scholarly journals Detection of Mental Stress through EEG Signal in Virtual Reality Environment

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2840
Author(s):  
Dorota Kamińska ◽  
Krzysztof Smółka ◽  
Grzegorz Zwoliński
Keyword(s):  
Virtual Reality ◽  
Stress Level ◽  
Classification Performance ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Interactive Simulation ◽  
Eeg Signal ◽  
Conventional Machine ◽  
Electroencephalogram Eeg ◽  
Adult Volunteers

This paper investigates the use of an electroencephalogram (EEG) signal to classify a subject’s stress level while using virtual reality (VR). For this purpose, we designed an acquisition protocol based on alternating relaxing and stressful scenes in the form of a VR interactive simulation, accompanied by an EEG headset to monitor the subject’s psycho-physical condition. Relaxation scenes were developed based on scenarios created for psychotherapy treatment utilizing bilateral stimulation, while the Stroop test worked as a stressor. The experiment was conducted on a group of 28 healthy adult volunteers (office workers), participating in a VR session. Subjects’ EEG signal was continuously monitored using the EMOTIV EPOC Flex wireless EEG head cap system. After the session, volunteers were asked to re-fill questionnaires regarding the current stress level and mood. Then, we classified the stress level using a convolutional neural network (CNN) and compared the classification performance with conventional machine learning algorithms. The best results were obtained considering all brain waves (96.42%) with a multilayer perceptron (MLP) and Support Vector Machine (SVM) classifiers.

scholarly journals A Survey on Machine Learning Algorithms for Vision State Classification and Prediction Through Electroencephalogram (EEG) Signal

2020 ◽  
pp. 426-429
Author(s):  
Devipriya A ◽  
Brindha D ◽  
Kousalya A
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Problem Area ◽  
Machine Learning Algorithms ◽  
Eeg Signal ◽  
Ensemble Machine Learning ◽  
State Classification ◽  
Machine Learning Model ◽  
Knn Classification ◽  
Electroencephalogram Eeg

Eye state ID is a sort of basic time-arrangement grouping issue in which it is additionally a problem area in the late exploration. Electroencephalography (EEG) is broadly utilized in a vision state in order to recognize people perception form. Past examination was approved possibility of AI & measurable methodologies of EEG vision state arrangement. This research means to propose novel methodology for EEG vision state distinguishing proof utilizing Gradual Characteristic Learning (GCL) in light of neural organizations. GCL is a novel AI methodology which bit by bit imports and prepares includes individually. Past examinations have confirmed that such a methodology is appropriate for settling various example acknowledgment issues. Nonetheless, in these past works, little examination on GCL zeroed in its application to temporal-arrangement issues. Thusly, it is as yet unclear if GCL will be utilized for adapting the temporal-arrangement issues like EEG vision state characterization. Trial brings about this examination shows that, with appropriate element extraction and highlight requesting, GCL cannot just productively adapt to time-arrangement order issues, yet additionally display better grouping execution as far as characterization mistake rates in correlation with ordinary and some different methodologies. Vision state classification is performed and discussed with KNN classification and accuracy is enriched finally discussed the vision state classification with ensemble machine learning model.

scholarly journals EEG Based Classification of Long-Term Stress Using Psychological Labeling

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1886 ◽  
Author(s):  
Sanay Muhammad Umar Saeed ◽  
Syed Muhammad Anwar ◽  
Humaira Khalid ◽  
Muhammad Majid ◽  
Ulas Bagci
Keyword(s):  
Objective Measure ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Eeg Signal ◽  
Expert Evaluation ◽  
Clinical Practices ◽  
Control Groups ◽  
Alpha Asymmetry ◽  
And Control

