Relaxation Degree Analysis Using Frontal Electroencephalogram Under Virtual Reality Relaxation Scenes

Increasing social pressure enhances the psychological burden on individuals, and the severity of depression can no longer be ignored. The characteristics of high immersion and interactivity enhance virtual reality (VR) application in psychological therapy. Many studies have verified the effectiveness of VR relaxation therapy, although a few have performed a quantitative study on relaxation state (R-state). To confirm the effectiveness of VR relaxation and quantitatively assess relaxation, this study confirmed the effectiveness of the VR sightseeing relaxation scenes using subjective emotion scale and objective electroencephalogram (EEG) data from college students. Moreover, some EEG features with significant consistent differences after they watched the VR scenes were detected including the energy ratio of the alpha wave, gamma wave, and differential asymmetry. An R-state regression model was then built using the model stacking method for optimization, of which random forest regression, AdaBoost, gradient boosting (GB), and light GB were adopted as the first level, while linear regression and support vector machine were applied at the second level. The leave-one-subject-out method for cross-validation was used to evaluate the results, where the mean accuracy of the framework achieved 81.46%. The significantly changed features and the R-state model with over 80% accuracy have laid a foundation for further research on relaxation interaction systems. Moreover, the VR relaxation therapy was applied to the clinical treatment of patients with depression and achieved preliminary good results, which might provide a possible method for non-drug treatment of patients with depression.

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Practical Artifact Removal Brain-Computer Interface System

Advances in Bioinformatics and Biomedical Engineering - Emerging Theory and Practice in Neuroprosthetics ◽

10.4018/978-1-4666-6094-6.ch013 ◽

2014 ◽

pp. 265-277

Author(s):

Wei-Yen Hsu

Keyword(s):

Support Vector Machine ◽

Classification Accuracy ◽

Brain Computer Interface ◽

Computer Interface ◽

Support Vector ◽

Artifact Removal ◽

Eeg Data ◽

Interface System ◽

Electroencephalogram Eeg ◽

Bci System

In this chapter, a practical artifact removal Brain-Computer Interface (BCI) system for single-trial Electroencephalogram (EEG) data is proposed for applications in neuroprosthetics. Independent Component Analysis (ICA) combined with the use of a correlation coefficient is proposed to remove the EOG artifacts automatically, which can further improve classification accuracy. The features are then extracted from wavelet transform data by means of the proposed modified fractal dimension. Finally, Support Vector Machine (SVM) is used for the classification. When compared with the results obtained without using the EOG signal elimination, the proposed BCI system achieves promising results that will be effectively applied in neuroprosthetics.

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Drowsiness Estimation Using Electroencephalogram and Recurrent Support Vector Regression

Information ◽

10.3390/info10060217 ◽

2019 ◽

Vol 10 (6) ◽

pp. 217 ◽

Cited By ~ 1

Author(s):

Izzat Aulia Akbar ◽

Tomohiko Igasaki

Keyword(s):

Support Vector Regression ◽

Estimation Method ◽

Electrode Site ◽

Estimation Methods ◽

Support Vector ◽

Physical Damage ◽

Prediction Ability ◽

Estimation Performance ◽

Eeg Data ◽

Electroencephalogram Eeg

As a cause of accidents, drowsiness can cause economical and physical damage. A range of drowsiness estimation methods have been proposed in previous studies to aid accident prevention and address this problem. However, none of these methods are able to improve their estimation ability as the length of time or number of trials increases. Thus, in this study, we aim to find an effective drowsiness estimation method that is also able to improve its prediction ability as the subject’s activity increases. We used electroencephalogram (EEG) data to estimate drowsiness, and the Karolinska sleepiness scale (KSS) for drowsiness evaluation. Five parameters (α, β/α, (θ+α)/β, activity, and mobility) from the O1 electrode site were selected. By combining these parameters and KSS, we demonstrate that a typical support vector regression (SVR) algorithm can estimate drowsiness with a correlation coefficient (R2) of up to 0.64 and a root mean square error (RMSE) of up to 0.56. We propose a “recurrent SVR” (RSVR) method with improved estimation performance, as highlighted by an R2 value of up to 0.83, and an RMSE of up to 0.15. These results suggest that in addition to being able to estimate drowsiness based on EEG data, RSVR is able to improve its drowsiness estimation performance.

