Sleep stages classification based on feature extraction from music of brain.

Sleep stages classification one of the essential factors concerning sleep disorder diagnoses, which can contribute to many functional disease treatments or prevent the primary cognitive risks in daily activities. In this study, A novel method of mapping EEG signals to music is proposed to classify sleep stages. A total of 4.

Distinct Neural Connectivity Patterns During Music Listening and Imagination: An Electroencephalography Study.

The brain's function changes during various activities, and numerous studies have explored this field. An intriguing and significant area of research is the brain's functioning during imagination and periods of inactivity. This study explores the differences in brain connectivity during music listening and imagination: by identifying distinct neural connectivity patterns and providing insights into the cognitive mechanisms underlying auditory imagination.

Prediction of Expanded Disability Status Scale in patients with MS using deep learning.

Multiple sclerosis (MS) is a chronic neurological condition that leads to significant disability in patients. Accurate prediction of disease progression, specifically the Expanded Disability Status Scale (EDSS), is crucial for personalizing treatment and improving patient outcomes. This study aims to develop a robust deep neural network framework to predict EDSS in MS patients using MRI scans.

Possible Neuropathological Mechanisms Underlying the Increased Complexity of Brain Electrical Activity in Schizophrenia: A Computational Study.

Schizophrenia is a complex neurodevelopmental illness that is associated with different deficits in the cerebral cortex and neural networks, resulting in irregularity of brain waves. Various neuropathological hypotheses have been proposed for this irregularity that we intend to examine in this computational study. We used a mathematical model of a neuronal population based on cellular automata to examine two hypotheses about the neuropathology of schizophrenia: first, reducing neuronal stimulation thresholds to increase neuronal excitability; and second, increasing the percentage of excitatory neurons and decreasing the percentage of inhibitory neurons to increase the excitation to inhibition ratio in the neuronal population.

A Comparative Study of Correlation Methods in Functional Connectivity Analysis Using fMRI Data of Alzheimer's Patients.

Background: Functional Magnetic Resonance Imaging (fMRI) is a non-invasive neuroimaging tool, used in brain function research and is also a low-frequency signal, showing brain activation by means of Oxygen consumption.

Objective: One of the reliable methods in brain functional connectivity analysis is the correlation method. In correlation analysis, the relationship between two time-series has been investigated.

Analysis of EEG brain connectivity of children with ADHD using graph theory and directional information transfer.

Research shows that Attention Deficit Hyperactivity Disorder (ADHD) is related to a disorder in brain networks. The purpose of this study is to use an effective connectivity measure and graph theory to examine the impairments of brain connectivity in ADHD. Weighted directed graphs based on electroencephalography (EEG) signals of 61 children with ADHD and 60 healthy children were constructed.

Epilepsy seizure prediction with few-shot learning method.

Epileptic seizures prediction and timely alarms allow the patient to take effective and preventive actions. In this paper, a convolutional neural network (CNN) is proposed to diagnose the preictal period. Our goal is for those epileptic patients in whom seizures occur late and it is very challenging to record the preictal signal for them.

Epileptic seizure prediction based on features extracted from lagged Poincaré plots.

Objective: The present work proposes a new epileptic seizure prediction method based on lagged Poincaré plot analysis of heart rate (HR).

Methods: In this article, the Poincaré plots with six different lags (1-6) were constructed for four episodes of heart rate variability (HRV) before the seizures. Moreover, two features were extracted based on lagged Poincare plots, which include the angle between the time series and the ellipse density fitted to the RR points.

A Spherical Phase Space Partitioning Based Symbolic Time Series Analysis (SPSP-STSA) for Emotion Recognition Using EEG Signals.

Emotion recognition systems have been of interest to researchers for a long time. Improvement of brain-computer interface systems currently makes EEG-based emotion recognition more attractive. These systems try to develop strategies that are capable of recognizing emotions automatically.

Estimating brain effective connectivity from EEG signals of patients with autism disorder and healthy individuals by reducing volume conduction effect.

Studying brain connectivity has shed light on understanding brain functions. Electroencephalogram signals recorded from the scalp surface comprise inter-dependent multi-channel signals each of which is a linear combination of simultaneously active brain sources as well as adjacent non-brain sources whose activity is widely volume conducted to the scalp through overlapping patterns. Evaluation of brain connectivity based on multivariate autoregressive (MVAR) model identification from neurological time series can be a proper tool for brain signal analysis.

Evaluating the Effect of Quran Memorizing on the Event-related Potential Features by Using Graphs Created from the Neural Gas Networks.

Background: Quran memorizing causes a state of trance, which its result is the changes in the amplitude and time of P300 and N200 components in the event related potential (ERP) signal. Nevertheless, a limited number of studies that have examined the effects of Quran memorizing on brain signals to enhance relaxation and attention, and improve the lives of patients with autism and stroke, generally have not presented any analysis based on comparing structural differences relevant to features extracted from ERP signal obtained from the two groups of Quran memorizer and nonmemorizer by using the hybrid of graph theory and competitive networks.

Methods: In this study, we investigated structural differences relevant to the graph obtained from the weight of neural gas (NG) and growing NG (GNG) networks trained by features extracted from the ERP signal recorded from two groups during the PRM test.

