Correlation and coherence analysis of the EEG: A selective tutorial review
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The present study was conducted to detect the sleep stages by electroencephalography (EEG) using chaotic features. The method used in this study was the content analysis method. First, the sleep stages and EEG have been analyzed, and the EEG with chaotic features was used to detect the sleep stages. Detection of artifacts in sleep electroencephalography (EEG) is one of the vital tasks in the pre-processing stage. Despite many artifact exploration algorithms over the years, lots of them lose their advantages to use sleep EEG. Types of brain activities can be measured, and the involved brain areas can be detected using EEG. Electroencephalography (EEG) signal includes different rhythms, which are dependent on various sensory and movement conditions. Detection of each rhythm of this signal needs experience and skills. As a result, analysis of the signal recorded by EEG can be used widely for detection and academic purposes.
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EEG-fMRI
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Neural activity across the brain shows both spatial and temporal correlations at multiple scales, and understanding these correlations is a key step toward understanding cortical processing. Correlation in the local field potential (LFP) recorded from two brain areas is often characterized by computing the coherence, which is generally taken to reflect the degree of phase consistency across trials between two sites. Coherence, however, depends on two factors—phase consistency as well as amplitude covariation across trials—but the spatial structure of amplitude correlations across sites and its contribution to coherence are not well characterized. We recorded LFP from an array of microelectrodes chronically implanted in the primary visual cortex of monkeys and studied correlations in amplitude across electrodes as a function of interelectrode distance. We found that amplitude correlations showed a similar trend as coherence as a function of frequency and interelectrode distance. Importantly, even when phases were completely randomized between two electrodes, amplitude correlations introduced significant coherence. To quantify the contributions of phase consistency and amplitude correlations to coherence, we simulated pairs of sinusoids with varying phase consistency and amplitude correlations. These simulations confirmed that amplitude correlations can significantly bias coherence measurements, resulting in either over- or underestimation of true phase coherence. Our results highlight the importance of accounting for the correlations in amplitude while using coherence to study phase relationships across sites and frequencies.
Local field potential
Degree of coherence
Phase coherence
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Magnetoencephalography
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Electroencephalography (EEG) is an essential investigative tool for use in young people with epilepsy. This study assesses the effects of different EEG protocols on the yield of EEG abnormalities in young people with possible new epilepsy.85 patients presenting to the unit underwent three EEGs with differing protocols: routine EEG (r-EEG), sleep-deprived EEG (SD-EEG), EEG carried out during drug-induced sleep (DI-EEG). The yield of EEG abnormalities was compared using each EEG protocol.98 patients were recruited to the study. Of the 85 patients who completed the study, 33 (39%) showed no discernible abnormality on any of their EEG recordings. 36 patients (43%) showed generalised spike and wave during at least one EEG recording, whereas 15 (18%) had a focal discharge evident at some stage. SD-EEG had a sensitivity of 92% among these patients, whereas the sensitivity of DI-EEG and r-EEG was 58% and 44%, respectively. The difference between the yield from SD-EEG was significantly higher than that from other protocols (p < 0.001). Among the 15 patients showing focal discharges, SD-EEG provoked abnormalities in 11 (73%). r-EEG and DI-EEG each produced abnormalities in 40% and 27%, respectively. 7 patients (47%) had changes seen only after sleep deprivation. In 2 (13%), the only abnormalities were seen on r-EEG. In only 1 patient with focal discharges (7%) was the focal change noted solely after drug-induced sleep. These differences did not reach significance.EEG has an important role in the classification of epilepsies. SD-EEG is an easy and inexpensive way of increasing the yield of EEG abnormalities. Using this as the preferred protocol may help reduce the numbers of EEGs carried out in young patients presenting with epilepsy.
