scholarly journals Increased Phase Cone Turnover in 80–250 Hz Bands Occurs in the Epileptogenic Zone During Interictal Periods

2020 ◽  
Vol 14 ◽  
Author(s):  
Ceon Ramon ◽  
Mark D. Holmes
Keyword(s):  
Cortical Excitability ◽  
Stable Phase ◽  
Spatial Gradient ◽  
Epileptogenic Zone ◽  
Instantaneous Phase ◽  
Eeg Data ◽  
Contour Plots ◽  
Consecutive Time ◽  
Eeg Recordings ◽  
Cortical Regions

We found that phase cone clustering patterns in EEG ripple bands demonstrate an increased turnover rate in epileptogenic zones compared to adjacent regions. We employed 256 channel EEG data collected in four adult subjects with refractory epilepsy. The analysis was performed in the 80–150 and 150–250 Hz ranges. Ictal onsets were documented with intracranial EEG recordings. Interictal scalp recordings, free of epileptiform patterns, of 240-s duration, were selected for analysis for each subject. The data was filtered, and the instantaneous phase was extracted after the Hilbert transformation. Spatiotemporal contour plots of the unwrapped instantaneous phase with 1.0 ms intervals were constructed using a montage layout of the 256 electrode positions. Stable phase cone patterns were selected based on criteria that the sign of spatial gradient did not change for a minimum of three consecutive time samples and the frame velocity was consistent with known propagation velocities of cortical axons. These plots exhibited increased dynamical formation and dissolution of phase cones in the ictal onset zones, compared to surrounding cortical regions, in all four patients. We believe that these findings represent markers of abnormally increased cortical excitability. They are potential tools that may assist in localizing the epileptogenic zone.

scholarly journals Oscillatory Patterns of Phase Cone Formations near to Epileptic Spikes Derived from 256-Channel Scalp EEG Data

2018 ◽  
Vol 2018 ◽  
pp. 1-15
Author(s):  
Ceon Ramon ◽  
Mark D. Holmes ◽  
Mackenzie V. Wise ◽  
Don Tucker ◽  
Kevin Jenson ◽  
...  
Keyword(s):  
Cortical Neurons ◽  
High Density ◽  
State Transitions ◽  
Stable Phase ◽  
Spatial Gradient ◽  
Scalp Eeg ◽  
Contour Plots ◽  
Epileptic Spikes ◽  
Consecutive Time ◽  
Eeg Recordings

Our objective was to determine if there are any distinguishable phase cone clustering patterns present near to epileptic spikes. These phase cones arise from episodic phase shifts due to the coordinated activity of cortical neurons at or near to state transitions and can be extracted from the high-density scalp EEG recordings. The phase cone clustering activities in the low gamma band (30–50 Hz) and in the ripple band (80–150 Hz) were extracted from the analytic phase after taking Hilbert transform of the 256-channel high density (dEEG) data of adult patients. We used three subjects in this study. Spatiotemporal contour plots of the unwrapped analytic phase with 1.0 ms intervals were constructed using a montage layout of 256 electrode positions. Stable phase cone patterns were selected based on the criteria that the sign of the spatial gradient did not change for at least three consecutive time samples and the frame velocity was within the range of propagation velocities of cortical axons. These plots exhibited dynamical formation of phase cones which were higher in the seizure area as compared with the nearby surrounding brain areas. Spatiotemporal oscillatory patterns were also visible during ±5 sec period from the location of the spike. These results suggest that the phase cone activity might be useful for noninvasive localization of epileptic sites and also for examining the cortical neurodynamics near to epileptic spikes.

scholarly journals Efficient automated localization of ECoG electrodes in CT images via shape analysis

2021 ◽  
Vol 16 (4) ◽  
pp. 543-554
Author(s):  
Jessica Centracchio ◽  
Antonio Sarno ◽  
Daniele Esposito ◽  
Emilio Andreozzi ◽  
Luigi Pavone ◽  
...  
Keyword(s):  
Shape Analysis ◽  
Focal Epilepsy ◽  
Support Vector ◽  
Epileptogenic Zone ◽  
Platinum Electrodes ◽  
Public Repository ◽  
Automated Method ◽  
Depth Electrodes ◽  
Ethical Approval ◽  
Cortical Regions

