Power Changes of EEG Signals Associated with Muscle Fatigue: The Root Mean Square Analysis of EEG Bands

Abdul-latif, A. A.; Ćosić, Irena; Kumar, Dinesh; Polus, Barbara; Costa, Cristiano

doi:10.1109/issnip.2004.1417517

Cited by 26 publications

(15 citation statements)

References 16 publications

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“…[46] Other studies found that the power of EEG signals also changed as muscle fatigue changed. [47] The results which found the strong relationship between muscle activity and mental activity were consistent with some previous study results. A study by Waersted [41] evidenced that mental activity contributed to muscle fatigue especially in the neck and shoulder regions.…”

Section: Mental Activitysupporting

confidence: 92%

Development of statistical models for predicting muscle and mental activities during repetitive precision tasks

Zadry

Dawal

Taha

2016

International Journal of Occupational Safety and Ergonomics

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This study was conducted to develop muscle and mental activities on repetitive precision tasks. A laboratory experiment was used to address the objectives. Surface electromyography was used to measure muscle activities from eight upper limb muscles, while electroencephalography recorded mental activities from six channels. Fourteen university students participated in the study. The results show that muscle and mental activities increase for all tasks, indicating the occurrence of muscle and mental fatigue. A linear relationship between muscle activity, mental activity and time was found while subjects were performing the task. Thus, models were developed using those variables. The models were found valid after validation using other students' and workers' data. Findings from this study can contribute as a reference for future studies investigating muscle and mental activity and can be applied in industry as guidelines to manage muscle and mental fatigue, especially to manage job schedules and rotation.

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Section: Mental Activitysupporting

confidence: 92%

Development of statistical models for predicting muscle and mental activities during repetitive precision tasks

Zadry

Dawal

Taha

2016

International Journal of Occupational Safety and Ergonomics

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“…The achieved results indicate that predicting blood lactate levels, high or low, using electroencephalogram brain data can be done accurately in terms of classification scores when implemented for healthy athlete who endures a single bout of acute exercises. The discrimination ability is driven by the changes encountered in the band power values of EEG signal bands after doing an exercise [ 25 ]. This hypothesis was proven by variations that occurred with alpha and beta frequency band power that investigated after implementing a maximal effort exercise and shows an increment in beta absolute power in a group of electrodes [ 26 ].…”

Section: Discussionmentioning

confidence: 99%

Classification of Lactate Level Using Resting-State EEG Measurements

Shaban

Uçan

Duru

2021

Applied Bionics and Biomechanics

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The electroencephalography (EEG) signals have been used widely for studying the brain neural information dynamics and behaviors along with the developing impact of using the machine and deep learning techniques. This work proposes a system based on the fast Fourier transform (FFT) as a feature extraction method for the classification of human brain resting-state electroencephalography (EEG) recorded signals. In the proposed system, the FFT method is applied on the resting-state EEG recordings and the corresponding band powers were calculated. The extracted relative power features are supplied to the classification methods (classifiers) as an input for the classification purpose as a measure of human tiredness through predicting lactate enzyme level, high or low. To validate the suggested method, we used an EEG dataset which has been recorded from a group of elite-level athletes consisting of two classes: not tired, the EEG signals were recorded during the resting-state task before performing acute exercise and tired, the EEG signals were recorded in the resting-state after performing an acute exercise. The performance of three different classifiers was evaluated with two performance measures, accuracy and precision values. The accuracy was achieved above 98% by the K-nearest neighbor (KNN) classifier. The findings of this study indicated that the feature extraction scheme has the ability to classify the analyzed EEG signals accurately and predict the level of lactate enzyme high or low. Many studying fields, like the Internet of Things (IoT) and the brain computer interface (BCI), can utilize the findings of the proposed system in many crucial decision-making applications.

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“…The power spectrum analysis has been also used in the research mentioned earlier but in complex form and also found dependent upon error rate variation in time. Power estimation and changes in EEG signals related to muscle fatigue over the motor cortex area for Adductor Pollicis muscle measured in Relaxed and contraction states are classified using change in power spectrum [7], studies like Test of Variables of Attention (TOVA) was performed with power estimation and PCA to measure alertness [8]. Feature extraction also done with the help of power spectral entropy in case to recognize left and right hand movement [9].…”

Section: Introductionmentioning

confidence: 99%

Power Spectrum Analysis of EEG Signals for Estimating Visual Attention

KumarAhirwal¹,

Londhe²

2012

IJCA

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The task oriented brain activity analysis and classification is a prime issue in EEG signal processing these days. The similar attempt has been done here to estimate the brain activity on the basis of power spectrum analysis. For this, the modified approach involving both Independent Component Analysis (ICA) and Principal Component Analysis (PCA) methodologies has been used in this paper to investigate the behavior of brain's electrical activity for a simple case of visual attention. The proposed method of EEG classification can be very useful in predicting the action or the intention of action performed on the basis of EEG which leads to more development in brain computer interface. The EEG data has been referred from a website and the mathematical tool for EEG analysis called EEGLAB has been used to perform work in this paper.

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Power Changes of EEG Signals Associated with Muscle Fatigue: The Root Mean Square Analysis of EEG Bands

Cited by 26 publications

References 16 publications

Development of statistical models for predicting muscle and mental activities during repetitive precision tasks

Development of statistical models for predicting muscle and mental activities during repetitive precision tasks

Classification of Lactate Level Using Resting-State EEG Measurements

Power Spectrum Analysis of EEG Signals for Estimating Visual Attention

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