Feature Extraction and Simulation of EEG Signals During Exercise-Induced Fatigue

Yang, Zhongwan; Ren, Huijie

doi:10.1109/access.2019.2909035

Cited by 27 publications

(7 citation statements)

References 7 publications

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“…To measure the performance of the cognitive load classification, a support vector machine (SVM) [36] is selected to conduct the binary classification. The SVM has been successfully used in alcoholic EEG classification [37] and fatigue identification [30], [38]. It can perform both the linear space discrimination and nonlinear classification by choosing different "kernel" functions which can be linear, polynomial kernel, radial basis function (RBF) and sigmoid.…”

Section: F Support Vector Machinementioning

confidence: 99%

Cognitive Load During Multitasking Can Be Accurately Assessed Based on Single Channel Electroencephalography Using Graph Methods

et al. 2021

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Mental workload has been widely estimated based on electroencephalography (EEG) in the frequency domain. However, simple frequency features are not entirely accurate indicators of the cognitive load because surface EEG signals are weak, nonstationary and randomness. We hypothesize that graph methods, which analyse the relationship between each point and other points of the EEG signals, may provide a more precise identification of the mental load. To investigate this hypothesis, we aim to identify the optimum graph features from 14 channel EEG recordings (sampling rate=128 Hz) in order to detect the high cognitive load related to multitasking. Three graph features: mean degree d, clustering coefficient c, and degree distribution p(k), are extracted from 48 subjects EEG records. Each experimental subject conducts two tasks: without tasks and with a simultaneous capacity task, respectively. After the experiment is completed, the feeling of the subject with the cognitive load tags in three types: low load, medium load, and heavy load. The optimal features of these three levels of the subject sensation and two types of cognitive load in different tasks are selected on the basis of statistical analysis. Then all graph features are forwarded into a support vector machine (SVM) and a decision tree to conduct objective scoring classification and a three subjective rating classification, respectively. Based on the present results,channels O2, T8, FC6, F8, and AF4 are considered optimal for a more efficiently estimation of the cognitive load. c associated with F8 and T8 during low cognitive load is significantly lower than those associated with high cognitive load (p<0.001). Using three graph features, the accuracy of identifying two types of mental load is 89.6%. Current findings suggest that the mental workload associated with multi-tasks can be accurately assessed using the graph approaches to EEG data.

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Section: F Support Vector Machinementioning

confidence: 99%

Cognitive Load During Multitasking Can Be Accurately Assessed Based on Single Channel Electroencephalography Using Graph Methods

et al. 2021

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“…For this reason, the desired signal structure has to be obtained using feature extraction from the EEG signal with different techniques. Therefore, in this study, feature vectors were created by examining the instantaneous frequency changes of the spectral entropy obtained from EEG signal [28]. Attribute vectors for classification algorithms have to be established before applying EEG data to the Bi-LSTM network.…”

Section: Filtering Of Eeg Signalsmentioning

confidence: 99%

Classification of P300 based brain computer interface systems using long short-term memory (LSTM) neural networks with feature fusion

Selvi¹,

Ferikoğlu²,

Erdoğan³

2021

Turk J Elec Eng & Comp Sci

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Enabling to obtain brain activation signs, electroencephalography is currently used in many applications as a medical diagnostic method. Brain-Computer Interface (BCI) applications are developed to facilitate the lives of individuals who have not lost their brain functions, yet have lost their motor and communication abilities. In this study, a BCI system is proposed to classify using Bi-directional Long Short Term Memory (Bi-LSTM) neural networks.In the designed system, spectral entropy method including instantaneous frequency change of signal is used as feature fusion. In the study, EEG data of 10 participants are collected with Emotiv EPOC+ device using 2x2 visual stimulus matrix prepared on Unity. Each symbol of the 2x2 matrix includes stimulus such as Doctor, Police, Fireman and Family.These stimuluses are demonstrated to participants with a fixed order. As data collection protocol, 200ms stimulus time and 300ms interstimulus interval are used. As the performance success of classification, the average accuracy rates are obtained to be 98.6% for training set and 96.9% for the test set. In addition, in classification of P300 EEG signals, the results obtained via Bi-LSTM are compared with the results obtained using 1 Dimensional Convolutional Neural Networks (1DCNN) and Support Vector Machines (SVM) classification methods. Moreover, in the study, information transfer rate (ITR) is provided as 40.39 at an acceptable level.

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“…Physical fatigue not only reduces the body's exercise ability but also causes neurological function decline. So, electroencephalograms (EEG) are another physiological index used for physical fatigue assessment [17,18]. Unfortunately, both EMGs and EEGs are weak bioelectric signals and are easily disturbed by many kinds of noise.…”

Section: Introductionmentioning

confidence: 99%

Heart Rate Variability-Based Subjective Physical Fatigue Assessment

Sun

2022

Sensors

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Accurate assessment of physical fatigue is crucial to preventing physical injury caused by excessive exercise, overtraining during daily exercise and professional sports training. However, as a subjective feeling of an individual, physical fatigue is difficult for others to objectively evaluate. Heart rate variability (HRV), which is derived from electrocardiograms (ECG) and controlled by the autonomic nervous system, has been demonstrated to be a promising indicator for physical fatigue estimation. In this paper, we propose a novel method for the automatic and objective classification of physical fatigue based on HRV. First, a total of 24 HRV features were calculated. Then, a feature selection method was proposed to remove useless features that have a low correlation with physical fatigue and redundant features that have a high correlation with the selected features. After feature selection, the best 11 features were selected and were finally used for physical fatigue classifying. Four machine learning algorithms were trained to classify fatigue using the selected features. The experimental results indicate that the model trained using the selected 11 features could classify physical fatigue with high accuracy. More importantly, these selected features could provide important information regarding the identification of physical fatigue.

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Feature Extraction and Simulation of EEG Signals During Exercise-Induced Fatigue

Cited by 27 publications

References 7 publications

Cognitive Load During Multitasking Can Be Accurately Assessed Based on Single Channel Electroencephalography Using Graph Methods

Cognitive Load During Multitasking Can Be Accurately Assessed Based on Single Channel Electroencephalography Using Graph Methods

Classification of P300 based brain computer interface systems using long short-term memory (LSTM) neural networks with feature fusion

Heart Rate Variability-Based Subjective Physical Fatigue Assessment

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