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Classification of mental workload using brain connectivity and machine learning on electroencephalogram data

Scientific reports, 2024-04, Vol.14 (1), p.9153-9153, Article 9153

2024

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Monitoring the level of hypnosis using a hierarchical SVM system

Journal of clinical monitoring and computing, 2020-04, Vol.34 (2), p.331-338

2020

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Assessment of Anesthesia Depth Using Effective Brain Connectivity Based on Transfer Entropy on EEG Signal

Basic and clinical neuroscience, 2021-03, Vol.12 (2), p.269-280

2021

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Combined Yoga and Transcranial Direct Current Stimulation Increase Functional Connectivity and Synchronization in the Frontal Areas

Brain topography, 2022-03, Vol.35 (2), p.207-218

2022

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Monitoring the depth of anesthesia using entropy features and an artificial neural network

Journal of neuroscience methods, 2013-08, Vol.218 (1), p.17-24

2013

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Non-linear Entropy Analysis in EEG to Predict Treatment Response to Repetitive Transcranial Magnetic Stimulation in Depression

Frontiers in pharmacology, 2018-10, Vol.9, p.1188-1188

2018

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Prediction of treatment response in major depressive disorder using a hybrid of convolutional recurrent deep neural networks and effective connectivity based on EEG signal

Physical and engineering sciences in medicine, 2024-06, Vol.47 (2), p.633-642

2024

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Classification of mental workload with EEG analysis by using effective connectivity and a hybrid model of CNN and LSTM

Computer methods in biomechanics and biomedical engineering, 2024-07, p.1-15

2024

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Monitoring the Depth of Anesthesia Using a New Adaptive Neurofuzzy System

IEEE journal of biomedical and health informatics, 2018-05, Vol.22 (3), p.671-677

2018

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Frontal–temporal functional connectivity of EEG signal by standardized permutation mutual information during anesthesia

Cognitive neurodynamics, 2019-12, Vol.13 (6), p.531-540

2019

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An effective brain connectivity technique to predict repetitive transcranial magnetic stimulation outcome for major depressive disorder patients using EEG signals

Physical and engineering sciences in medicine, 2023-03, Vol.46 (1), p.67-81

2023

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A Predictive Model to Diagnose Psychophysiological Insomnia with Daytime Hyperarousal and Nighttime Micro–Macro-Structures Correlation

Sleep and vigilance, 2023-12, Vol.7 (2), p.123-133

2023

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Sleep Micro–Macro-structures in Psychophysiological Insomnia. PSG Study

Sleep and vigilance, 2023-04, Vol.7 (1), p.55-63

2023

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Automatic Computation of Left Ventricular Volume Changes Over a Cardiac Cycle from Echocardiography Images by Nonlinear Dimensionality Reduction

Journal of digital imaging, 2015-02, Vol.28 (1), p.91-98

2015

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Frontal-Temporal Synchronization of EEG Signals Quantified by Order Patterns Cross Recurrence Analysis During Propofol Anesthesia

IEEE transactions on neural systems and rehabilitation engineering, 2015-05, Vol.23 (3), p.468-474

2015

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Extracting a seizure intensity index from one-channel EEG signal using bispectral and detrended fluctuation analysis

Journal of biomedical science and engineering, 2010-03, Vol.3 (3), p.253-261

2010

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ORDER PATTERNS RECURRENCE ANALYSIS OF ELECTROENCEPHALOGRAM DURING SEVOFLURANE ANESTHESIA

Biomedical engineering : applications, basis, and communications, 2015-10, Vol.27 (5), p.1550049

2015

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Epilepsy detection using Detrended Fluctuation Analysis

2009 International Conference on Wavelet Analysis and Pattern Recognition, 2009, p.235-240

2009

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A convolutional recurrent neural network with attention for response prediction to repetitive transcranial magnetic stimulation in major depressive disorder

Scientific reports, 2023-06, Vol.13 (1), p.10147-10147, Article 10147

2023

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Machine learning approaches for early detection of non-alcoholic steatohepatitis based on clinical and blood parameters