Stress research is a rapidly emerging area in the field of electroencephalography (EEG) signal processing. The use of EEG as an objective measure for cost effective and personalized stress management becomes important in situations like the nonavailability of mental health facilities. In this study, long-term stress was classified with machine learning algorithms using resting state EEG signal recordings. The labeling for the stress and control groups was performed using two currently accepted clinical practices: (i) the perceived stress scale score and (ii) expert evaluation. The frequency domain features were extracted from five-channel EEG recordings in addition to the frontal and temporal alpha and beta asymmetries. The alpha asymmetry was computed from four channels and used as a feature. Feature selection was also performed to identify statistically significant features for both stress and control groups (via t-test). We found that support vector machine was best suited to classify long-term human stress when used with alpha asymmetry as a feature. It was observed that the expert evaluation-based labeling method had improved the classification accuracy by up to 85.20%. Based on these results, it is concluded that alpha asymmetry may be used as a potential bio-marker for stress classification, when labels are assigned using expert evaluation.

scholarly journals Automatic recognition of self-acknowledged limitations in clinical research literature

2018 ◽  
Vol 25 (7) ◽  
pp. 855-861 ◽  
Author(s):  
Halil Kilicoglu ◽  
Graciela Rosemblat ◽  
Mario Malički ◽  
Gerben ter Riet
Keyword(s):  
Machine Learning ◽  
Clinical Research ◽  
Binary Classification ◽  
Classification Performance ◽  
Research Literature ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Rule Based ◽  
Research Transparency

Abstract Objective To automatically recognize self-acknowledged limitations in clinical research publications to support efforts in improving research transparency. Methods To develop our recognition methods, we used a set of 8431 sentences from 1197 PubMed Central articles. A subset of these sentences was manually annotated for training/testing, and inter-annotator agreement was calculated. We cast the recognition problem as a binary classification task, in which we determine whether a given sentence from a publication discusses self-acknowledged limitations or not. We experimented with three methods: a rule-based approach based on document structure, supervised machine learning, and a semi-supervised method that uses self-training to expand the training set in order to improve classification performance. The machine learning algorithms used were logistic regression (LR) and support vector machines (SVM). Results Annotators had good agreement in labeling limitation sentences (Krippendorff’s α = 0.781). Of the three methods used, the rule-based method yielded the best performance with 91.5% accuracy (95% CI [90.1-92.9]), while self-training with SVM led to a small improvement over fully supervised learning (89.9%, 95% CI [88.4-91.4] vs 89.6%, 95% CI [88.1-91.1]). Conclusions The approach presented can be incorporated into the workflows of stakeholders focusing on research transparency to improve reporting of limitations in clinical studies.

scholarly journals Development of Automated BCI System to Assist the Physically Challenged Person Through Audio Announcement With Help of EEG Signal

2021 ◽  
Vol 16 ◽  
pp. 302-314
Author(s):  
U Rajashekhar ◽  
Neelappa Neelappa
Keyword(s):  
Virtual Reality ◽  
Machine Learning Algorithms ◽  
Prototype System ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Verilog Hdl ◽  
Wheelchair Users ◽  
Medical Needs ◽  
Physically Challenged ◽  
Control Feedback

