Novel Imaging Approach for Mental Stress Detection Using EEG Signals

Mane, Swaymprabha Alias Megha; Shinde, Arundhati

doi:10.1007/978-981-16-6887-6_3

Cited by 5 publications

(5 citation statements)

References 24 publications

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“… Mane & Shinde (2022) utilized the DASPS dataset to estimate mental stress levels and investigate the effectiveness of neural network techniques in utilizing EEG signals for this purpose. The study addresses the challenges of the irregular shape of EEG signals and the requirement for fixed data shapes as input to convolutional neural networks.…”

Section: Related Workmentioning

confidence: 99%

“…These signals provide a reliable and non-invasive method for detecting changes in mental states, which can be used in various settings to detect anxiety. Accordingly, different artificial intelligence and machine learning approaches have been applied to build systems that have the potential to improve the accuracy of anxiety detection, thereby enabling early intervention and support for individuals with anxiety disorders ( Chatterjee, Gavas & Saha, 2023 ; Mane & Shinde, 2022 ; Chen et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

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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: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

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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“…EEG has shown promising potential as a non-invasive and objective tool for detecting crime. Through the use of EEG, it is possible to capture and analyze brain activity patterns in response to various stimuli (Mane and Shinde 2022), including those related to criminal activity. By identifying distinctive patterns of neural activity associated with criminal intent or actions, EEG can assist law enforcement in identifying suspects and preventing crimes.…”

Section: Face Recognitionmentioning

confidence: 99%

“…Crime detection by examining EEG signals and using MLOps approach as well as some small techniques. Emotion detection based on brain signals is one of the best methods to detect human emotions and stress, giving accurate results (Mane and Shinde 2022). Brain wave or signal based system along with EEG signal based system can help in detecting various disorders and disabilities.…”

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confidence: 99%

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Integrating MLOps and EEG Techniques for Enhanced Crime Detection and Prevention

Kathole,

Shinde,

Wadhwa

2023

Multidiscip. Sci. J.

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The use of machine learning and artificial intelligence in crime detection has gained significant attention in recent years. This research paper explores the potential of MLOps techniques in identifying criminal behavior through the analysis of vehicle plate numbers, person detection, and object behavior detection. In this paper, we present a literature review of studies that have investigated the use of MLOps in crime detection and highlight the potential of these techniques to be used in criminal investigations. Specifically, we discuss the use of machine learning models for vehicle plate number recognition, person detection through image and video analysis, and object behavior detection through image and video analysis. We also present a framework for integrating MLOps techniques into criminal investigations, which involves a combination of data acquisition, data preprocessing, model development, model training and testing, and deployment. Additionally, we discuss the ethical implications of using MLOps techniques in criminal investigations and highlight the need for transparency and fairness in model development and deployment. The detection and prevention of criminal behavior is a critical issue for society. In recent years, there has been growing interest in the use of electroencephalogram (EEG) techniques to detect and predict criminal behavior. This research paper explores the potential of EEG techniques as a means of detecting crime by analyzing brainwave activity. Specifically, we examine the use of alpha and beta waves in identifying deceptive or abnormal behavior. We present a literature review of studies that have investigated the relationship between EEG signals and criminal behavior and highlight the potential of these techniques to be used in criminal investigations. Furthermore, we propose a framework for integrating EEG techniques into criminal investigations, which involves a combination of data acquisition, analysis, and interpretation. Finally, we discuss the ethical implications of using EEG techniques in criminal investigations and highlight the need for further research in this area.

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A review on evaluating mental stress by deep learning using EEG signals

Badr,

Tariq,

Al-Shargie

et al. 2024

Neural Comput & Applic

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Mental stress is a common problem that affects individuals all over the world. Stress reduces human functionality during routine work and may lead to severe health defects. Early detection of stress is important for preventing diseases and other negative health-related consequences of stress. Several neuroimaging techniques have been utilized to assess mental stress, however, due to its ease of use, robustness, and non-invasiveness, electroencephalography (EEG) is commonly used. This paper aims to fill a knowledge gap by reviewing the different EEG-related deep learning algorithms with a focus on Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs) for the evaluation of mental stress. The review focuses on data representation, individual deep neural network model architectures, hybrid models, and results amongst others. The contributions of the paper address important issues such as data representation and model architectures. Out of all reviewed papers, 67% used CNN, 9% LSTM, and 24% hybrid models. Based on the reviewed literature, we found that dataset size and different representations contributed to the performance of the proposed networks. Raw EEG data produced classification accuracy around 62% while using spectral and topographical representation produced up to 88%. Nevertheless, the roles of generalizability across different deep learning models and individual differences remain key areas of inquiry. The review encourages the exploration of innovative avenues, such as EEG data image representations concurrently with graph convolutional neural networks (GCN), to mitigate the impact of inter-subject variability. This novel approach not only allows us to harmonize structural nuances within the data but also facilitates the integration of temporal dynamics, thereby enabling a more comprehensive assessment of mental stress levels.

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Novel Imaging Approach for Mental Stress Detection Using EEG Signals

Cited by 5 publications

References 24 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

Integrating MLOps and EEG Techniques for Enhanced Crime Detection and Prevention

A review on evaluating mental stress by deep learning using EEG signals

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