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Automated Recognition of Epileptic EEG States Using a Combination of Symlet Wavelet Processing, Gradient Boosting Machine, and Grid Search Optimizer

Sensors ◽

10.3390/s19020219 ◽

2019 ◽

Vol 19 (2) ◽

pp. 219 ◽

Cited By ~ 9

Author(s):

Xiashuang Wang ◽

Guanghong Gong ◽

Ni Li

Keyword(s):

Classification Accuracy ◽

Classification Scheme ◽

Normal Subjects ◽

Gradient Boosting ◽

Support Vector ◽

Grid Search ◽

Automated Recognition ◽

Eeg Data ◽

Gradient Boosting Machine

Automatic recognition methods for non-stationary electroencephalogram (EEG) data collected from EEG sensors play an essential role in neurological detection. The integrated approaches proposed in this study consist of Symlet wavelet processing, a gradient boosting machine, and a grid search optimizer for a three-class classification scheme for normal subjects, intermittent epilepsy, and continuous epilepsy. Fourth-order Symlet wavelets are adopted to decompose the EEG data into five frequencies sub-bands, such as gamma, beta, alpha, theta, and delta, whose statistical features were computed and used as classification features. The grid search optimizer is used to automatically find the optimal parameters for training the classifier. The classification accuracy of the gradient boosting machine was compared with that of a conventional support vector machine and a random forest classifier constructed according to previous descriptions. Multiple performance indices were used to evaluate the proposed classification scheme, which provided better classification accuracy and detection effectiveness than has been recently reported in other studies on three-class classification of EEG data.

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InstanceEasyTL: An Improved Transfer-Learning Method for EEG-Based Cross-Subject Fatigue Detection

Sensors ◽

10.3390/s20247251 ◽

2020 ◽

Vol 20 (24) ◽

pp. 7251

Author(s):

Hong Zeng ◽

Jiaming Zhang ◽

Wael Zakaria ◽

Fabio Babiloni ◽

Borghini Gianluca ◽

...

Keyword(s):

Transfer Learning ◽

Support Vector ◽

Learning Method ◽

Learning Models ◽

Eeg Signals ◽

Eeg Data ◽

State Detection ◽

Fatigue Detection ◽

Electroencephalogram Eeg ◽

Machine Learning Models

Electroencephalogram (EEG) is an effective indicator for the detection of driver fatigue. Due to the significant differences in EEG signals across subjects, and difficulty in collecting sufficient EEG samples for analysis during driving, detecting fatigue across subjects through using EEG signals remains a challenge. EasyTL is a kind of transfer-learning model, which has demonstrated better performance in the field of image recognition, but not yet been applied in cross-subject EEG-based applications. In this paper, we propose an improved EasyTL-based classifier, the InstanceEasyTL, to perform EEG-based analysis for cross-subject fatigue mental-state detection. Experimental results show that InstanceEasyTL not only requires less EEG data, but also obtains better performance in accuracy and robustness than EasyTL, as well as existing machine-learning models such as Support Vector Machine (SVM), Transfer Component Analysis (TCA), Geodesic Flow Kernel (GFK), and Domain-adversarial Neural Networks (DANN), etc.

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Realtime Recognition of Attention State by Complexity Analysis of Electroencephalogram

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.385-386.1457 ◽

2013 ◽

Vol 385-386 ◽

pp. 1457-1460

Author(s):

Li Yu Huang ◽

Hong Juan Ma ◽

Lang Jin ◽

Rong Lu

Keyword(s):

Support Vector Machine ◽

Complexity Analysis ◽

Wavelet Packet ◽

Approximate Entropy ◽

Support Vector ◽

Eeg Rhythms ◽

Average Performance ◽

Novel Approach ◽

Eeg Data ◽

Electroencephalogram Eeg

The object of this paper is presenting a novel approach to classify the attention state and non-attention state. Firstly, the raw recorded electroencephalogram (EEG) data were decomposed by the algorithm of wavelet packet, several main EEG rhythms were extracted; then a complexity measure of these rhythm signal, approximate entropy (ApEn) was calculated respectively, and the values were used as input vector of a trained support vector machine (SVM), the output of this SVM will be the result of classification. The average performance obtained for the proposed scheme in classification is: sensitivity 73.7%, specificity 71.4% and accuracy 72.5%.

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Epilepsy EEG Classification Based on Convolution Support Vector Machine

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2021.3259 ◽

2021 ◽

Vol 11 (1) ◽

pp. 25-32

Author(s):

Qi Xin ◽

Shaohai Hu ◽

Shuaiqi Liu ◽

Xiaole Ma ◽

Hui Lv ◽

...

Keyword(s):

Support Vector Machine ◽

Focal Epilepsy ◽

Principal Component ◽

Support Vector ◽

Eeg Signals ◽

Eeg Classification ◽

Eeg Data ◽

Epilepsy Diagnosis ◽

Electroencephalogram Eeg

Clinical Electroencephalogram (EEG) data is of great significance to realize automatable detection, recognition and diagnosis to reduce the valuable diagnosis time. To make a classification of epilepsy, we constructed convolution support vector machine (CSVM) by integrating the advantages of convolutional neural networks (CNN) and support vector machine (SVM). To distinguish the focal and non-focal epilepsy EEG signals, we firstly reduced the dimensionality of EEG signals by using principal component analysis (PCA). After that, we classified the epilepsy EEG signals by the CSVM. The accuracy, sensitivity and specificity of our method reach up to 99.56%, 99.72% and 99.52% respectively, which are competitive than the widely acceptable algorithms. The proposed automatic end to end epilepsy EEG signals classification algorithm provides a better reference for clinical epilepsy diagnosis.

