QUANTIFICATION OF MENTAL STRESS USING COMPLEXITY ANALYSIS OF EEG SIGNALS

2020 ◽  
Vol 32 (02) ◽  
pp. 2050011
Author(s):  
Kawser Ahammed ◽  
Mosabber Uddin Ahmed
Keyword(s):  
Mental Stress ◽  
Complexity Analysis ◽  
Detection System ◽  
Entropy Method ◽  
Multiscale Entropy ◽  
Support Vector ◽  
Cognitive Workload ◽  
Eeg Signals ◽  
Nonlinear Stress ◽  
Multivariate Multiscale Entropy

Detection of mental stress has been receiving great attention from the researchers for many years. Many studies have analyzed electroencephalogram signals in order to estimate mental stress using linear methods. In this paper, a novel nonlinear stress assessment method based on multivariate multiscale entropy has been introduced. Since the multivariate multiscale entropy method characterizes the complexity of nonlinear time series, this research determines the mental stress of human during cognitive workload using complexity of electroencephalogram (EEG) signals. To perform this work, 36 subjects including 9 men and 27 women were participated in the cognitive workload experiment. Multivariate multiscale entropy method has been applied to electroencephalogram data collected from those subjects for estimating mental stress in terms of complexity. The complexity feature of brain electroencephalogram signals collected during resting and cognitive workload has shown statistically significant ([Formula: see text]) differences across brain regions and mental tasks which can be implemented practically for building stress detection system. In addition, the complexity profile of electroencephalogram signals has shown that higher stress is reflected in good counting compared to bad counting. Moreover, the support vector machine (SVM) has shown promising classification between resting and mental counting states by providing 80% sensitivity, 100% specificity and 90% classification accuracy.

scholarly journals Multivariate Multiscale Entropy: An Approach to Estimating Vigilance of Driver

2020 ◽  
Author(s):  
Kawser Ahammed ◽  
Mosabber Uddin Ahmed
Keyword(s):  
Detection System ◽  
Entropy Method ◽  
Multiscale Entropy ◽  
Support Vector ◽  
Eeg Signals ◽  
Cognitive States ◽  
Simulated Environment ◽  
Classi Cation ◽  
Multivariate Multiscale Entropy ◽  
Electroencephalogram Eeg

Abstract This research has been done using recently introduced multivariate multiscale entropy method with a view to estimating vigilance of drivers during driving in simulated environment. In this driving simulation experiment, twenty one subjects including twelve men and nine women were participated. Multivariate multiscale entropy (MMSE) has been applied to this multimodal Seed dataset for estimating vigilance from electroencephalogram (EEG) and electrooculogram (EOG) signals in order to build a vigilance detection system. The experimental MMSE analysis curves show statistically signi cant di erences (p < 0.01) in terms of complexity among brain EEG signals, forehead EEG signals and EOG signals. Moreover, the di erence in the multivariate sample entropy across all scales in awake (1.0828 0.4664), tired (0.7841 0.3183) and drowsy (0.2938 0.1664) states are statistically signi cant (p <0.01). Also, the support vector machine (SVM), a machine learning technique, has discriminated the cognitive states (awake, tired and drowsy) with the promising classi cation accuracy of 76.2%. As a result, the MMSE analysis of cognitive states can be implemented practically for vigilance detection by building a programmable vigilance detection system .

scholarly journals Multivariate Multiscale Entropy: An Approach to Estimating Vigilance of Driver

2021 ◽  
Author(s):  
Kawser Ahammed ◽  
Mosabber Uddin Ahmed
Keyword(s):  
Detection System ◽  
Sample Entropy ◽  
Multiscale Entropy ◽  
Support Vector ◽  
Eeg Signals ◽  
Cognitive States ◽  
The Difference ◽  
New Feature ◽  
Multivariate Multiscale Entropy ◽  
Multivariate Sample

Abstract Various driver’s vigilance estimation techniques currently exist in literature. But none of them detects the vigilance of driver in complexity domain. As a result, we have proposed the recently introduced multivariate multiscale entropy (MMSE) method to fill this research gap. In this research, we have applied the MMSE technique to differential entropy features of electroencephalogram (EEG) and electrooculogram (EOG) signals for detecting vigilance of driver in complexity domain. The MMSE has also been employed to PERCLOS (Percentage of Eye Closure) values to analyse cognitive states (awake, tired and drowsy) in complexity domain. The contribution of this research is to show how a new feature called MMSE can efficiently classify the awake, tired and drowsy state of the driver in complexity domain. Another contribution is to demonstrate the distinguishing ability of the MMSE by validating it with applying multivariate sample entropy feature of cognitive states to support vector machine (SVM). The experimental MMSE analysis curves show statistically significant differences (p < 0.01) in terms of complexity among brain EEG signals, forehead EEG signals and EOG signals. Moreover, the difference in the multivariate sample entropy across all scales in awake (1.0828 ± 0.4664), tired (0.7841 ± 0.3183) and drowsy (0.2938 ± 0.1664) states are statistically significant (p <0.01). Also, the SVM, a machine learning technique, has discriminated the cognitive states with the promising classification accuracy of 76.2%. As a result, the MMSE analysis of cognitive states can be implemented practically for vigilance detection by building a programmable vigilance detection system.