Analysis of Effective Connectivity Strength in Children with Attention Deficit Hyperactivity Disorder Using Phase Transfer Entropy.

This study aimed to investigate differences in brain networks between healthy children and children with attention deficit hyperactivity disorder (ADHD) during an attention test. To fulfill this, we constructed weighted directed graphs based on Electroencephalography (EEG) signals of 61 children with ADHD and 60 healthy children with the same age. Nodes of graphs were 19 EEG electrodes, and the edges were phase transfer entropy (PTE) between each pair of electrodes.

Computational Neuroscience Approach to Psychiatry: A Review on Theory-driven Approaches.

Translating progress in neuroscience into clinical benefits for patients with psychiatric disorders is challenging because it involves the brain as the most complex organ and its interaction with a complex environment and condition. Dealing with such complexity requires powerful techniques. Computational neuroscience approach to psychiatry integrates multiple levels and types of simulation, analysis and computation according to the different types of computational models to enhance comprehending, prediction and treatment of psychiatric disorder.

First Event-Related Potentials Evidence of Auditory Morphosyntactic Processing in a Subject-Object-Verb Nominative-Accusative Language (Farsi).

While most studies on neural signals of online language processing have focused on a few-usually western-subject-verb-object (SVO) languages, corresponding knowledge on subject-object-verb (SOV) languages is scarce. Here we studied Farsi, a language with canonical SOV word order. Because we were interested in the consequences of second-language acquisition, we compared monolingual native Farsi speakers and equally proficient bilinguals who had learned Farsi only after entering primary school.

A fuzzy sensitivity analysis approach to estimate brain effective connectivity and its application to epileptic seizure detection.

The brain is considered to be the most complicated organ in human body. Inferring and quantification of effective (causal) connectivity among regions of the brain is an important step in characterization of its complicated functions. The proposed method is comprised of modeling multivariate time series with Adaptive Neurofuzzy Inference System (ANFIS) and carrying out a sensitivity analysis using Fuzzy network parameters as a new approach to introduce a connectivity measure for detecting causal interactions between interactive input time series.

The Relation between Chaotic Feature of Surface EEG and Muscle Force: Case Study Report.

Background: Nonlinear dynamics, especially the chaos characteristics, are useful in analyzing bio-potentials with many complexities. In this study, the evaluation of arm-tip force estimation method from the electroencephalography (EEG) signal in the vertical plane has been studied and chaos characteristics, including fractal dimension, Lyapunov exponent, entropy, and correlation dimension characteristics of EEG signals have been measured and analyzed at different levels of forces.

Method: Electromyography signal was recorded with the help of the BIOPEC device (the Mp-100 model) and from the forearm muscle with surface electrodes, and the EEG signals were recorded from five major motor-related cortical areas according to 10-20 standard three times in a normal healthy 33-year-old male, athlete and right handed simultaneously with importing a force to 10 sinkers weighing from 10 to 100 Newton with step 10 Newton.

Direction of information flow between brain regions in ADHD and healthy children based on EEG by using directed phase transfer entropy.

Directed information flow between brain regions might be disrupted in children with Attention Deficit Hyperactivity Disorder (ADHD) which is related to the behavioral characteristics of ADHD. This paper aims to investigate the different information pathways of brain networks in children with ADHD in comparison with healthy subjects. EEG recordings were obtained from 61 children with ADHD and 60 healthy children without neurological disorders during attentional visual task.

Effect of inhibition indexed by auditory P300 on transmission of visual sensory information.

Early electroencephalographic studies that focused on finding brain correlates of psychic events led to the discovery of the P300. Since then, the P300 has become the focus of many basic and clinical neuroscience studies. However, despite its wide applications, the underlying function of the P300 is not yet clearly understood.

Deep sparse graph functional connectivity analysis in AD patients using fMRI data.

Functional magnetic resonance imaging (fMRI) is a non-invasive method that helps to analyze brain function based on BOLD signal fluctuations. Functional Connectivity (FC) catches the transient relationship between various brain regions usually measured by correlation analysis. The elements of the correlation matrix are between -1 to 1.

Frequency-specific network effective connectivity: ERP analysis of recognition memory process by directed connectivity estimators.

Human memory retrieval is one of the brain's most important, and least understood cognitive mechanisms. Traditionally, research on this aspect of memory has focused on the contributions of particular brain regions to recognition responses, but the interaction between regions may be of even greater importance to a full understanding. In this study, we examined patterns of network connectivity during retrieval in a recognition memory task.

EEG signal analysis during Ishihara's test in subjects with normal vision and color vision deficiency.

Color Vision Deficiency (CVD) is one of the most common types of vision deficiency. People with CVD have difficulty seeing color spectra depending on what types of retina photoreceptors are impaired. In this paper, the Ishihara test with 38 plates was used to examine the Electroencephalogram (EEG) of ten subjects with CVD plus ten healthy individuals.

Multiclass classification of patients during different stages of Alzheimer's disease using fMRI time-series.

Alzheimer's Disease (AD) begins several years before the symptoms develop. It starts with Mild Cognitive Impairment (MCI) which can be separated into Early MCI and Late MCI (EMCI and LMCI). Functional connectivity analysis and classification are done among the different stages of illness with Functional Magnetic Resonance Imaging (fMRI).