Abstract Purpose People with drug-refractory epilepsy are potential candidates for surgery. In many cases, epileptogenic zone localization requires intracranial investigations, e.g., via ElectroCorticoGraphy (ECoG), which uses subdural electrodes to map eloquent areas of large cortical regions. Precise electrodes localization on cortical surface is mandatory to delineate the seizure onset zone. Simple thresholding operations performed on patients’ computed tomography (CT) volumes recognize electrodes but also other metal objects (e.g., wires, stitches), which need to be manually removed. A new automated method based on shape analysis is proposed, which provides substantially improved performances in ECoG electrodes recognition. Methods The proposed method was retrospectively tested on 24 CT volumes of subjects with drug-refractory focal epilepsy, presenting a large number (> 1700) of round platinum electrodes. After CT volume thresholding, six geometric features of voxel clusters (volume, symmetry axes lengths, circularity and cylinder similarity) were used to recognize the actual electrodes among all metal objects via a Gaussian support vector machine (G-SVM). The proposed method was further tested on seven CT volumes from a public repository. Simultaneous recognition of depth and ECoG electrodes was also investigated on three additional CT volumes, containing penetrating depth electrodes. Results The G-SVM provided a 99.74% mean classification accuracy across all 24 single-patient datasets, as well as on the combined dataset. High accuracies were obtained also on the CT volumes from public repository (98.27% across all patients, 99.68% on combined dataset). An overall accuracy of 99.34% was achieved for the recognition of depth and ECoG electrodes. Conclusions The proposed method accomplishes automated ECoG electrodes localization with unprecedented accuracy and can be easily implemented into existing software for preoperative analysis process. The preliminary yet surprisingly good results achieved for the simultaneous depth and ECoG electrodes recognition are encouraging. Ethical approval n°NCT04479410 by “IRCCS Neuromed” (Pozzilli, Italy), 30th July 2020.

scholarly journals Scale-Free Coupled Dynamics in Brain Networks Captured by Bivariate Focus-Based Multifractal Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Orestis Stylianou ◽  
Frigyes Samuel Racz ◽  
Andras Eke ◽  
Peter Mukli
Keyword(s):  
Resting State ◽  
Functional Coupling ◽  
Dependent Manner ◽  
Coupled Dynamics ◽  
Scale Free ◽  
Neural Processes ◽  
Eeg Recordings ◽  
Cortical Regions ◽  
The Brain

While most connectivity studies investigate functional connectivity (FC) in a scale-dependent manner, coupled neural processes may also exhibit broadband dynamics, manifesting as power-law scaling of their measures of interdependence. Here we introduce the bivariate focus-based multifractal (BFMF) analysis as a robust tool for capturing such scale-free relations and use resting-state electroencephalography (EEG) recordings of 12 subjects to demonstrate its performance in reconstructing physiological networks. BFMF was employed to characterize broadband FC between 62 cortical regions in a pairwise manner, with all investigated connections being tested for true bivariate multifractality. EEG channels were also grouped to represent the activity of six resting-state networks (RSNs) in the brain, thus allowing for the analysis of within- and between- RSNs connectivity, separately. Most connections featured true bivariate multifractality, which could be attributed to the genuine scale-free coupling of neural dynamics. Bivariate multifractality showed a characteristic topology over the cortex that was highly concordant among subjects. Long-term autocorrelation was higher in within-RSNs, while the degree of multifractality was generally found stronger in between-RSNs connections. These results offer statistical evidence of the bivariate multifractal nature of functional coupling in the brain and validate BFMF as a robust method to capture such scale-independent coupled dynamics.

scholarly journals Signal-space projection suppresses the tACS artifact in EEG recordings

2019 ◽  
Author(s):  
Johannes Vosskuhl ◽  
Tuomas P. Mutanen ◽  
Toralf Neuling ◽  
Risto J. Ilmoniemi ◽  
Christoph S. Herrmann
Keyword(s):  
Brain Activity ◽  
Current Source ◽  
Oscillatory Activity ◽  
Signal Space ◽  
Artifact Correction ◽  
Eeg Data ◽  
Before And After ◽  
Transcranial Alternating Current Stimulation ◽  
Space Projection ◽  
Eeg Recordings