Scientific reports, 2024-01, Vol.14 (1), p.2442-12, Article 2442

2024

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Prediction of response to repetitive transcranial magnetic stimulation for major depressive disorder using hybrid Convolutional recurrent neural networks and raw Electroencephalogram Signal

Cognitive neurodynamics, 2023-08, Vol.17 (4), p.909-920

2023

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Attention-Based Convolutional Recurrent Deep Neural Networks for the Prediction of Response to Repetitive Transcranial Magnetic Stimulation for Major Depressive Disorder

International journal of neural systems, 2023-02, Vol.33 (2), p.2350007

2023

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Graph-based Analysis to Predict Repetitive Transcranial Magnetic Stimulation Treatment Response in Patients With Major Depressive Disorder Using EEG Signals

Basic and clinical neuroscience, 2024-03, Vol.15 (2), p.199-210

2024

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Prediction of treatment outcome for repetitive transcranial magnetic stimulation in major depressive disorder using connectivity measures and ensemble of pre-trained deep learning models

Biomedical signal processing and control, 2023-08, Vol.85, p.104822, Article 104822

2023

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Granger causality analysis in combination with directed network measures for classification of MS patients and healthy controls using task-related fMRI

Computers in biology and medicine, 2019-12, Vol.115, p.103495-103495, Article 103495

2019

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Classification of mental workload using brain connectivity and machine learning on electroencephalogram data

Monitoring the level of hypnosis using a hierarchical SVM system

Assessment of Anesthesia Depth Using Effective Brain Connectivity Based on Transfer Entropy on EEG Signal

Combined Yoga and Transcranial Direct Current Stimulation Increase Functional Connectivity and Synchronization in the Frontal Areas

Monitoring the depth of anesthesia using entropy features and an artificial neural network

Non-linear Entropy Analysis in EEG to Predict Treatment Response to Repetitive Transcranial Magnetic Stimulation in Depression

Prediction of treatment response in major depressive disorder using a hybrid of convolutional recurrent deep neural networks and effective connectivity based on EEG signal

Classification of mental workload with EEG analysis by using effective connectivity and a hybrid model of CNN and LSTM

Monitoring the Depth of Anesthesia Using a New Adaptive Neurofuzzy System

Frontal–temporal functional connectivity of EEG signal by standardized permutation mutual information during anesthesia

An effective brain connectivity technique to predict repetitive transcranial magnetic stimulation outcome for major depressive disorder patients using EEG signals

A Predictive Model to Diagnose Psychophysiological Insomnia with Daytime Hyperarousal and Nighttime Micro–Macro-Structures Correlation

Sleep Micro–Macro-structures in Psychophysiological Insomnia. PSG Study

Automatic Computation of Left Ventricular Volume Changes Over a Cardiac Cycle from Echocardiography Images by Nonlinear Dimensionality Reduction

Frontal-Temporal Synchronization of EEG Signals Quantified by Order Patterns Cross Recurrence Analysis During Propofol Anesthesia

Extracting a seizure intensity index from one-channel EEG signal using bispectral and detrended fluctuation analysis

ORDER PATTERNS RECURRENCE ANALYSIS OF ELECTROENCEPHALOGRAM DURING SEVOFLURANE ANESTHESIA

Epilepsy detection using Detrended Fluctuation Analysis

A convolutional recurrent neural network with attention for response prediction to repetitive transcranial magnetic stimulation in major depressive disorder

Machine learning approaches for early detection of non-alcoholic steatohepatitis based on clinical and blood parameters

Prediction of response to repetitive transcranial magnetic stimulation for major depressive disorder using hybrid Convolutional recurrent neural networks and raw Electroencephalogram Signal

Attention-Based Convolutional Recurrent Deep Neural Networks for the Prediction of Response to Repetitive Transcranial Magnetic Stimulation for Major Depressive Disorder

Graph-based Analysis to Predict Repetitive Transcranial Magnetic Stimulation Treatment Response in Patients With Major Depressive Disorder Using EEG Signals

Prediction of treatment outcome for repetitive transcranial magnetic stimulation in major depressive disorder using connectivity measures and ensemble of pre-trained deep learning models

Granger causality analysis in combination with directed network measures for classification of MS patients and healthy controls using task-related fMRI

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