Individuals face numerous challenges with many disorders, particularly when multiple disfunctions are diagnosed and especially for visually effected wheelchair users. This scenario, in reality creates in a degree of incapacity on the part of the wheelchair user in terms of performing simple activities. Based on their specific medical needs confined patients are treated in a modified method. Independent navigation is secured for individuals with vision and motor disabilities. There is a necessity for communication which justifies the use of virtual reality (VR) in this navigation situation. For the effective integration of locomotion besides, it must be under natural guidance. Electroencephalography (EEG), which uses random brain impulses, has made significant progress in the field of health. The custom of an automated audio announcement system modified to have the help of Virtual Reality (VR) and EEG for training of locomotion and individualised interaction of wheelchair users with visual disability is demonstrated in this study through an experiment. Enabling the patients who were otherwise deemed incapacitated to participate in social activities, as the aim was to have efficient connections. The natural control, feedback, stimuli, and protection these subsequent principles founded this project. Via properly conducted experiments, a multilayer computer rehabilitation system was created that integrated natural interaction assisted by EEG, which enabled the movements in the virtual environment and real wheelchair. For blind wheelchair operator patients this study involved of expounding the proper methodology. For educating the value of life and independence of blind wheelchair users, outcomes proven that VR with EEG signals has that potential. To protect their life straightaway and to report all these disputes, the military system should have high speed, more precise portable prototype device for nursing the soldier health, recognition of solider location and report about health sharing system to the concerned system. FPGA-based soldier’s health observing and position gratitude system is proposed in this paper. Reliant on heart rate which is centred on EEG signals the soldier health is observed in systematic bases. By emerging Verilog HDL programming language and executing on Artix-7 development FPGA board of part name XC7ACSG100t the whole work is approved in a Vivado Design Suite. Classification of different abnormalities, and cloud storage of EEG along with type of abnormalities, artifact elimination, abnormalities identification based on feature extraction, exist in the segment of suggested architecture. Irregularity circumstances are noticed through developed prototype system and alert the physically challenged (PHC) individual via audio announcement. An actual method for eradicating motion artefacts from EEG signals that have anomalies in the PHC person's brain has been established, and the established system is a portable device that can deliver differences in brain signal variation intensity. Primarily the EEG signals can be taken and the undesirable artifact can be detached, later structures can be mined by DWT these are the two stages through which artifact deletion can be completed. The anomalies in signal can be noticed and recognized by using machine learning algorithms known as Multirate SVM classifiers, when the features have been extracted using a combination of HMM and GMM. Intended for capable declaration about action taken by a blind person, these result signals are protected in storage devices and conveyed to the controller. Pretending daily motion schedules allows the pretentious EEG signals to be caught. Aimed at the validation of planned system, the database can be used and continued with numerous recorded signals of EEG. The projected strategy executes better in terms of re-storing theta, delta, alpha, and beta (TDAB) complexes of the original EEG with less alteration and a higher signal to noise ratio (SNR) value of the EEG signal which illustrates in the quantitative analysis. The projected method used Verilog HDL and MATLAB software for both formation and authorization of results in order to yield improved results. Since from the achieved results, it is initiated that 32% enhancement in SNR, 14% in MSE and 65% enhancement in recognition of anomalies, hence design is effectively certified and proved for standard EEG signals datasets on FPGA.

scholarly journals EEG signal classification for drowsiness detection using wavelet transform and support vector machine

2021 ◽  
Vol 10 (2) ◽  
pp. 501
Author(s):  
Novie Theresia Br. Pasaribu ◽  
Timotius Halim ◽  
Ratnadewi Ratnadewi ◽  
Agus Prijono
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
Support Vector ◽  
Eeg Signal ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
Electroencephalogram Eeg ◽  
Behavioral Methods ◽  
Physiological Method ◽  
Fold Cross Validation

<span id="docs-internal-guid-ed628156-7fff-8934-2369-94f011b043ca"><span>There are several categories to detect and measure driver drowsiness such as physiological methods, subjective methods and behavioral methods. The most objective method for drowsiness detection is the physiological method. One of the physiological methods used is an electroencephalogram (EEG). In this research wavelet transform is used as a feature extraction and using support vector machine (SVM) as a classifier. We proposed an experiment of retrieval data which is designed by using modified-EAR and EEG signal. From the SVM training process, with the 5-fold cross validation, Quadratic kernel has the highest accuracy 84.5% then others. In testing Driving-2 process 7 respondents were detected as drowsiness class, and 3 respondents were detected as awake class. In the testing of Driving-3 process, 6 respondents were detected as drowsiness class, and 4 respondents were detected as awake class. </span></span>

scholarly journals Distinguishing Different Emotions Evoked by Music via Electroencephalographic Signals

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Yimin Hou ◽  
Shuaiqi Chen
Keyword(s):  
Recognition Accuracy ◽  
Feature Selection Method ◽  
Support Vector ◽  
Eeg Signal ◽  
Feature Sets ◽  
Signal Features ◽  
Correlation Based Feature Selection ◽  
Series Of Experiments ◽  
Electroencephalogram Eeg ◽  
Eeg Features