1.AbstractBackgroundTo probe the functional role of brain oscillations, transcranial alternating current stimulation (tACS) has proven to be a useful neuroscientific tool. Because of the huge tACS-caused artifact in electroencephalography (EEG) signals, tACS–EEG studies have been mostly limited to compare brain activity between recordings before and after concurrent tACS. Critically, attempts to suppress the artifact in the data cannot assure that the entire artifact is removed while brain activity is preserved. The current study aims to evaluate the feasibility of specific artifact correction techniques to clean tACS-contaminated EEG data.New MethodIn the first experiment, we used a phantom head to have full control over the signal to be analyzed. Driving pre-recorded human brain-oscillation signals through a dipolar current source within the phantom, we simultaneously applied tACS and compared the performance of different artifact-correction techniques: sine subtraction, template subtraction, and signal-space projection (SSP). In the second experiment, we combined tACS and EEG on a human subject to validate the best-performing data-correction approach.ResultsThe tACS artifact was highly attenuated by SSP in the phantom and the human EEG; thus, we were able to recover the amplitude and phase of the oscillatory activity. In the human experiment, event-related desynchronization could be restored after correcting the artifact.Comparison with existing methodsThe best results were achieved with SSP, which outperformed sine subtraction and template subtraction.ConclusionsOur results demonstrate the feasibility of SSP by applying it to human tACS–EEG data.

Artifacts

2017 ◽  
pp. 98-127
Author(s):  
Riitta Hari ◽  
Aina Puce
Keyword(s):  
Data Analysis ◽  
Eye Movements ◽  
Cardiac Activity ◽  
Signal Space Separation ◽  
Eeg Data ◽  
Eeg Recordings ◽  
Power Line Noise ◽  
Post Hoc ◽  
Space Separation ◽  
The Brain

This chapter focuses on different types of biological and nonbiological artifacts in MEG and EEG recordings, and discusses methods for their recognition and removal. Examples are given of various physiological artifacts, including eye movements, eyeblinks, saccades, muscle, and cardiac activity. Nonbiological artifacts, such as power-line noise, are also demonstrated. Some examples are given to illustrate how these unwanted signals can be identified and removed from MEG and EEG signals with methods such as independent component analysis (as applied to EEG data) and temporal signal-space separation (applied to MEG data). However, prevention of artifacts is always preferable to removing or compensating for them post hoc during data analysis. The chapter concludes with a discussion of how to ensure that signals are emanating from the brain and not from other sources.

Integration of High Resolution EEG and Functional Magnetic Resonance in the Study of Human Movement-Related Potentials

2000 ◽  
Vol 39 (02) ◽  
pp. 179-182 ◽  
Author(s):  
F. Carducci ◽  
F. Cincotti ◽  
C. Del Gratta ◽  
G. M. Roberti ◽  
G. L. Romani ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Figure Of Merit ◽  
Human Movement ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Finger Extension ◽  
Eeg Data ◽  
Related Potentials ◽  
Standard Linear ◽  
Cortical Regions

Abstract:Cortical sources of human movement-related potentials (i.e. unilateral finger extension) were modeled using functional magnetic resonance imaging (fMR) data as a constraint of a linear inverse source estimation from highly sampled (128 channels) EEG data. Remarkably, this estimation was performed within realistic subject’s MR-constructed head models by boundary element techniques. An appropriate figure of merit served to set the optimal amount of fMR constraints. With respect to standard linear inverse source estimates, fMR-constrained ones presented increased spatial detail and provided a more reliable timing of activation in bilateral sensorimotor cortical regions of interest.

A step forward in the quest for a mobile EEG-designed epoch for psychophysiological studies

2019 ◽  
Vol 64 (6) ◽  
pp. 655-667 ◽  
Author(s):  
Sebastián A. Balart-Sánchez ◽  
Hugo Vélez-Pérez ◽  
Sergio Rivera-Tello ◽  
Fabiola R. Gómez Velázquez ◽  
Andrés A. González-Garrido ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
International System ◽  
Central Area ◽  
Behavioral Responses ◽  
Clinical Grade ◽  
Healthy Individuals ◽  
Eeg Data ◽  
Wide Range ◽  
Eeg Recordings ◽  
Visual Oddball

Abstract The aim of this study was to compare a reconfigurable mobile electroencephalography (EEG) system (M-EMOTIV) based on the Emotiv Epoc® (which has the ability to record up to 14 electrode sites in the 10/20 International System) and a commercial, clinical-grade EEG system (Neuronic MEDICID-05®), and then validate the rationale and accuracy of recordings obtained with the prototype proposed. In this approach, an Emotiv Epoc® was modified to enable it to record in the parieto-central area. All subjects (15 healthy individuals) performed a visual oddball task while connected to both devices to obtain electrophysiological data and behavioral responses for comparative analysis. A Pearson’s correlation analysis revealed a good between-devices correlation with respect to electrophysiological measures. The present study not only corroborates previous reports on the ability of the Emotiv Epoc® to suitably record EEG data but presents an alternative device that allows the study of a wide range of psychophysiological experiments with simultaneous behavioral and mobile EEG recordings.

