scholarly journals EEG Mental Stress Assessment Using Hybrid Multi-Domain Feature Sets of Functional Connectivity Network and Time-Frequency Features

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6300
Author(s):  
Ala Hag ◽  
Dini Handayani ◽  
Thulasyammal Pillai ◽  
Teddy Mantoro ◽  
Mun Hou Kit ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Mental Stress ◽  
Mental Arithmetic ◽  
Support Vector ◽  
Svm Classifier ◽  
Amylase Level ◽  
Negative Consequences ◽  
Eeg Signals ◽  
Arithmetic Task ◽  
Time Frequency

Exposure to mental stress for long period leads to serious accidents and health problems. To avoid negative consequences on health and safety, it is very important to detect mental stress at its early stages, i.e., when it is still limited to acute or episodic stress. In this study, we developed an experimental protocol to induce two different levels of stress by utilizing a mental arithmetic task with time pressure and negative feedback as the stressors. We assessed the levels of stress on 22 healthy subjects using frontal electroencephalogram (EEG) signals, salivary alpha-amylase level (AAL), and multiple machine learning (ML) classifiers. The EEG signals were analyzed using a fusion of functional connectivity networks estimated by the Phase Locking Value (PLV) and temporal and spectral domain features. A total of 210 different features were extracted from all domains. Only the optimum multi-domain features were used for classification. We then quantified stress levels using statistical analysis and seven ML classifiers. Our result showed that the AAL level was significantly increased (p < 0.01) under stress condition in all subjects. Likewise, the functional connectivity network demonstrated a significant decrease under stress, p < 0.05. Moreover, we achieved the highest stress classification accuracy of 93.2% using the Support Vector Machine (SVM) classifier. Other classifiers produced relatively similar results.

Epileptic EEG Identification via LBP Operators on Wavelet Coefficients

2018 ◽  
Vol 28 (08) ◽  
pp. 1850010 ◽  
Author(s):  
Qi Yuan ◽  
Weidong Zhou ◽  
Fangzhou Xu ◽  
Yan Leng ◽  
Dongmei Wei
Keyword(s):  
Recognition System ◽  
Support Vector ◽  
Svm Classifier ◽  
Automatic Identification ◽  
Wavelet Coefficients ◽  
Eeg Signals ◽  
Eeg Classification ◽  
Time Frequency ◽  
Ictal Eeg ◽  
Uniform Lbp

The automatic identification of epileptic electroencephalogram (EEG) signals can give assistance to doctors in diagnosis of epilepsy, and provide the higher security and quality of life for people with epilepsy. Feature extraction of EEG signals determines the performance of the whole recognition system. In this paper, a novel method using the local binary pattern (LBP) based on the wavelet transform (WT) is proposed to characterize the behavior of EEG activities. First, the WT is employed for time–frequency decomposition of EEG signals. After that, the “uniform” LBP operator is carried out on the wavelet-based time–frequency representation. And the generated histogram is regarded as EEG feature vector for the quantification of the textural information of its wavelet coefficients. The LBP features coupled with the support vector machine (SVM) classifier can yield the satisfactory recognition accuracies of 98.88% for interictal and ictal EEG classification and 98.92% for normal, interictal and ictal EEG classification on the publicly available EEG dataset. Moreover, the numerical results on another large size EEG dataset demonstrate that the proposed method can also effectively detect seizure events from multi-channel raw EEG data. Compared with the standard LBP, the “uniform” LBP can obtain the much shorter histogram which greatly reduces the computational burden of classification and enables it to detect ictal EEG signals in real time.

scholarly journals Decision Fusion of EEG and fNIRS Signals

2019 ◽  
Author(s):  
Fares Al-Shargie
Keyword(s):  
Mental Stress ◽  
Near Infrared ◽  
Time Pressure ◽  
Functional Neuroimaging ◽  
Mental Arithmetic ◽  
Decision Fusion ◽  
Support Vector ◽  
Functional Near Infrared Spectroscopy ◽  
Arithmetic Task ◽  
Mental Arithmetic Task

