An effective optimized deep learning for emotion classification from EEG signals

Lokesh, Sittiahgari; Reddy, Tadipathri Sreenivasulu

doi:10.1007/s11760-022-02373-2

Cited by 2 publications

(1 citation statement)

References 33 publications

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“…The experimental results showed that the average accuracy was 92.44 %, which had increased by 0.79 %. Lokesh, S. et al [ 33 ]proposed Fractional Chimpanzee Optimization Algorithm (FrChOA) which combined the Chimpanzee Optimization Algorithm (COA) with fractional order calculus, and its classification accuracy was 88.48 % with 20 channels selected. Kannadasan K et al [ 34 ] proposed a differential-evolution-based feature selection algorithm (DEFS) to obtain an optimal feature set for effective subject-independent for emotion recognition with SVM (DEFS-SVM).…”

Section: Introductionmentioning

confidence: 99%

Optimal channel dynamic selection for Constructing lightweight Data EEG-based emotion recognition

Zhang,

Xu,

Zhang

et al. 2024

Heliyon

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Section: Introductionmentioning

confidence: 99%

Optimal channel dynamic selection for Constructing lightweight Data EEG-based emotion recognition

Zhang,

Xu,

Zhang

et al. 2024

Heliyon

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A Mental Health Detection Model of College Students Under the Behavior of Learning Burnout

LU,

GUO

2024

J. Mech. Med. Biol.

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With the increase in academic pressure on college students, some students show signs of academic burnout, which may lead to mental health problems. This study aims to explore the mental health status of college students under the manifestation of academic burnout and to propose a mental health detection model based on electroencephalography (EEG) signals. In this study, college students with the manifestation of study burnout were selected as research subjects, and their EEG signals were collected to identify and analyze their psychological state using a mental health recognition model. In the selection of mental health recognition models, this study used a variety of classical models for comparative analysis, including support vector machine (SVM), k-nearest neighbor (KNN), logistic regression (LR), convolutional neural network (CNN), long and short-term memory network (LSTM), and convolutional neural network-long and short-term memory network (CNN-LSTM). Experimental results show that the CNN-LSTM model performs best in identifying students with mental health problems, and its accuracy and stability are better than other models. Through this study, we conclude that academic burnout is closely related to college students’ mental health problems, and EEG signals combined with the CNN-LSTM model can effectively identify the mental health status of these students. This finding provides a new method and tool for monitoring college students’ mental health, which helps detect and intervene in students’ mental health problems early and promote their healthy growth and development.

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An effective optimized deep learning for emotion classification from EEG signals

Cited by 2 publications

References 33 publications

Optimal channel dynamic selection for Constructing lightweight Data EEG-based emotion recognition

Optimal channel dynamic selection for Constructing lightweight Data EEG-based emotion recognition

A Mental Health Detection Model of College Students Under the Behavior of Learning Burnout

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