Phase Synchronization Between EEG Signals as a Function of Differences Between Stimuli Characteristics

2019 ◽  
Author(s):  
L. ten Bosch ◽  
K. Mulder ◽  
L. Boves
Keyword(s):  
Phase Synchronization ◽  
Eeg Signals

scholarly journals DeqiInduction by HT7 Acupuncture Alters Theta and Alpha Band Coherence in Human Healthy Subjects

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Go-Eun Lee ◽  
Jong-Min Yun ◽  
Seung-Bum Yang ◽  
Yeonseok Kang ◽  
Hyung-Won Kang ◽  
...  
Keyword(s):  
Phase Synchronization ◽  
Phase Coherence ◽  
Alpha Band ◽  
Eeg Signals ◽  
Temporal Area ◽  
Acupuncture Stimulation ◽  
The Central Nervous System ◽  
Preliminary Study ◽  
Young Subjects ◽  
Electroencephalogram Eeg

The aim of this preliminary study is to investigate the changes in phase synchronization in the theta and alpha bands before and during the performance of classical acupuncture on the Sinmun (HT7). The electroencephalogram (EEG) signals from nine healthy young subjects were recorded before and during acupuncture in the “closed-eye” state. The EEG signals were acquired from 19 surface scalp electrodes (FP1, FP2, F7, F3, Fz F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, and O2). Needles were inserted into the HT7 bilaterally and were then manipulated to inducedeqiand retained for 15 minutes. Phase synchronization was measured by phase coherence. In the theta band, coherence significantly increased between the temporal (T5, T6) and occipital areas (O1, O2) during the acupuncture stimulation. In the alpha band, coherence significantly increased between the left temporal area (T5) and other areas (frontal, parietal, and occipital). Phase coherence in the theta and alpha bands tended to increase during the retention of the acupuncture needles afterdeqi. Therefore, it can be concluded that acupuncture stimulation withdeqiis clinically effective via the central nervous system (CNS).

Feature Extraction from Distributions of Phase Synchronization Valuesof EEG Recordings

2021 ◽  
Author(s):  
◽  
J. A. Quirarte-Tejeda
Keyword(s):  
Continuous Monitoring ◽  
Prediction Models ◽  
Phase Synchronization ◽  
Treatment Success ◽  
Eeg Signals ◽  
Degree Of Certainty ◽  
Eeg Recordings ◽  
High Degree ◽  
Neurological Pathology ◽  
Phase Differences

Epilepsy is the most common neurological pathology. Despite treatments available to patients, only 58% to 73% will be free of seizures. This uncertainty in treatment outcomes can lead to other psychiatric affectations in ca-ses where treatment success may be in doubt. Seizure prediction models (SPMs) emerged as a measure to help determine when patients may be susceptible to an imminent crisis. These models are based on the continuous monitoring of patient’s EEG signals and subsequent continuous analysis to identify features that differentiate ictal from interictal states. This is an ongoing field of research whose aim is to establish a robust set of features to feed the SPM and obtain a high degree of certainty regarding when the next seizure will occur. In this work we propose the analysis of phase differences of EEG as a method to extract features capable of discriminating ictal and preictal states in patients; specifically, the numeric distance between Q1 and Q3 of the distribution of phase differences. We compared this values to other phase synchronization methods and tested our hypothesis getting a p < 0.0009 with our proposed method.

scholarly journals Detecting Phase-Synchrony Connectivity Anomalies in EEG Signals. Application to Dyslexia Diagnosis

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7061
Author(s):  
Marco A. Formoso ◽  
Andrés Ortiz ◽  
Francisco J. Martinez-Murcia ◽  
Nicolás Gallego ◽  
Juan L. Luque
Keyword(s):  
Auditory Processing ◽  
Phase Synchronization ◽  
Detection System ◽  
Brain Network ◽  
Learning Disorders ◽  
Eeg Signals ◽  
Functional Brain Network ◽  
Area Under Roc Curve ◽  
Band Limited ◽  
Behavioural Tests

Objective Dyslexia diagnosis is a challenging task, since traditional diagnosis methods are not based on biological markers but on behavioural tests. Although dyslexia diagnosis has been addressed by these tests in clinical practice, it is difficult to extract information about the brain processes involved in the different tasks and, then, to go deeper into its biological basis. Thus, the use of biomarkers can contribute not only to the diagnosis but also to a better understanding of specific learning disorders such as dyslexia. In this work, we use Electroencephalography (EEG) signals to discover differences among controls and dyslexic subjects using signal processing and artificial intelligence techniques. Specifically, we measure phase synchronization among channels, to reveal the functional brain network activated during auditory processing. On the other hand, to explore synchronicity patterns risen by low-level auditory processing, we used specific stimuli consisting in band-limited white noise, modulated in amplitude at different frequencies. The differential information contained in the functional (i.e., synchronization) network has been processed by an anomaly detection system that addresses the problem of subjects variability by an outlier-detection method based on vector quantization. The results, obtained for 7 years-old children, show that the proposed method constitutes an useful tool for clinical use, with the area under ROC curve (AUC) values up to 0.95 in differential diagnosis tasks.

scholarly journals A Novel Approach to Study the Effects of Anesthesia on Respiratory Signals by using the EEG Signals