In this study, we investigated the use of multimodal functional neuroimaging in detecting mental stress on the prefrontal cortex (PFC). We recorded Electroencephalography (EEG) and functional Near Infrared Spectroscopy (fNIRS) simultaneously from 20-subjects performing mental arithmetic task under control and stress conditions. Stress was induced in this study based on two established stressors – time pressure and negative feedback about peer performance. We explored decision fusion by using support vector machine classifier for each modality, and optimizing the classifiers based on Receiver Operating Characteristic (ROC) curve values. Experiment results revealed significant reduction in alpha rhythm and mean change in concentration of oxygenated hemoglobin at PFC when stressed, p&lt;0.001 and 0.0001 respectively. The decision fusion improved significantly the detection rate of mental stress by +7.76% and +10.57%, when compared to sole modality of EEG and fNIRS, respectively.

scholarly journals Fusion of fNIRS and EEG Signals: Mental Stress Study

2019 ◽  
Author(s):  
Fares Al-Shargie
Keyword(s):  
Mental Stress ◽  
Detection Rate ◽  
Near Infrared ◽  
Time Pressure ◽  
Individual Performance ◽  
Support Vector ◽  
Eeg Signals ◽  
Functional Near Infrared Spectroscopy ◽  
Arithmetic Task ◽  
Novel Approach

Fusion of Functional Near infrared Spectroscopy (fNIRS) and Electroencephalograph (EEG) is a novel approach. This study aims in improving the detection rate of mental stress using the complementary nature of fNIRS and EEG neuroimaging modality. Simultaneous measurements of fNIRS and EEG signals were conducted on 12 subjects while solving arithmetic problems under two different conditions (control and stress). The stress in this work was based on arithmetic task difficulty, time pressure and negative feedback of individual performance. The study demonstrated significant reduction in the concentration of oxygenated hemoglobin (p=0.0032) and alpha rhythm power (p=0.0213) on the PFC under stress condition. Specifically, the right PFC and dorsolateral PFC were highly sensitive to mental stress. Using support vector machine (SVM), the mean detection rate of mental stress was calculated as 91%, 95% and 98% using fNIRS, EEG and fusion of fNIRS and EEG signals respectively.

scholarly journals Classification of Relaxation and Concentration Mental States with EEG

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 187
Author(s):  
Shingchern D. You
Keyword(s):  
Mental States ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Consumer Market ◽  
Time Frequency ◽  
Average Accuracy ◽  
Spectral Components ◽  
Machine Interface

In this paper, we study the use of EEG (Electroencephalography) to classify between concentrated and relaxed mental states. In the literature, most EEG recording systems are expensive, medical-graded devices. The expensive devices limit the availability in a consumer market. The EEG signals are obtained from a toy-grade EEG device with one channel of output data. The experiments are conducted in two runs, with 7 and 10 subjects, respectively. Each subject is asked to silently recite a five-digit number backwards given by the tester. The recorded EEG signals are converted to time-frequency representations by the software accompanying the device. A simple average is used to aggregate multiple spectral components into EEG bands, such as α, β, and γ bands. The chosen classifiers are SVM (support vector machine) and multi-layer feedforward network trained individually for each subject. Experimental results show that features, with α+β+γ bands and bandwidth 4 Hz, the average accuracy over all subjects in both runs can reach more than 80% and some subjects up to 90+% with the SVM classifier. The results suggest that a brain machine interface could be implemented based on the mental states of the user even with the use of a cheap EEG device.

