Feature Selection for EEG-Based Fatigue Analysis Using Pearson Correlation

Risqiwati, Diah; Wibawa, Adhi Dharma; Pane, Evi Septiana; Islamiyah, Wardah Rahmatul; Tyas, Agnes Estuning; Purnomo, Mauridhi Hery

doi:10.1109/isitia49792.2020.9163760

Cited by 28 publications

(15 citation statements)

References 21 publications

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“…There are many methods for selecting optimal signal features. Commonly used methods include techniques such as chi-square [ 97 , 98 ], tree-based feature selection [ 99 , 100 ], Pearson’s correlation [ 101 , 102 ], LASSO [ 103 , 104 , 105 ], low variance [ 106 ], and recursive feature elimination [ 107 ].…”

Section: Methodsmentioning

confidence: 99%

Diagnostics of Articular Cartilage Damage Based on Generated Acoustic Signals Using ANN—Part II: Patellofemoral Joint

Karpiński

Krakowski

Jonak

et al. 2022

Sensors

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Cartilage loss due to osteoarthritis (OA) in the patellofemoral joint provokes pain, stiffness, and restriction of joint motion, which strongly reduces quality of life. Early diagnosis is essential for prolonging painless joint function. Vibroarthrography (VAG) has been proposed in the literature as a safe, noninvasive, and reproducible tool for cartilage evaluation. Until now, however, there have been no strict protocols for VAG acquisition especially in regard to differences between the patellofemoral and tibiofemoral joints. The purpose of this study was to evaluate the proposed examination and acquisition protocol for the patellofemoral joint, as well as to determine the optimal examination protocol to obtain the best diagnostic results. Thirty-four patients scheduled for knee surgery due to cartilage lesions were enrolled in the study and compared with 33 healthy individuals in the control group. VAG acquisition was performed prior to surgery, and cartilage status was evaluated during the surgery as a reference point. Both closed (CKC) and open (OKC) kinetic chains were assessed during VAG. The selection of the optimal signal measures was performed using a neighborhood component analysis (NCA) algorithm. The classification was performed using multilayer perceptron (MLP) and radial basis function (RBF) neural networks. The classification using artificial neural networks was performed for three variants: I. open kinetic chain, II. closed kinetic chain, and III. open and closed kinetic chain. The highest diagnostic accuracy was obtained for variants I and II for the RBF 9-35-2 and MLP 10-16-2 networks, respectively, achieving a classification accuracy of 98.53, a sensitivity of 0.958, and a specificity of 1. For variant III, a diagnostic accuracy of 97.79 was obtained with a sensitivity and specificity of 0.978 for MLP 8-3-2. This indicates a possible simplification of the examination protocol to single kinetic chain analyses.

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Section: Methodsmentioning

confidence: 99%

Diagnostics of Articular Cartilage Damage Based on Generated Acoustic Signals Using ANN—Part II: Patellofemoral Joint

Karpiński

Krakowski

Jonak

et al. 2022

Sensors

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“…The R value reflects the correlation coefficients and this value takes values between 1 and -1. 1 represents an absolute positive correlation, 0 represents no correlation and -1 represents an absolute negative correlation [31].…”

Section: Performance Criteriamentioning

confidence: 99%

Prediction of Life Quality Index Value Rankings of Countries After the COVID-19 Pandemic by Artificial Neural Networks

2024

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There are some indexes that affect the quality of life of countries. Economic crises, pandemics, natural events, etc. affect these indexes. The COVID-19 pandemic also had a direct impact on all these indexes. In this study, the impact of the COVID-19 pandemic on the quality of life of countries was investigated. In this context, 29 different artificial neural networks were trained between 2012 and 2019 with the data of 29 countries in the European continent, consisting of a total of six indexes. The countries' quality of life indexes for 2020 were predicted and compared with the quality of life indexes realized in 2020. The study was evaluated according to the performance criteria R, R2, RMSE and MAPE in the range "very good, good, valid and invalid" and showed “very good” results. In this study, it was found that the country with the highest decrease in quality of life after the pandemic was Germany and the country with the highest increase was Bulgaria. In Turkey and Spain, the change in quality of life indexes was close to zero. In accordance with the results, evaluations were made and suggestions were made.

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“…Among those methods for EEG-based evaluation of brain FC, the most representatives usually include Correlation, Coherence, and Phase Locking Value (PLV). For example, Risqiwati et al ( 22 ) performed EEG-based FC analysis with Pearson Correlation metric in different mental states and found obvious FC effect in Beta (13–30 Hz) and Theta (4–7 Hz) frequency bands. In Duffy and Als ( 23 ).…”

Section: Related Studymentioning

confidence: 99%

EEG-FCV: An EEG-Based Functional Connectivity Visualization Framework for Cognitive State Evaluation

Zeng

Jin

et al. 2022

Front. Psychiatry

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Electroencephalogram (EEG)-based tools for brain functional connectivity (FC) analysis and visualization play an important role in evaluating brain cognitive function. However, existing similar FC analysis tools are not only visualized in 2 dimensions (2D) but also are highly prone to cause visual clutter and unable to dynamically reflect brain connectivity changes over time. Therefore, we design and implement an EEG-based FC visualization framework in this study, named EEG-FCV, for brain cognitive state evaluation. EEG-FCV is composed of three parts: the Data Processing module, Connectivity Analysis module, and Visualization module. Specially, FC is visualized in 3 dimensions (3D) by introducing three existing metrics: Pearson Correlation Coefficient (PCC), Coherence, and PLV. Furthermore, a novel metric named Comprehensive is proposed to solve the problem of visual clutter. EEG-FCV can also visualize dynamically brain FC changes over time. Experimental results on two available datasets show that EEG-FCV has not only results consistent with existing related studies on brain FC but also can reflect dynamically brain FC changes over time. We believe EEG-FCV could prompt further progress in brain cognitive function evaluation.

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Feature Selection for EEG-Based Fatigue Analysis Using Pearson Correlation

Cited by 28 publications

References 21 publications

Diagnostics of Articular Cartilage Damage Based on Generated Acoustic Signals Using ANN—Part II: Patellofemoral Joint

Diagnostics of Articular Cartilage Damage Based on Generated Acoustic Signals Using ANN—Part II: Patellofemoral Joint

Prediction of Life Quality Index Value Rankings of Countries After the COVID-19 Pandemic by Artificial Neural Networks

EEG-FCV: An EEG-Based Functional Connectivity Visualization Framework for Cognitive State Evaluation

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