StressNet: Hybrid model of LSTM and CNN for stress detection from electroencephalogram signal (EEG)

Mane, Swaymprabha Alias Megha; Shinde, Arundhati

doi:10.1016/j.rico.2023.100231

Cited by 17 publications

(3 citation statements)

References 21 publications

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“…CNN has demonstrated a high level of effectiveness in EEG and ECG signal-related tasks [10][11][12][13][14]. In this study, we utilized models based on CNN to categorize EEG and ECG signals separately, and subsequently incorporated the classi cation outcomes of both into a decision model for conclusive decision making.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

“…In addition to traditional machine learning models, deep learning based EEG and ECG stress classi cation is also becoming popular. Mane et al [10] designed a hybrid network of Convolution Neural Network(CNN) and Recurrent Neural Network(RNN) based on EEG signals to binary classify heart stress. Alruily et al [11] used CNN and Long Short-Term Memory(LSTM) to extract features and combined with the Arti cial Bee Colony (ABC) method and African buffalo optimization to classify acute stress and chronic stress based on EEG.…”

Section: Introductionmentioning

confidence: 99%

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Psychological Stress Classification Using EEG and ECG: A CNN Based Multimodal Fusion Model

Zhou,

Wang,

Jiang

2024

Preprint

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Psychological stress cannot be ignored in today's society, and there is an urgent need for an objective and cost-effective method to detect it. However, traditional machine learning methods that require manual feature extraction require a lot of research time and cannot guarantee accuracy. In this paper, we establish a four-category stress multimodal dataset by collecting EEG and ECG signals from 24 subjects performing mental arithmetic tasks with different difficulty levels and propose a multimodal decision fusion model based on Convolution Neural Network to classify the data. The prediction probabilities of EEG and ECG signals for the four stress categories are first extracted by two models each and then fused into the decision model for the final classification, 5-fold cross-validation and Leave-Three-Subjects-Out experiments are performed, which achieve 91.14% and 91.97% accuracy, respectively. In addition, the features of the convolution layer were visualized using the 1D-Grad-CAM method to improve the interpretability of the model.

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Section: Deep Learning Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Psychological Stress Classification Using EEG and ECG: A CNN Based Multimodal Fusion Model

Zhou,

Wang,

Jiang

2024

Preprint

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“…While there have been many studies using EEG and pulse waves to measure stress and depression in the general population [23][24][25][26], there is a lack of research on the relationship between EEG and pulse waves and the psychological state of military personnel [27][28][29][30][31].…”

Section: Introduction 1background and Purpose Of The Studymentioning

confidence: 99%

A Study of Brain Function Characteristics of Service Members at High Risk for Accidents in the Military

Choi,

Yun

2023

Brain Sciences

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Military accidents are often associated with stress and depressive psychological conditions among soldiers, and they often fail to adapt to military life. Therefore, this study analyzes whether there are differences in EEG and pulse wave indices between general soldiers and three groups of soldiers who have not adapted to military life and are at risk of accidents. Data collection was carried out using a questionnaire and a device that can measure EEG and pulse waves, and data analysis was performed using SPSS. The results showed that the concentration level and brain activity indices were higher in the general soldiers and the soldiers in the first stage of accident risk. The body stress index was higher for each stage of accident risk, and the physical vitality index was higher for general soldiers. Therefore, it can be seen that soldiers who have not adapted to military life and are at risk of accidents have somewhat lower concentration and brain activity than general soldiers, and have symptoms of stress and lethargy. The results of this study will contribute to reducing human accidents through EEG and pulse wave measurements not only in the military but also in occupations with a high risk of accidents such as construction.

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Efficient classification model for anxiety detection in autism using intelligent search optimization based on Deep CNN

Umrani,

Harshavardhanan

2024

Multimed Tools Appl

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StressNet: Hybrid model of LSTM and CNN for stress detection from electroencephalogram signal (EEG)

Cited by 17 publications

References 21 publications

Psychological Stress Classification Using EEG and ECG: A CNN Based Multimodal Fusion Model

Psychological Stress Classification Using EEG and ECG: A CNN Based Multimodal Fusion Model

A Study of Brain Function Characteristics of Service Members at High Risk for Accidents in the Military

Efficient classification model for anxiety detection in autism using intelligent search optimization based on Deep CNN

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