ALCOHOLIC INDEX USING NON-LINEAR FEATURES EXTRACTED FROM DIFFERENT FREQUENCY BANDS

2017 ◽  
Vol 17 (07) ◽  
pp. 1740009
Author(s):  
G. MURALIDHAR BAIRY ◽  
U. C. NIRANJAN ◽  
SHU LIH OH ◽  
JOEL E. W. KOH ◽  
VIDYA K. SUDARSHAN ◽  
...  
Keyword(s):  
Fuzzy Entropy ◽  
Multiscale Entropy ◽  
Permutation Entropy ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Linear Features ◽  
Entropy Formula ◽  
Non Linear ◽  
The Brain

Alcoholism is a complex condition that mainly disturbs the neuronal networks in Central Nervous System (CNS). This disorder not only disturbs the brain, but also affects the behavior, emotions, and cognitive judgements. Electroencephalography (EEG) is a valuable tool to examine the neuropsychiatric disorders like alcoholism. The EEG is a well-established modality to diagnose the electrical activity produced by the populations of neurons in cerebral cortex. However, EEG signals are non-linear in nature; hence very challenging to interpret the valuable information from them using linear methods. Thus, using non-linear methods to analyze EEG signals can be beneficial in order to predict the brain signals condition. This paper presents a computer-aided diagnostic method for the detection of alcoholic EEG signals from normal by employing the non-linear techniques. First, the EEG signals are subjected to six levels of Wavelet Packet Decomposition (WPD) to obtain seven wavebands (delta ([Formula: see text]), theta ([Formula: see text]), lower alpha (la), upper alpha (ua), lower beta (lb), upper beta (ub), lower gamma (lg)). From each wavebands (activity bands), 19 non-linear features such as Recurrence Quantification Analysis (RQA) ([Formula: see text]), Approximate Entropy ([Formula: see text]), Energy ([Formula: see text]), Fractal Dimension (FD) ([Formula: see text]), Permutation Entropy ([Formula: see text]), Detrended Fluctuation Analysis ([Formula: see text]), Hurst Exponent ([Formula: see text]), Largest Lyapunov Exponent ([Formula: see text]), Sample Entropy ([Formula: see text]), Shannon’s Entropy ([Formula: see text]), Renyi’s entropy ([Formula: see text]), Tsalli’s entropy ([Formula: see text]), Fuzzy entropy ([Formula: see text]), Wavelet entropy ([Formula: see text]), Kolmogorov–Sinai entropy ([Formula: see text]), Modified Multiscale Entropy ([Formula: see text]), Hjorth’s parameters (activity ([Formula: see text]), mobility ([Formula: see text]), and complexity ([Formula: see text])) are extracted. The extracted features are then ranked using Bhattacharyya, Entropy, Fuzzy entropy-based Max-Relevancy and Min-Redundancy (mRMR), Receiver Operating Characteristic (ROC), [Formula: see text]-test, and Wilcoxon. These ranked features are given to train Support Vector Machine (SVM) classifier. The SVM classifier with radial basis function (RBF) achieved 95.41% accuracy, 93.33% sensitivity and 97.50% specificity using four non-linear features ranked by Wilcoxon method. In addition, an integrated index called Alcoholic Index (ALCOHOLI) is developed using highly ranked two features for identification of normal and alcoholic EEG signals using a single number. This system is rapid, efficient, and inexpensive and can be employed as an EEG analysis assisting system by clinicians in the detection of alcoholism. In addition, the proposed system can be used in rehabilitation centers to evaluate person with alcoholism over time and observe the outcome of treatment provided for reducing or reversing the impact of the condition on the brain.

scholarly journals Portable Drowsiness Detection through Use of a Prefrontal Single-Channel Electroencephalogram

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4477 ◽  
Author(s):  
Mikito Ogino ◽  
Yasue Mitsukura
Keyword(s):  
Pattern Recognition ◽  
Classification Accuracy ◽  
Single Channel ◽  
Detection System ◽  
Brain Activity ◽  
Multiscale Entropy ◽  
Support Vector ◽  
Time Averaging ◽  
Drowsiness Detection ◽  
Significant Difference