Music can evoke a variety of emotions, which may be manifested by distinct signals on the electroencephalogram (EEG). Many previous studies have examined the associations between specific aspects of music, including the subjective emotions aroused, and EEG signal features. However, no study has comprehensively examined music-related EEG features and selected those with the strongest potential for discriminating emotions. So, this paper conducted a series of experiments to identify the most influential EEG features induced by music evoking different emotions (calm, joy, sad, and angry). We extracted 27-dimensional features from each of 12 electrode positions then used correlation-based feature selection method to identify the feature set most strongly related to the original features but with lowest redundancy. Several classifiers, including Support Vector Machine (SVM), C4.5, LDA, and BPNN, were then used to test the recognition accuracy of the original and selected feature sets. Finally, results are analyzed in detail and the relationships between selected feature set and human emotions are shown clearly. Through the classification results of 10 random examinations, it could be concluded that the selected feature sets of Pz are more effective than other features when using as the key feature set to classify human emotion statues.

scholarly journals Identification of Risk Factors for Suicidal Ideation and Attempt Based on Machine Learning Algorithms: A Longitudinal Survey in Korea (2007–2019)

2021 ◽  
Vol 18 (23) ◽  
pp. 12772
Author(s):  
Junggu Choi ◽  
Seoyoung Cho ◽  
Inhwan Ko ◽  
Sanghoon Han
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Suicidal Ideation ◽  
Suicide Risk ◽  
Large Scale ◽  
Learning Algorithms ◽  
Sociodemographic Factors ◽  
Classification Performance ◽  
Machine Learning Algorithms ◽  
Support Vector

Investigating suicide risk factors is critical for socioeconomic and public health, and many researchers have tried to identify factors associated with suicide. In this study, the risk factors for suicidal ideation were compared, and the contributions of different factors to suicidal ideation and attempt were investigated. To reflect the diverse characteristics of the population, the large-scale and longitudinal dataset used in this study included both socioeconomic and clinical variables collected from the Korean public. Three machine learning algorithms (XGBoost classifier, support vector classifier, and logistic regression) were used to detect the risk factors for both suicidal ideation and attempt. The importance of the variables was determined using the model with the best classification performance. In addition, a novel risk-factor score, calculated from the rank and importance scores of each variable, was proposed. Socioeconomic and sociodemographic factors showed a high correlation with risks for both ideation and attempt. Mental health variables ranked higher than other factors in suicidal attempts, posing a relatively higher suicide risk than ideation. These trends were further validated using the conditions from the integrated and yearly dataset. This study provides novel insights into suicidal risk factors for suicidal ideations and attempts.

scholarly journals Generalising Better: Applying Deep Learning to Integrate Deleteriousness Prediction Scores for Whole-Exome SNV Studies

2017 ◽  
Author(s):  
Ilia Korvigo ◽  
Andrey Afanasyev ◽  
Nikolay Romashchenko ◽  
Mihail Skoblov
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Hardware Acceleration ◽  
Scoring Systems ◽  
Classification Performance ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Whole Exome ◽  
Generalisation Ability

AbstractMany automatic classifiers were introduced to aid inference of phenotypical effects of uncategorised nsSNVs (nonsynonymous Single Nucleotide Variations) in theoretical and medical applications. Lately, several meta-estimators have been proposed that combine different predictors, such as PolyPhen and SIFT, to integrate more information in a single score. Although many advances have been made in feature design and machine learning algorithms used, the shortage of high-quality reference data along with the bias towards intensively studied in vitro models call for improved generalisation ability in order to further increase classification accuracy and handle records with insufficient data. Since a meta-estimator basically combines different scoring systems with highly complicated nonlinear relationships, we investigated how deep learning (supervised and unsupervised), which is particularly efficient at discovering hierarchies of features, can improve classification performance. While it is believed that one should only use deep learning for high-dimensional input spaces and other models (logistic regression, support vector machines, Bayesian classifiers, etc) for simpler inputs, we still believe that the ability of neural networks to discover intricate structure in highly heterogenous datasets can aid a meta-estimator. We compare the performance with various popular predictors, many of which are recommended by the American College of Medical Genetics and Genomics (ACMG), as well as available deep learning-based predictors. Thanks to hardware acceleration we were able to use a computationally expensive genetic algorithm to stochastically optimise hyper-parameters over many generations. Overfitting was hindered by noise injection and dropout, limiting coadaptation of hidden units. Although we stress that this work was not conceived as a tool comparison, but rather an exploration of the possibilities of deep learning application in ensemble scores, our results show that even relatively simple modern neural networks can significantly improve both prediction accuracy and coverage. We provide open-access to our finest model at http://score.generesearch.ru.