Estimation of Cognitive Distraction from Driver Gazing

2017 ◽  
Vol 9 (2) ◽  
pp. 50-60 ◽  
Author(s):  
Akira Yoshizama ◽  
Hiroyuki Nishiyama ◽  
Hirotoshi Iwasaki ◽  
Fumio Mizoguchi
Keyword(s):  
Qualitative Data ◽  
Mental Arithmetic ◽  
Support Vector ◽  
Simulation Environment ◽  
Time Intervals ◽  
Arithmetic Task ◽  
Task Load ◽  
Cognitive Distraction ◽  
Mental Arithmetic Task ◽  
Using Data

In their study, the authors sought to generate rules for cognitive distractions of car drivers using data from a driving simulation environment. They collected drivers' eye-movement and driving data from 18 research participants using a simulator. Each driver drove the same 15-minute course two times. The first drive was normal driving (no-load driving), and the second drive was driving with a mental arithmetic task (load driving), which the authors defined as cognitive-distraction driving. To generate rules of distraction driving using a machine-learning tool, they transformed the data at constant time intervals to generate qualitative data for learning. Finally, the authors generated rules using a Support Vector Machine (SVM).

scholarly journals Classification of Emotional States Inparkinson’s Disease Patients Using Time,Frequency and Time-Frequency Analysis

2021 ◽  
Author(s):  
Rejith K.N ◽  
Kamalraj Subramaniam ◽  
Ayyem Pillai Vasudevan Pillai ◽  
Roshini T V ◽  
Renjith V. Ravi ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Classification Accuracy ◽  
Machine Learning Algorithms ◽  
Signal Frequency ◽  
Spectral Energy ◽  
Support Vector ◽  
Svm Classifier ◽  
Time Frequency ◽  
Energy Entropy ◽  
Teager Energy

Abstract In this work, PD patients and healthy individuals were categorized with machine-learning algorithms. EEG signals associated with six different emotions, (Happiness(E1), Sadness(E2), Fear(E3), Anger(E4), Surprise,(E5) and disgust(E6)) were used for the study. EEG data were collected from 20 PD patients and 20 normal controls using multimodal stimuli. Different features were used to categorize emotional data. Emotional recognition in Parkinson’s disease (PD) has been investigated in three domains namely, time, frequency and time frequency using Entropy, Energy-Entropy and Teager Energy-Entropy features. Three classifiers namely, K-Nearest Neighbor Algorithm, Support Vector Machine and Probabilistic Neural Network were used to observethe classification results. Emotional EEG stimuli such as anger, surprise, happiness, sadness, fear, and disgust were used to categorize PD patients and healthy controls (HC). For each EEG signal, frequency features corresponding to alpha, beta and gamma bands were obtained for nine feature extraction methods (Entropy, Energy Entropy, Teager Energy Entropy, Spectral Entropy, Spectral Energy-Entropy, Spectral Teager Energy-Entropy, STFT Entropy, STFT Energy-Entropy and STFT Teager Energy-Entropy). From the analysis, it is observed that the entropy feature in frequency domain performs evenly well (above 80 %) for all six emotions with KNN. Classification results shows that using the selected energy entropy combination feature in frequency domain provides highest accuracy for all emotions except E1 and E2 for KNN and SVM classifier, whereas other features give accuracy values of above 60% for most emotions.It is also observed that emotion E1 gives above 90 % classification accuracy for all classifiers in time domain.In frequency domain also, emotion E1 gives above 90% classification accuracy using PNN classifier.

VENTRICULAR TACHYCARDIA AND FIBRILLATION DETECTION USING DWT AND DECISION TREE CLASSIFIER

2019 ◽  
Vol 19 (03) ◽  
pp. 1950008
Author(s):  
MONALISA MOHANTY ◽  
PRADYUT BISWAL ◽  
SUKANTA SABUT
Keyword(s):  
Ventricular Tachycardia ◽  
Decision Tree ◽  
Ventricular Arrhythmias ◽  
Ventricular Tachyarrhythmia ◽  
Support Vector ◽  
Svm Classifier ◽  
Discrete Wavelet ◽  
Time Frequency ◽  
Tree Classifier ◽  
Frequency Features