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To compare the quality of electroencephalography (EEG) signals recorded with a rapid response EEG system and the signals recorded with conventional clinical EEG recordings.We studied the differences between EEG recordings taken with a rapid response EEG system (Ceribell) compared to conventional EEG through two separate set of studies. First, we conducted simultaneous recording on a healthy subject in an experimental laboratory setting where the rapid response EEG and two conventional EEG recording systems (Nihon Kohden and Natus) were used at the same time on the same subject using separate but adjacently placed electrodes. The rapid response EEG was applied by a user without prior training in EEG set up while two separate sets of conventional EEG electrodes were placed by a trained EEG technologist. The correlation between each of the recordings was calculated and quantitatively compared. In the second study, we performed a set of consecutive recordings on 22 patients in an ICU environment. The rapid response EEG system was applied by clinical ICU fellows without prior training in EEG set up while waiting for the conventional EEG system to arrive, after which the rapid response EEG was stopped and the conventional EEG was applied by a trained EEG technologist. We measured and compared several metrics of EEG quality using comparative metrics.For the simultaneous recording performed in a laboratory environment, the tested rapid response EEG and conventional EEG recordings showed agreement when aligned and visually compared in the time domain, all EEG waveform features were distinguishable in both recordings. The correlation between each pair of recordings also showed that the correlation between the rapid response EEG recording and each of the two conventional recordings was statistically the same as the correlation between the two conventional recordings. For the consecutive recordings performed in real life clinical ICU environment, Hjorth parameters, spike count, baseline wander, and kurtosis measures were statistically similar (p > 0.05, Wilcoxon signed rank test) for the rapid response EEG and conventional clinical EEG recordings. The rapid response EEG data had significantly lower 60 Hz noise compared to recordings made with the conventional systems both in laboratory and ICU settings. Lastly, the clinical information obtained with the rapid response EEG system was concordant with the diagnostic information obtained with the conventional EEG recordings in the ICU setting.Our findings show that the tested rapid response EEG system provides EEG recording quality that is equivalent to conventional EEG systems and even better when it comes to 60 Hz noise level. The concordance between the rapid response EEG and conventional EEG systems was demonstrated both in a controlled laboratory environment as well as in the noisy environment of a hospital ICU on patients with altered mental status.Our findings clearly confirm that the tested rapid response EEG system provides EEG data that is equivalent in quality to the recordings made using conventional EEG systems despite the fact that the rapid response system can be applied within few minutes and with no reliance on specialized technologists. This can be important for urgent situations where the use of conventional EEG systems is hindered by the lengthy setup time and limited availability of EEG technologists.
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Brain Function
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Burst-suppression EEG (BS-EEG) after cardiopulmonary resuscitation implies a bad prognosis, but little is known of the temporal dynamics of postanoxic BS-EEG. The authors studied 24 consecutive patients who developed BS-EEG within 24 hours after cardiopulmonary resuscitation, and followed 20 of these patients with serial EEGs. Except for one patient, BS-EEG was followed by another EEG pattern within 1 day, mainly areactive alpha EEG (n = 6), isoelectric EEG (n = 5), generalized continuous epileptiform discharges (n = 4), or theta; EEG (n = 3). The coexistence of different EEG patterns in the same recording was seen in 10 patients. Serial recordings disclosed a variety of EEG sequences with (often subtle) transitions between the different EEG patterns, including reappearance of BS-EEG. Postanoxic BS-EEG is followed by a variety of EEG sequences composed of different EEG patterns, each of which is recognized as an unfavorable sign in and of itself. The coexistence of different unfavorable EEG patterns in the same recording, and transitions between these EEG patterns in subsequent recordings, are common in patients with postanoxic BS-EEG. It seems reasonable to speculate that BS-EEG and subsequently evolving EEG patterns in anoxic encephalopathy reflect different forms of neocortical dysfunction, which occur at different stages of a dynamic process, leading ultimately to severe neuronal loss.
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This paper investigated the interregional correlation changed by sport training through electroencephalography (EEG) signals using the techniques of classification and feature selection. The EEG data are obtained from students with long-time professional sport training and normal students without sport training as baseline. Every channel of the 19-channel EEG signals is considered as a node in the brain network and Pearson Correlation Coefficients are calculated between every two nodes as the new features of EEG signals. Then, the Partial Least Square (PLS) is used to select the top 10 most varied features and Pearson Correlation Coefficients of selected features are compared to show the difference of two groups. Result shows that the classification accuracy of two groups is improved from 88.13% by the method using measurement of EEG overall energy to 97.19% by the method using EEG correlation measurement. Furthermore, the features selected reveal that the most important interregional EEG correlation changed by training is the correlation between left inferior frontal and left middle temporal with a decreased value.
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