[HTML][HTML] Why brain criticality is clinically relevant: a scoping review
V Zimmern - Frontiers in neural circuits, 2020 - frontiersin.org
The past twenty-five years have seen a strong increase in the number of publications related
to criticality in different areas of neuroscience. The potential of criticality to explain various …
to criticality in different areas of neuroscience. The potential of criticality to explain various …
Brain entropy, fractal dimensions and predictability: A review of complexity measures for EEG in healthy and neuropsychiatric populations
There has been an increasing trend towards the use of complexity analysis in quantifying
neural activity measured by electroencephalography (EEG) signals. On top of revealing …
neural activity measured by electroencephalography (EEG) signals. On top of revealing …
Classification of focal and non-focal EEG signals using neighborhood component analysis and machine learning algorithms
Background: Classification and localization of focal epileptic seizures provide a proper
diagnostic procedure for epilepsy patients. Visual identification of seizure activity from long …
diagnostic procedure for epilepsy patients. Visual identification of seizure activity from long …
Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal
Diagnosing depression in the early curable stages is very important and may even save the
life of a patient. In this paper, we study nonlinear analysis of EEG signal for discriminating …
life of a patient. In this paper, we study nonlinear analysis of EEG signal for discriminating …
Methods for classifying depression in single channel EEG using linear and nonlinear signal analysis
M Bachmann, L Päeske, K Kalev, K Aarma… - Computer methods and …, 2018 - Elsevier
Abstract Background and Objective Depressive disorder is one of the leading causes of
burden of disease today and it is presumed to take the first place in the world in 2030. Early …
burden of disease today and it is presumed to take the first place in the world in 2030. Early …
Electroencephalogram (EEG)-based computer-aided technique to diagnose major depressive disorder (MDD)
Abstract Recently, Electroencephalogram (EEG)-based computer-aided (CAD) techniques
have shown their promise as decision-making tools to diagnose major depressive disorder …
have shown their promise as decision-making tools to diagnose major depressive disorder …
Epileptic EEG classification based on extreme learning machine and nonlinear features
Q Yuan, W Zhou, S Li, D Cai - Epilepsy research, 2011 - Elsevier
The automatic detection and classification of epileptic EEG are significant in the evaluation
of patients with epilepsy. This paper presents a new EEG classification approach based on …
of patients with epilepsy. This paper presents a new EEG classification approach based on …
A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD)
Major depressive disorder (MDD), a debilitating mental illness, could cause functional
disabilities and could become a social problem. An accurate and early diagnosis for …
disabilities and could become a social problem. An accurate and early diagnosis for …
[HTML][HTML] Major depression detection from EEG signals using kernel eigen-filter-bank common spatial patterns
Major depressive disorder (MDD) has become a leading contributor to the global burden of
disease; however, there are currently no reliable biological markers or physiological …
disease; however, there are currently no reliable biological markers or physiological …
Nonlinear dynamical analysis of sleep electroencephalography using fractal and entropy approaches
The analysis of electroencephalography (EEG) recordings has attracted increasing interest
in recent decades and provides the pivotal scientific tool for researchers to quantitatively …
in recent decades and provides the pivotal scientific tool for researchers to quantitatively …