Ventricular tachycardia (VT) and ventricular fibrillation (VF) are the life-threatening ventricular arrhythmias that require treatment in an emergency. Detection of VT and VF at an early stage is crucial for achieving the success of the defibrillation treatment. Hence an automatic system using computer-aided diagnosis tool is helpful in detecting the ventricular arrhythmias in electrocardiogram (ECG) signal. In this paper, a discrete wavelet transform (DWT) was used to denoise and decompose the ECG signals into different consecutive frequency bands to reduce noise. The methodology was tested using ECG data from standard CU ventricular tachyarrhythmia database (CUDB) and MIT-BIH malignant ventricular ectopy database (VFDB) datasets of PhysioNet databases. A set of time-frequency features consists of temporal, spectral, and statistical were extracted and ranked by the correlation attribute evaluation with ranker search method in order to improve the accuracy of detection. The ranked features were classified for VT and VF conditions using support vector machine (SVM) and decision tree (C4.5) classifier. The proposed DWT based features yielded the average sensitivity of 98%, specificity of 99.32%, and accuracy of 99.23% using a decision tree (C4.5) classifier. These results were better than the SVM classifier having an average accuracy of 92.43%. The obtained results prove that using DWT based time-frequency features with decision tree (C4.5) classifier can be one of the best choices for clinicians for precise detection of ventricular arrhythmias.

scholarly journals An Investigation of Insider Threat Mitigation Based on EEG Signal Classification

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6365
Author(s):  
Jung Hwan Kim ◽  
Chul Min Kim ◽  
Man-Sung Yim
Keyword(s):  
Nearest Neighbor ◽  
Nuclear Industry ◽  
Classification Model ◽  
Support Vector ◽  
Insider Threat ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Nuclear Facilities ◽  
Time Frequency ◽  
Threat Mitigation

This study proposes a scheme to identify insider threats in nuclear facilities through the detection of malicious intentions of potential insiders using subject-wise classification. Based on electroencephalography (EEG) signals, a classification model was developed to identify whether a subject has a malicious intention under scenarios of being forced to become an insider threat. The model also distinguishes insider threat scenarios from everyday conflict scenarios. To support model development, 21-channel EEG signals were measured on 25 healthy subjects, and sets of features were extracted from the time, time–frequency, frequency and nonlinear domains. To select the best use of the available features, automatic selection was performed by random-forest-based algorithms. The k-nearest neighbor, support vector machine with radial kernel, naïve Bayes, and multilayer perceptron algorithms were applied for the classification. By using EEG signals obtained while contemplating becoming an insider threat, the subject-wise model identified malicious intentions with 78.57% accuracy. The model also distinguished insider threat scenarios from everyday conflict scenarios with 93.47% accuracy. These findings could be utilized to support the development of insider threat mitigation systems along with existing trustworthiness assessments in the nuclear industry.

scholarly journals A Novel Fractional Filter Design and Cross-Term Elimination in Wigner Distribution

2015 ◽  
Vol 2015 ◽  
pp. 1-12
Author(s):  
Jiexiao Yu ◽  
Kaihua Liu ◽  
Liang Zhang ◽  
Peng Luo
Keyword(s):  
Frequency Distribution ◽  
Filter Design ◽  
Wigner Distribution ◽  
Piecewise Linear ◽  
Support Vector ◽  
Svm Classifier ◽  
Cross Term ◽  
Time Frequency ◽  
Fractional Filter ◽  
Time Frequency Distribution

The second and the third sentences of the abstract are changed and the shorter abstract is given as follows. To recover the nonstationary signal in complicated noise environment without distortion, a novel general design of fractional filter is proposed and applied to eliminate the Wigner cross-term. A time-frequency binary image is obtained from the time-frequency distribution of the observed signal and the optimal separating lines are determined by the support vector machine (SVM) classifier where the image boundary extraction algorithms are used to construct the training set of SVM. After that, the parameters and transfer function of filter can be determined by the parameters of the separating lines directly in the case of linear separability or line segments after the piecewise linear fitting of the separating curves in the case of nonlinear separability. Without any prior knowledge of signal and noise, this method can meet the reliability and universality simultaneously for filter design and realize the global optimization of filter parameters by machine learning even in the case of strong coupling between signal and noise. Furthermore, it could completely eliminate the cross-term in Wigner-Ville distribution (WVD) and the time-frequency distribution we get in the end has high resolution and good readability even when autoterms and cross-terms overlap. Simulation results verified the efficiency of this method.

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