Drowsiness detection has been studied in the context of evaluating products, assessing driver alertness, and managing office environments. Drowsiness level can be readily detected through measurement of human brain activity. The electroencephalogram (EEG), a device whose application relies on adhering electrodes to the scalp, is the primary method used to monitor brain activity. The many electrodes and wires required to perform an EEG place considerable constraints on the movement of users, and the cost of the device limits its availability. For these reasons, conventional EEG devices are not used in practical studies and businesses. Many potential practical applications could benefit from the development of a wire-free, low-priced device; however, it remains to be elucidated whether portable EEG devices can be used to estimate human drowsiness levels and applied within practical research settings and businesses. In this study, we outline the development of a drowsiness detection system that makes use of a low-priced, prefrontal single-channel EEG device and evaluate its performance in an offline analysis and a practical experiment. Firstly, for the development of the system, we compared three feature extraction methods: power spectral density (PSD), autoregressive (AR) modeling, and multiscale entropy (MSE) for detecting characteristics of an EEG. In order to efficiently select a meaningful PSD, we utilized step-wise linear discriminant analysis (SWLDA). Time-averaging and robust-scaling were used to fit the data for pattern recognition. Pattern recognition was performed by a support vector machine (SVM) with a radial basis function (RBF) kernel. The optimal hyperparameters for the SVM were selected by the grind search method so as to increase drowsiness detection accuracy. To evaluate the performance of the detections, we calculated classification accuracy using the SVM through 10-fold cross-validation. Our model achieved a classification accuracy of 72.7% using the PSD with SWLDA and the SVM. Secondly, we conducted a practical study using the system and evaluated its performance in a practical situation. There was a significant difference (* p < 0.05) between the drowsiness-evoked task and concentration-needed task. Our results demonstrate the efficacy of our low-priced portable drowsiness detection system in quantifying drowsy states. We anticipate that our system will be useful to practical studies with aims as diverse as measurement of classroom mental engagement, evaluation of movies, and office environment evaluation.

scholarly journals Anxiety Level Recognition for Virtual Reality Therapy System Using Physiological Signals

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 1039 ◽  
Author(s):  
Justas Šalkevicius ◽  
Robertas Damaševičius ◽  
Rytis Maskeliunas ◽  
Ilona Laukienė
Keyword(s):  
Virtual Reality ◽  
Mental Stress ◽  
Cross Validation ◽  
Detection System ◽  
Anxiety Level ◽  
Physiological Signals ◽  
Support Vector ◽  
Svm Classifier ◽  
Stress Detection ◽  
The Individual

Virtual reality exposure therapy (VRET) can have a significant impact towards assessing and potentially treating various anxiety disorders. One of the main strengths of VRET systems is that they provide an opportunity for a psychologist to interact with virtual 3D environments and change therapy scenarios according to the individual patient’s needs. However, to do this efficiently the patient’s anxiety level should be tracked throughout the VRET session. Therefore, in order to fully use all advantages provided by the VRET system, a mental stress detection system is needed. The patient’s physiological signals can be collected with wearable biofeedback sensors. Signals like blood volume pressure (BVP), galvanic skin response (GSR), and skin temperature can be processed and used to train the anxiety level classification models. In this paper, we combine VRET with mental stress detection and highlight potential uses of this kind of VRET system. We discuss and present a framework for anxiety level recognition, which is a part of our developed cloud-based VRET system. Physiological signals of 30 participants were collected during VRET-based public speaking anxiety treatment sessions. The acquired data were used to train a four-level anxiety recognition model (where each level of ‘low’, ‘mild’, ‘moderate’, and ‘high’ refer to the levels of anxiety rather than to separate classes of the anxiety disorder). We achieved an 80.1% cross-subject accuracy (using leave-one-subject-out cross-validation) and 86.3% accuracy (using 10 × 10 fold cross-validation) with the signal fusion-based support vector machine (SVM) classifier.

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 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.

MULTISCALE ENTROPY ANALYSIS OF EEG RECORDINGS IN EPILEPTIC RATS

2009 ◽  
Vol 21 (03) ◽  
pp. 169-176 ◽  
Author(s):  
Gaoxiang Ouyang ◽  
Chuangyin Dang ◽  
Xiaoli Li
Keyword(s):  
Entropy Method ◽  
Multiscale Entropy ◽  
Test Results ◽  
Entropy Analysis ◽  
Eeg Signals ◽  
Linear Discriminant ◽  
Seizure State ◽  
Single Scale ◽  
Eeg Recordings ◽  
Electroencephalogram Eeg

In this study, we investigate multiscale entropy (MSE) as a tool to evaluate the dynamic characteristics of electroencephalogram (EEG) during seizure-free, pre-seizure and seizure state, respectively, in epileptic rats. The results show that MSE method is able to reveal that EEG signals are more complex in seizure-free state than in seizure state, and can successfully distinguish among different seizure states. The classification ability of the MSE measures is tested using the linear discriminant analysis (LDA). Test results confirm that the classification accuracy of MSE method is superior to traditional single-scale entropy method. MSE method has potential in classifying the epileptic EEG signals.

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