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A Study on Sensitive Bands of EEG Data under Different Mental Workloads

Algorithms ◽

10.3390/a12070145 ◽

2019 ◽

Vol 12 (7) ◽

pp. 145

Author(s):

Hongquan Qu ◽

Zhanli Fan ◽

Shuqin Cao ◽

Liping Pang ◽

Hao Wang ◽

...

Keyword(s):

Feature Extraction ◽

Support Vector ◽

Svm Classifier ◽

Eeg Signals ◽

Power Spectral ◽

Eeg Data ◽

Vector Machines ◽

Full Band ◽

Electroencephalogram Eeg

Electroencephalogram (EEG) signals contain a lot of human body performance information. With the development of the brain–computer interface (BCI) technology, many researchers have used the feature extraction and classification algorithms in various fields to study the feature extraction and classification of EEG signals. In this paper, the sensitive bands of EEG data under different mental workloads are studied. By selecting the characteristics of EEG signals, the bands with the highest sensitivity to mental loads are selected. In this paper, EEG signals are measured in different load flight experiments. First, the EEG signals are preprocessed by independent component analysis (ICA) to remove the interference of electrooculogram (EOG) signals, and then the power spectral density and energy are calculated for feature extraction. Finally, the feature importance is selected based on Gini impurity. The classification accuracy of the support vector machines (SVM) classifier is verified by comparing the characteristics of the full band with the characteristics of the β band. The results show that the characteristics of the β band are the most sensitive in EEG data under different mental workloads.

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A Pilot Study on Electroencephalogram-based Evaluation of Visually Induced Motion Sickness

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2020.64.2.020501 ◽

2020 ◽

Vol 64 (2) ◽

pp. 20501-1-20501-10

Author(s):

Ran Liu ◽

Miao Xu ◽

Yanzhen Zhang ◽

Eli Peli ◽

Alex D. Hwang

Keyword(s):

Virtual Reality ◽

Standard Deviation ◽

Motion Sickness ◽

Driving Simulator ◽

Visually Induced Motion Sickness ◽

Power Spectral ◽

Wearable Eeg ◽

Eeg Data ◽

Induced Motion ◽

Electroencephalogram Eeg

Abstract The most prominent problem in virtual reality (VR) technology is that users may experience motion-sickness-like symptoms when they immerse into a VR environment. These symptoms are recognized as visually induced motion sickness (VIMS) or virtual reality motion sickness. The objectives of this study were to investigate the association between the electroencephalogram (EEG) and subjectively rated VIMS level (VIMSL) and find EEG markers for VIMS evaluation. A VR-based vehicle-driving simulator was used to induce VIMS symptoms, and a wearable EEG device with four electrodes (the Muse) was used to collect EEG data. The results suggest that individual tolerance, susceptibility, and recoverability to VIMS varied largely among subjects; the following markers were shown to be significantly different from no-VIMS and VIMS states (P < 0.05): (1) means of gravity frequency (GF) for theta@FP1, alpha@TP9, alpha@FP2, alpha@TP10, and beta@FP1; (2) standard deviation of GF for alpha@TP9, alpha@FP1, alpha@FP2, alpha@TP10, and alpha@(FP2‐FP1); (3) standard deviation of power spectral entropy for FP1; (4) means of Kolmogorov complexity (KC) for TP9, FP1, and FP2. These results also demonstrate that it is feasible to perform VIMS evaluation using an EEG device with a few electrodes.

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EEG-Based Classification of the Driver Alertness State

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2020-3091 ◽

2020 ◽

Vol 6 (3) ◽

pp. 353-356

Author(s):

Martin Golz ◽

Sebastian Thomas ◽

Adolf Schenka

Keyword(s):

Machine Learning ◽

Gradient Boosting ◽

Support Vector ◽

Weighting Matrix ◽

Machine Learning Methods ◽

Young Drivers ◽

Eeg Data ◽

Vector Machines ◽

Generalized Matrix

AbstractGMLVQ (Generalized Matrix Relevance Learning Vector Quantization) is a method of machine learning with an adaptive metric. While training, the prototype vectors as well as the weight matrix of the metric are adapted simultaneously. The method is presented in more detail and compared with other machine learning methods employing a fixed metric. It was investigated how accurately the methods can assign the 6-channel EEG of 25 young drivers, who drove overnight in the simulation lab, to the two classes of mild and severe drowsiness. Results of cross-validation show that GMLVQ is at 81.7 ± 1.3 % mean classification accuracy. It is not as accurate as support-vector machines (SVM) and gradient boosting machines (GBM) and cannot exploit the potential of learning adaptive metrics in the case of EEG data. However, information is provided on the relevance of each signal feature from the weighting matrix.

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Detection of Mental Stress through EEG Signal in Virtual Reality Environment

Electronics ◽

10.3390/electronics10222840 ◽

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.

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