An Investigation Screen for Sleep Disorders Using AI/ML

2021 ◽  
pp. 161-183
Author(s):  
Shilpa Hiremath ◽  
Chandra Prabha R. ◽  
Sushil Kumar I.
Keyword(s):  
Neural Network ◽  
Sleep Disorders ◽  
Deep Neural Network ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Age Group ◽  
Eeg Signal ◽  
Factors Affecting ◽  
Detailed Review

In this chapter, the authors have discussed a detailed review on sleep, sleep disorders, and their diagnosis. This chapter provides an insight study of sleep, sleep illness characterized by The International Classification of Sleep Disorders (ICSD), factors affecting sleep, and types of sleep based on age group. Artificial intelligence and machine learning algorithms are also applied in recognizing sleep disorders based on EEG signal attributes. It also highlights the classification of insomnia using different classifiers such as support vector machine, decision tree, and deep neural network.

scholarly journals Mood Detection from Physical and Neurophysical Data Using Deep Learning Models

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Zeynep Hilal Kilimci ◽  
Aykut Güven ◽  
Mitat Uysal ◽  
Selim Akyokus
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Behavioral Data ◽  
Support Vector ◽  
Learning Models ◽  
Physical Data ◽  
Conventional Machine

Nowadays, smart devices as a part of daily life collect data about their users with the help of sensors placed on them. Sensor data are usually physical data but mobile applications collect more than physical data like device usage habits and personal interests. Collected data are usually classified as personal, but they contain valuable information about their users when it is analyzed and interpreted. One of the main purposes of personal data analysis is to make predictions about users. Collected data can be divided into two major categories: physical and behavioral data. Behavioral data are also named as neurophysical data. Physical and neurophysical parameters are collected as a part of this study. Physical data contains measurements of the users like heartbeats, sleep quality, energy, movement/mobility parameters. Neurophysical data contain keystroke patterns like typing speed and typing errors. Users’ emotional/mood statuses are also investigated by asking daily questions. Six questions are asked to the users daily in order to determine the mood of them. These questions are emotion-attached questions, and depending on the answers, users’ emotional states are graded. Our aim is to show that there is a connection between users’ physical/neurophysical parameters and mood/emotional conditions. To prove our hypothesis, we collect and measure physical and neurophysical parameters of 15 users for 1 year. The novelty of this work to the literature is the usage of both combinations of physical and neurophysical parameters. Another novelty is that the emotion classification task is performed by both conventional machine learning algorithms and deep learning models. For this purpose, Feedforward Neural Network (FFNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) neural network are employed as deep learning methodologies. Multinomial Naïve Bayes (MNB), Support Vector Regression (SVR), Decision Tree (DT), Random Forest (RF), and Decision Integration Strategy (DIS) are evaluated as conventional machine learning algorithms. To the best of our knowledge, this is the very first attempt to analyze the neurophysical conditions of the users by evaluating deep learning models for mood analysis and enriching physical characteristics with neurophysical parameters. Experiment results demonstrate that the utilization of deep learning methodologies and the combination of both physical and neurophysical parameters enhances the classification success of the system to interpret the mood of the users. A wide range of comparative and extensive experiments shows that the proposed model exhibits noteworthy results compared to the state-of-art studies.

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