scholarly journals Reducing Electrodes based on Decision Tree Classification for EEG Motor Movement Data

2018 ◽  
Vol 7 (3.12) ◽  
pp. 344
Author(s):  
Jayesh Deep Dubey ◽  
Deepak Arora ◽  
Pooja Khanna
Keyword(s):  
Random Forest ◽  
Information Gain ◽  
Random Forest Classifier ◽  
Attribute Selection ◽  
Average Decrease ◽  
Motor Movement ◽  
Movement Data ◽  
Decision Tree Classification ◽  
Eeg Data ◽  
Eeg Recordings

Analysis of EEG data is one of the most important parts of Brain Computer Interface systems because EEG data consists of a substantial amount of crucial information that can be used for better study and improvements in BCI system. One of the problems with the analysis of EEG is the large amount of data that is produced, some of which might not be useful for the analysis. Therefore identifying the relevant data from the large amount of EEG data is important for better analysis. The objective of this study is to find out the performance of Random Forest classifier on the motor movement EEG data and reducing the number of electrodes that are considered in the EEG recording and analysis so that the amount of data that is produced through EEG recording is reduced and only relevant electrodes are considered in the analysis. The dataset used in the study is Physionet motor movement/imagery data which consists of EEG recordings obtained using 64 electrodes. These 64 electrodes were ranked based on their information gain with respect to the class using Info Gain attribute selection algorithm. The electrodes were then divided into 4 lists. List 1 consists of top 18 ranked electrodes and number of electrodes was increased by 15 [in ranked order] in each subsequent list. List 2, 3 and 4 consists of top 33, 48 and 64 electrodes respectively. The accuracy of random forest classifier for each of the list was compared with the accuracy of the classifier for the List 4 which consists of all the 64 electrodes. The additional electrodes in the List 4 were rejected because the accuracy of the classifier was almost same for List 4 and List3. Through this method we were able to reduce the electrodes from 64 to 48 with an average decrease of only 0.9% in the accuracy of the classifier. This reduction in the electrode can substantially reduce the time and effort required for analysis of EEG data.      

scholarly journals Gender-Specific Hippocampal Dysrhythmia and Aberrant Hippocampal and Cortical Excitability in the APPswePS1dE9 Model of Alzheimer’s Disease

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Anna Papazoglou ◽  
Julien Soos ◽  
Andreas Lundt ◽  
Carola Wormuth ◽  
Varun Raj Ginde ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Primary Motor Cortex ◽  
Seizure Activity ◽  
Cortical Excitability ◽  
Memory Loss ◽  
Fast Fourier Transformation ◽  
Model Of Alzheimer’S Disease ◽  
Eeg Recordings ◽  
Gender Specific

Alzheimer’s disease (AD) is a multifactorial disorder leading to progressive memory loss and eventually death. In this study an APPswePS1dE9 AD mouse model has been analyzed using implantable video-EEG radiotelemetry to perform long-term EEG recordings from the primary motor cortex M1 and the hippocampal CA1 region in both genders. Besides motor activity, EEG recordings were analyzed for electroencephalographic seizure activity and frequency characteristics using a Fast Fourier Transformation (FFT) based approach. Automatic seizure detection revealed severe electroencephalographic seizure activity in both M1 and CA1 deflection in APPswePS1dE9 mice with gender-specific characteristics. Frequency analysis of both surface and deep EEG recordings elicited complex age, gender, and activity dependent alterations in the theta and gamma range. Females displayed an antithetic decrease in theta (θ) and increase in gamma (γ) power at 18-19 weeks of age whereas related changes in males occurred earlier at 14 weeks of age. In females, theta (θ) and gamma (γ) power alterations predominated in the inactive state suggesting a reduction in atropine-sensitive type II theta in APPswePS1dE9 animals. Gender-specific central dysrhythmia and network alterations in APPswePS1dE9 point to a functional role in behavioral and cognitive deficits and might serve as early biomarkers for AD in the future.

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