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Proceedings ArticleDOI

Detection of Stress in Human Brain

TL;DR: A stress detection mechanism and a stress level indicator circuit for measuring the stress level of human brain using the Electro-encephalogram (EEG) Signal are developed and indicated in the ‘Stress Indicating’ circuit.
Abstract: The essence of the paper is to develop a stress detection mechanism and a stress level indicator circuit for measuring the stress level of human brain using the Electro-encephalogram (EEG) Signal. Signals coming from the frontal lobe of human brain have been used for the measurement of stress. The brain signals of the thirty subjects are recorded while they are solving five mathematical question sets with increasing complexity. We assume that the subjects undergo through five different stress levels i.e. ‘Relaxed’, ‘Less stressed’, ‘Moderately Stressed’, ‘High Stressed’ and ‘Alarmingly Stressed’ while solving these question sets. After that recorded EEG data is processed and features are extracted. We design a feed forward neural network for classifying the stress level in human brain. We prepare a new question set consisting of easy as well as complex numerical questions for testing purpose. We record the EEG data of a subject while solving this question set. We extract six feature values from the processed EEG data of the subject. These data is fed to the designed feed forward neural network. The neural network predicts the stress level and the predicted stress level is indicated in the ‘Stress Indicating’ circuit.
Citations
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Journal ArticleDOI
TL;DR: A Multiclass Common Spatial Pattern-based moving window technique is proposed here to obtain the most distinguishable time segment of EEG trials, and BiLSTM is used to improve classification results.

7 citations

Proceedings ArticleDOI
11 Apr 2023
TL;DR: In this paper , a hybrid system that combines a regression classifier with a Haar Cascade Algorithm (HCA) is described to detect and distinguish facial emotions (Indignation, disgust, neutrality, fear, sadness, joy, and surprise).
Abstract: Human emotion and psychological stress are closely related to one another. According to research in computational psychology, knowing how stress and emotions interact is crucial. Deep learning has been used in research to identify facial expressions from photos, but finding psychological stress has not yet been directly addressed. In this study, a hybrid system that combines a regression classifier with a Haar Cascade Algorithm (HCA) is described. An HCA is taught to identify distinct emotional categories in human faces and detect and distinguish facial emotions (Indignation, disgust, neutrality, fear, sadness, joy, and surprise). For detection, on a huge number of both positive and negative images, a cascade function is learned. The deciphered emotions are then used to evaluate stress via further logarithmic regression. To assess the proposed design, experiments have been carried out using the Facial Expression Recognition (FER2013) dataset.
Proceedings ArticleDOI
11 Apr 2023
TL;DR: In this article , a hybrid system that combines a regression classifier with a Haar Cascade Algorithm (HCA) is described to detect and distinguish facial emotions (Indignation, disgust, neutrality, fear, sadness, joy, and surprise).
Abstract: Human emotion and psychological stress are closely related to one another. According to research in computational psychology, knowing how stress and emotions interact is crucial. Deep learning has been used in research to identify facial expressions from photos, but finding psychological stress has not yet been directly addressed. In this study, a hybrid system that combines a regression classifier with a Haar Cascade Algorithm (HCA) is described. An HCA is taught to identify distinct emotional categories in human faces and detect and distinguish facial emotions (Indignation, disgust, neutrality, fear, sadness, joy, and surprise). For detection, on a huge number of both positive and negative images, a cascade function is learned. The deciphered emotions are then used to evaluate stress via further logarithmic regression. To assess the proposed design, experiments have been carried out using the Facial Expression Recognition (FER2013) dataset.
Proceedings ArticleDOI
03 Oct 2022
TL;DR: In this paper , a solution for organizations where they can know the levels of stress faced by the students and could calculate percentage of stress was proposed, where students can take up the survey through a google form which consist of the parameters which are helpful in collecting information about mental distress and many other psychological factors faced by students.
Abstract: Due to the COVID-19 pandemic, to control pandemic situations and its spread, the government took a decision to shut all the educational institutions, which in turn creating a direct impact on many people by causing stress and mental illness. We propose a solution for organizations where they can know the levels of stress faced by the students and could calculate percentage of stress. So for this to be done, students can take up the survey through a google form which consist of the parameters which are helpful in collecting information about mental distress and many other psychological factors faced by the students. The data which is collected from the students is inputted into the model with results the stress levels of the students.
References
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Journal ArticleDOI
31 Jan 2012-Sensors
TL;DR: The state-of-the-art of BCIs are reviewed, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface.
Abstract: A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or 'locked in' by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.

