimensional populations (M3GP) [17]. Additionally, many deep learning models have been proposed in the literature that can extract features from raw EEG signal values, such as [4,9], and [18].…”

Section: Supervised Eeg-based Emotion Recognition Methodsmentioning

confidence: 99%

A novel semi-supervised deep learning method for enhancing discriminability and diversity in EEG-based emotion recognition task

Waleed Al-Asadi,

Salehpour,

Aghdasi

2024

Phys. Scr.

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Numerous deep learning models have been introduced for EEG-based Emotion recognition tasks. Nevertheless, the majority of these models are fully supervised, demanding substan-tial amounts of labeled EEG signals. The labeling process of EEG signals is both time-intensive and costly, involving numerous trials and meticulous analysis by experts. Recently, some advanced semi-supervised algorithms that can achieve a competitive performance with fully-supervised methods by using only a small set of labeled data have been presented. However, these algorithms are primarily developed for the image data type, and naïve adap-tation of them for EEG applications results in unsatisfactory performance. To address this is-sue, we present a robust semi-supervised EEG-based method that exploits the best techniques from advanced semi-supervised algorithms in the computer vision domain enriched with novel regularization terms for unlabeled signals. The proposed regularization terms improve both the discrimi-nability and diversity of the model’s predictions and effectively leverage prior knowledge about the class distributions, thereby achieving a superior performance compared to the dis-tribution alignment techniques in state-of-the-art methods. We evaluate our method on the DEAP dataset for cross-subject valence/arousal emotion recognition tasks. The results indi-cate that the proposed method consistently surpasses the peer methods at different numbers of labeled data by a large margin.

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Section: Supervised Eeg-based Emotion Recognition Methodsmentioning

confidence: 99%

A novel semi-supervised deep learning method for enhancing discriminability and diversity in EEG-based emotion recognition task

Waleed Al-Asadi,

Salehpour,

Aghdasi

2024

Phys. Scr.

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“…To date, many researchers have performed emotion recognition on various EEG datasets [ 13 , 14 , 15 , 16 ]. These datasets are mostly created by recording the EEG signals that occur on the subjects by applying stimuli such as sound or images (picture/video) to the subjects in a certain procedure.…”

Section: Introductionmentioning

confidence: 99%

Use of Differential Entropy for Automated Emotion Recognition in a Virtual Reality Environment with EEG Signals

et al. 2022

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Emotion recognition is one of the most important issues in human–computer interaction (HCI), neuroscience, and psychology fields. It is generally accepted that emotion recognition with neural data such as electroencephalography (EEG) signals, functional magnetic resonance imaging (fMRI), and near-infrared spectroscopy (NIRS) is better than other emotion detection methods such as speech, mimics, body language, facial expressions, etc., in terms of reliability and accuracy. In particular, EEG signals are bioelectrical signals that are frequently used because of the many advantages they offer in the field of emotion recognition. This study proposes an improved approach for EEG-based emotion recognition on a publicly available newly published dataset, VREED. Differential entropy (DE) features were extracted from four wavebands (theta 4–8 Hz, alpha 8–13 Hz, beta 13–30 Hz, and gamma 30–49 Hz) to classify two emotional states (positive/negative). Five classifiers, namely Support Vector Machine (SVM), k-Nearest Neighbor (kNN), Naïve Bayesian (NB), Decision Tree (DT), and Logistic Regression (LR) were employed with DE features for the automated classification of two emotional states. In this work, we obtained the best average accuracy of 76.22% ± 2.06 with the SVM classifier in the classification of two states. Moreover, we observed from the results that the highest average accuracy score was produced with the gamma band, as previously reported in studies in EEG-based emotion recognition.

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Domain-Aware Feature Learning with Grammar-Guided Genetic Programming

Ingelse¹,

Fonseca²

2023

Lecture Notes in Computer Science

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EEG-Based Emotion Recognition Using Deep Learning and M3GP

Cited by 16 publications

References 44 publications

A novel semi-supervised deep learning method for enhancing discriminability and diversity in EEG-based emotion recognition task

A novel semi-supervised deep learning method for enhancing discriminability and diversity in EEG-based emotion recognition task

Use of Differential Entropy for Automated Emotion Recognition in a Virtual Reality Environment with EEG Signals

Domain-Aware Feature Learning with Grammar-Guided Genetic Programming

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