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.

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.

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.

scholarly journals Complexity of EEG Signals in Schizophrenia Syndromes

2019 ◽  
Author(s):  
Илья Кутепов ◽  
Ilya Kutepov ◽  
Вадим Крысько ◽  
Vadim Krysko ◽  
Антон Крысько ◽  
...  
Keyword(s):  
Study Groups ◽  
Classification Problem ◽  
Multiscale Entropy ◽  
Control Group ◽  
Eeg Signals ◽  
Additional Tool ◽  
Chaotic Signals ◽  
And Control ◽  
Further Development ◽  
Electroencephalogram Eeg

In the present study, 45 patients with schizophrenia syndromes and 39 healthy subjects are studied with electroencephalogram (EEG) signals. The study groups were of different genders. For each of the two groups, the signals were analyzed using 16 EEG channels. Multiscale entropy, Lempel-Ziv complexity and Lyapunov exponent were used to study the chaotic signals. The data were compared for two groups of subjects. Entropy was compared for each of the 16 channels for all subjects. As a result, topographic images of brain areas were obtained, illustrating the entropy and complexity of Lempel-Ziv. Lempel-Ziv complexity was found to be more representative of the classification problem. The results will be useful for further development of EEG signal classification algorithms for machine learning. This study shows that EEG signals can be an effective tool for classifying participants with symptoms of schizophrenia and control group. It is suggested that this analysis may be an additional tool to help psychiatrists diagnose patients with schizophrenia.

scholarly journals Person authentication based on eye-closed and visual stimulation using EEG signals

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Hui Yen Yap ◽  
Yun-Huoy Choo ◽  
Zeratul Izzah Mohd Yusoh ◽  
Wee How Khoh
Keyword(s):  
User Satisfaction ◽  
Visual Stimulation ◽  
Support Vector ◽  
Biometric System ◽  
Eeg Signals ◽  
Pairwise Correlation ◽  
Eyes Closed ◽  
Recording Process ◽  
Electroencephalogram Eeg ◽  
Accuracy Performance

AbstractThe study of Electroencephalogram (EEG)-based biometric has gained the attention of researchers due to the neurons’ unique electrical activity representation of an individual. However, the practical application of EEG-based biometrics is not currently widespread and there are some challenges to its implementation. Nowadays, the evaluation of a biometric system is user driven. Usability is one of the concerning issues that determine the success of the system. The basic elements of the usability of a biometric system are effectiveness, efficiency and user satisfaction. Apart from the mandatory consideration of the biometric system’s performance, users also need an easy-to-use and easy-to-learn authentication system. Thus, to satisfy these user requirements, this paper proposes a reasonable acquisition period and employs a consumer-grade EEG device to authenticate an individual to identify the performances of two acquisition protocols: eyes-closed (EC) and visual stimulation. A self-collected database of eight subjects was utilized in the analysis. The recording process was divided into two sessions, which were the morning and afternoon sessions. In each session, the subject was requested to perform two different tasks: EC and visual stimulation. The pairwise correlation of the preprocessed EEG signals of each electrode channel was determined and a feature vector was formed. Support vector machine (SVM) was then used for classification purposes. In the performance analysis, promising results were obtained, where EC protocol achieved an accuracy performance of 83.70–96.42% while visual stimulation protocol attained an accuracy performance of 87.64–99.06%. These results have demonstrated the feasibility and reliability of our acquisition protocols with consumer-grade EEG devices.

Automated Classification of Focal and Non-Focal EEG Signals Based on Bivariate Empirical Mode Decomposition

2018 ◽  
pp. 13-33 ◽  
Author(s):  
Rajeev Sharma ◽  
Ram Bilas Pachori
Keyword(s):  
Empirical Mode Decomposition ◽  
Support Vector ◽  
Automated Classification ◽  
Intrinsic Mode Functions ◽  
Eeg Signals ◽  
Mode Decomposition ◽  
Electroencephalogram Eeg ◽  
Aided Diagnosis ◽  
Bivariate Empirical Mode Decomposition

The chapter presents a new approach of computer aided diagnosis of focal electroencephalogram (EEG) signals by applying bivariate empirical mode decomposition (BEMD). Firstly, the focal and non-focal EEG signals are decomposed using the BEMD, which results in intrinsic mode functions (IMFs) corresponding to each signal. Secondly, bivariate bandwidths namely, amplitude bandwidth, precession bandwidth, and deformation bandwidth are computed for each obtained IMF. Interquartile range (IQR) values of bivariate bandwidths of IMFs are employed as the features for classification. In order to perform classification least squares support vector machine (LS-SVM) is used. The results of the experiment suggest that the computed bivariate bandwidths are significantly useful to discriminate focal EEG signals. The resultant classification accuracy obtained using proposed methodology, applied on the Bern-Barcelona EEG database, is 84.01%. The obtained results are encouraging and the proposed methodology can be helpful for identification of epileptogenic focus.

Classifying Electroencephalogram (EEG) Signals Using BAT-SVM Classifier for Detecting Epilepsy

2021 ◽  
Vol 12 (3) ◽  
pp. 96-115
Author(s):  
Manal Tantawi ◽  
Aya Naser ◽  
Howida Shedeed ◽  
Mohammed Fahmy Tolba
Keyword(s):  
Support Vector Machines ◽  
Epileptic Seizures ◽  
Bat Algorithm ◽  
Line Length ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Vector Machines ◽  
Electroencephalogram Eeg ◽  
Source Of Information

Electroencephalogram (EEG) signals are a valuable source of information for detecting epileptic seizures. However, monitoring EEG for long periods of time is very exhausting and time consuming. Thus, detecting epilepsy in EEG signals automatically is highly appreciated. In this study, three classes, namely normal, interictal (out of seizure time), and ictal (during seizure), are considered. Moreover, a comparative study is provided for the efficient features in literature resulting in a suggested combination of only three discriminative features, namely R'enyi entropy, line length, and energy. These features are calculated from each of the EEG sub-bands. Finally, support vector machines (SVM) classifier optimized using BAT algorithm (BAT-SVM) is introduced by this study for discriminating between the three classes. Experiments were conducted using Andrzejak database. The accomplished experiments and comparisons in this study emphasize the superiority of the proposed BAT-SVM along with the suggested feature set in achieving the best results.

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

Sensors ◽  
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.

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.

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