1,407 citations

Journal ArticleDOI
TL;DR: This study applied machine-learning algorithms to categorize EEG dynamics according to subject self-reported emotional states during music listening to identify 30 subject-independent features that were most relevant to emotional processing across subjects and explored the feasibility of using fewer electrodes to characterize the EEG dynamics duringMusic listening.
Abstract: Ongoing brain activity can be recorded as electroen-cephalograph (EEG) to discover the links between emotional states and brain activity. This study applied machine-learning algorithms to categorize EEG dynamics according to subject self-reported emotional states during music listening. A framework was proposed to optimize EEG-based emotion recognition by systematically 1) seeking emotion-specific EEG features and 2) exploring the efficacy of the classifiers. Support vector machine was employed to classify four emotional states (joy, anger, sadness, and pleasure) and obtained an averaged classification accuracy of 82.29% ± 3.06% across 26 subjects. Further, this study identified 30 subject-independent features that were most relevant to emotional processing across subjects and explored the feasibility of using fewer electrodes to characterize the EEG dynamics during music listening. The identified features were primarily derived from electrodes placed near the frontal and the parietal lobes, consistent with many of the findings in the literature. This study might lead to a practical system for noninvasive assessment of the emotional states in practical or clinical applications.

823 citations


"Detection of Stress in Human Brain" refers methods in this paper

  • ...In [2] the authors have used Support Vector Machine for classifying different emotional states such as joy, anger, sadness and pleasure....

    [...]

Journal ArticleDOI
TL;DR: In this article, the authors look at how insights from some of the pioneers of neuroscience have begun to be integrated with today's technology, so bringing about the dawn of an era of brain and computer interfacing.
Abstract: 'Can the brain understand the brain?Can it understand the mind?'Such questions, here posed by neurophysiologist and 1981 Nobel laureate David Hubel, are under constant debate, but through the investigations of neurobiologists, psychologists, and physiologists, current knowledge and understanding has come a long way since brain exploration began hundreds of years BC. This article looks at how insights from some of the pioneers of neuroscience have begun to be integrated with today's technology, so bringing about the dawn of an era of brain and computer interfacing. One result has been brain–computer interfaces that can liberate the thoughts of those suffering from 'locked in' syndrome, by detection and interpretation of the brain's physiological signals. Also, manipulation of and support for the body's electrical and chemical signalling network has led to a variety of rehabilitation and therapeutic benefits, for example the possibility of giving sight to the blind. Brain–computer interfacing offers...

736 citations

Journal ArticleDOI
TL;DR: In this article, the authors examined whether reappraisalating stress-induced arousal could improve cardiovascular outcomes and decrease attentional bias for emotionally negative information, and found that participants who were instructed to reappraise their arousal exhibited more adaptive cardiovascular stress responses.
Abstract: Researchers have theorized that changing the way we think about our bodily responses can improve our physiological and cognitive reactions to stressful events. However, the underlying processes through which mental states improve downstream outcomes are not well-understood. To this end, we examined whether reappraising stress-induced arousal could improve cardiovascular outcomes and decrease attentional bias for emotionally-negative information. Participants were randomly assigned to either a reappraisal condition in which they were instructed to think about their physiological arousal during a stressful task as functional and adaptive, or to one of two control conditions: attention reorientation and no instructions. Relative to controls, participants instructed to reappraise their arousal exhibited more adaptive cardiovascular stress responses – increased cardiac efficiency and lower vascular resistance – and decreased attentional bias. Thus, reappraising arousal shows physiological and cognitive benefits. Implications for health and potential clinical applications are discussed.

347 citations

Journal ArticleDOI
01 Sep 2011
TL;DR: An extensive classification process was conducted using two feature-vector extraction techniques and a SVM classifier for six different classification scenarios in the valence/arousal space, confirming the efficacy of AsI as an index for the emotion elicitation evaluation.
Abstract: This paper aims at providing a novel method for evaluating the emotion elicitation procedures in an electroencephalogram (EEG)-based emotion recognition setup. By employing the frontal brain asymmetry theory, an index, namely asymmetry Index (AsI), is introduced, in order to evaluate this asymmetry. This is accomplished by a multidimensional directed information analysis between different EEG sites from the two opposite brain hemispheres. The proposed approach was applied to three-channel (Fp1, Fp2, and F3/F4 10/20 sites) EEG recordings drawn from 16 healthy right-handed subjects. For the evaluation of the efficiency of the AsI, an extensive classification process was conducted using two feature-vector extraction techniques and a SVM classifier for six different classification scenarios in the valence/arousal space. This resulted in classification results up to 62.58% for the user independent case and 94.40% for the user-dependent one, confirming the efficacy of AsI as an index for the emotion elicitation evaluation.

191 citations


"Detection of Stress in Human Brain" refers methods in this paper

  • ...In [1] the authors have used Support Vector Machine (SVM) as a classifier for emotion recognition....

    [...]