2017 ◽  
Vol 6 (2) ◽  
pp. 117
Author(s):  
Mohd Suhaib Kidwai ◽  
S. Hasan Saeed
Keyword(s):  
General Anesthesia ◽  
Phase Synchronization ◽  
Eeg Signals ◽  
Drug Induced ◽  
Anesthetic Depth ◽  
Brain Signals ◽  
Novel Approach ◽  
Respiratory Parameters ◽  
Single Coupling ◽  
Separate Subsystem

General anesthesia plays a crucial role in many surgical procedures. It is a drug-induced, reversible state characterized by unconsciousness, anti-nociception or analgesia, immobility and amnesia. On rare occasions, however, the patient can remain unconscious longer than intended, or may regain awareness during surgery. There are no precise measures for maintaining the correct dose of anesthetic, and there is currently no fully reliable instrument to monitor depth of anesthesia. Although a number of devices for monitoring brain function or sympathetic output are commercially available, the anesthetist also relies on clinical assessment and experience to judge anesthetic depth. The undesirable consequences of overdose or unintended awareness might in principle be ameliorated by improved control if we could understand better the changes in function that occur during general anesthesia. Coupling functions prescribe the physical rule specifying how the inter-oscillator interactions occur. They determine the possibility of qualitative transitions between the oscillations, e.g. routes into and out of phase synchronization. Their decomposition can describe the functional contribution from each separate subsystem within a single coupling relationship. In this way, coupling functions offer a unique means of describing mechanisms in a unified and mathematically precise way. It is a fast growing field of research, with much recent progress on the theory and especially towards being able to extract and reconstruct the coupling functions between interacting oscillations from data, leading to useful applications in cardio respiratory interactions.<br />In this paper, a novel approach has been proposed for detecting the changes in synchronism of brain signals, taken from EEG machine. During the effect of anesthesia, there are certain changes in the EEG signals. Those signals show changes in their synchronism. This phenomenon of synchronism can be utilized to study the effect of anesthesia on respiratory parameters like respiration rate etc, and hence the quantity of anesthesia can be regulated, and if any problem occurs in breathing during the effect of anesthesia on patient, that can also be monitored

Phase Synchronization Analysis of EEG Signals: An Evaluation Based on Surrogate Tests

2012 ◽  
Vol 59 (8) ◽  
pp. 2254-2263 ◽  
Author(s):  
Junfeng Sun ◽  
Xiangfei Hong ◽  
Shanbao Tong
Keyword(s):  
Phase Synchronization ◽  
Eeg Signals

scholarly journals EEG Fingerprints: Phase Synchronization of EEG Signals as Biomarker for Subject Identification

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 121165-121173 ◽  
Author(s):  
Wanzeng Kong ◽  
Luyun Wang ◽  
Sijia Xu ◽  
Fabio Babiloni ◽  
Hang Chen
Keyword(s):  
Phase Synchronization ◽  
Eeg Signals ◽  
Subject Identification

scholarly journals K-th Nearest Neighbor (KNN) Entropy Estimates of Complexity and Integration from Ongoing and Stimulus-Evoked Electroencephalographic (EEG) Recordings of the Human Brain

Entropy ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 61
Author(s):  
Logan Trujillo
Keyword(s):  
Human Brain ◽  
Nearest Neighbor ◽  
Phase Synchronization ◽  
Mental States ◽  
Statistical Dependence ◽  
Eeg Signals ◽  
Multivariate Statistical ◽  
Dynamical Interaction ◽  
Information Theoretic ◽  
Brain States

Information-theoretic measures for quantifying multivariate statistical dependence have proven useful for the study of the unity and diversity of the human brain. Two such measures–integration, I(X), and interaction complexity, CI(X)–have been previously applied to electroencephalographic (EEG) signals recorded during ongoing wakeful brain states. Here, I(X) and CI(X) were computed for empirical and simulated visually-elicited alpha-range (8–13 Hz) EEG signals. Integration and complexity of evoked (stimulus-locked) and induced (non-stimulus-locked) EEG responses were assessed using nonparametric k-th nearest neighbor (KNN) entropy estimation, which is robust to the nonstationarity of stimulus-elicited EEG signals. KNN-based I(X) and CI(X) were also computed for the alpha-range EEG of ongoing wakeful brain states. I(X) and CI(X) patterns differentiated between induced and evoked EEG signals and replicated previous wakeful EEG findings obtained using Gaussian-based entropy estimators. Absolute levels of I(X) and CI(X) were related to absolute levels of alpha-range EEG power and phase synchronization, but stimulus-related changes in the information-theoretic and other EEG properties were independent. These findings support the hypothesis that visual perception and ongoing wakeful mental states emerge from complex, dynamical interaction among segregated and integrated brain networks operating near an optimal balance between order and disorder.

scholarly journals A broadband method of quantifying phase synchronization for discriminating seizure EEG signals

2019 ◽  
Vol 52 ◽  
pp. 371-383 ◽  
Author(s):  
Lei Wang ◽  
Xi Long ◽  
Ronald M. Aarts ◽  
Johannes P. van Dijk ◽  
Johan B.A.M. Arends
Keyword(s):  
Phase Synchronization ◽  
Eeg Signals
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