Brain Connectivity Estimation Pitfall in Multiple Trials of EEG Data

The electroencephalography signal is well suited to calculate brain connectivity due to its high temporal resolution. When the purpose is to compute connectivity from multi-trial EEG data, confusion arises about how these trials involved in calculating the connectivity. The purpose of this paper is to study this confusing issue using simulated and experimental data. To this end, Granger causality-based connectivity measures were considered. Using simulations, two signals were generated with known AR (Auto-Regressive) coefficients and then simple MVAR (Multivariate AR) models based on different numbers of trials were extracted. For accurate estimation of the MVAR model, the data samples should be sufficient. Two Granger causality-based connectivity, GC and PDC were estimated. Estimating connectivity corresponding to small trial numbers (5 and 10 trials) resulted in an average value of connectivity that is significantly higher and also more variable over different estimates. By increasing the number of trials, the MVAR model has fitted more appropriately to the data and the connectivity values were converged. This procedure was implemented on real EEG data. The obtained results agreed well with the findings of simulated data. The results showed that the brain connectivity should calculate for each trial, and then average the connectivity values on all trials. Also, the larger the trial numbers, the MVAR model has fitted more appropriately to the data, and connectivity estimations are more reliable.

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Estimating brain connectivity patterns from EEG: Performance of Granger Causality, PDC and PSI on simulated data

Neuroscience Letters ◽

10.1016/j.neulet.2011.05.189 ◽

2011 ◽

Vol 500 ◽

pp. e42

Author(s):

Stefan Haufe ◽

Vadim Nikulin ◽

Guido Nolte

Keyword(s):

Granger Causality ◽

Brain Connectivity ◽

Simulated Data ◽

Connectivity Patterns

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Magnetoencephalography in the Detection and Characterization of Brain Abnormalities Associated with Traumatic Brain Injury: A Comprehensive Review

Medical Sciences ◽

10.3390/medsci9010007 ◽

2021 ◽

Vol 9 (1) ◽

pp. 7

Author(s):

Geoffrey W. Peitz ◽

Elisabeth A. Wilde ◽

Ramesh Grandhi

Keyword(s):

Traumatic Brain Injury ◽

Brain Injury ◽

Imaging Techniques ◽

Brain Connectivity ◽

Functional Brain Imaging ◽

Original Research ◽

High Temporal Resolution ◽

Post Traumatic Stress ◽

Clinical Role

Magnetoencephalography (MEG) is a functional brain imaging technique with high temporal resolution compared with techniques that rely on metabolic coupling. MEG has an important role in traumatic brain injury (TBI) research, especially in mild TBI, which may not have detectable features in conventional, anatomical imaging techniques. This review addresses the original research articles to date that have reported on the use of MEG in TBI. Specifically, the included studies have demonstrated the utility of MEG in the detection of TBI, characterization of brain connectivity abnormalities associated with TBI, correlation of brain signals with post-concussive symptoms, differentiation of TBI from post-traumatic stress disorder, and monitoring the response to TBI treatments. Although presently the utility of MEG is mostly limited to research in TBI, a clinical role for MEG in TBI may become evident with further investigation.

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Source space effective connectivity analysis of MEG/EEG data using Kalman filter based time-varying Granger causality

Frontiers in Neuroinformatics ◽

10.3389/conf.fninf.2011.08.00023 ◽

2011 ◽

Vol 5 ◽

Author(s):

Gow David

Keyword(s):

Kalman Filter ◽

Granger Causality ◽

Effective Connectivity ◽

Time Varying ◽

Connectivity Analysis ◽

Eeg Data

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Analyzing Brain Connectivity in the Mutual Regulation of Emotion–Movement Using Bidirectional Granger Causality

Frontiers in Neuroscience ◽

10.3389/fnins.2020.00369 ◽

2020 ◽

Vol 14 ◽

Author(s):

Ting Li ◽

Guoqi Li ◽

Tao Xue ◽

Jinhua Zhang

Keyword(s):

Granger Causality ◽

Brain Connectivity ◽

Mutual Regulation ◽

Regulation Of Emotion

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Super-Resolution for Improving EEG Spatial Resolution using Deep Convolutional Neural Network—Feasibility Study

Sensors ◽

10.3390/s19235317 ◽

2019 ◽

Vol 19 (23) ◽

pp. 5317 ◽

Cited By ~ 4

Author(s):

Moonyoung Kwon ◽

Sangjun Han ◽

Kiwoong Kim ◽

Sung Chan Jun

Keyword(s):

Spatial Resolution ◽

Evoked Potential ◽

Mean Squared Error ◽

Simulated Data ◽

Super Resolution ◽

Auditory Evoked Potential ◽

Brain Dynamics ◽

Deep Convolutional Neural Networks ◽

Squared Error ◽

Eeg Data

Electroencephalography (EEG) has relatively poor spatial resolution and may yield incorrect brain dynamics and distort topography; thus, high-density EEG systems are necessary for better analysis. Conventional methods have been proposed to solve these problems, however, they depend on parameters or brain models that are not simple to address. Therefore, new approaches are necessary to enhance EEG spatial resolution while maintaining its data properties. In this work, we investigated the super-resolution (SR) technique using deep convolutional neural networks (CNN) with simulated EEG data with white Gaussian and real brain noises, and experimental EEG data obtained during an auditory evoked potential task. SR EEG simulated data with white Gaussian noise or brain noise demonstrated a lower mean squared error and higher correlations with sensor information, and detected sources even more clearly than did low resolution (LR) EEG. In addition, experimental SR data also demonstrated far smaller errors for N1 and P2 components, and yielded reasonable localized sources, while LR data did not. We verified our proposed approach’s feasibility and efficacy, and conclude that it may be possible to explore various brain dynamics even with a small number of sensors.

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Comparison of Different Rainfall Erosion Estimation Methods for the Island of Crete

Proceedings ◽

10.3390/proceedings2019030067 ◽

2020 ◽

Vol 30 (1) ◽

pp. 67 ◽

Cited By ~ 1

Author(s):

Dimitrios D. Alexakis ◽

Manolis Grillakis

Keyword(s):

Temporal Resolution ◽

Total Duration ◽

Power Estimation ◽

Monthly Rainfall ◽

Rainfall Data ◽

Estimation Methods ◽

Accurate Estimation ◽

High Temporal Resolution ◽

Rainfall Erosivity ◽

Daily Data

Interactions between soil and rainfall plays a vital role in ecological, hydrological and biogeochemical cycles of land. Among those interactions, the phenomenon of rainfall induced soil erosion is crucial to the soil functions, as it affects the soil structure and organic matter content that subsequently affects soil ability to hold moisture and nutrients. The erosive power of a specific rainfall event is regulated by its intensity and total duration. Various methodologies have been developed and tested to estimate the rainfall erosivity in different hydroclimatic regions and using different rainfall measuring timescales. Studies have shown that high temporal resolution measurements provide a more robust erosivity estimation. Nonetheless the sparsity and scarcity of such high temporal resolution data make the accurate estimation of rainfall erosivity difficult. Here, we compare different erosion power estimation methods based on different rainfall timescales for the island of Crete. Sub-daily (30-min) rainfall data based estimation is used as the basis for the assessment of a daily data based estimation methodology and two different methods that use monthly rainfall data. Modified Fournier Index (MFI) is incorporated in the study through different literature approaches and a regression equation is developed between rainfall erosivity power and MFI index for Crete. Results indicate that the use of daily data in the rainfall erosive power estimation is a good approximation of the sub-daily estimation, while formulas based on monthly rainfall data tend to exhibit larger deviations.

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A Statistic-Based Calibration Method for TIADC System

Mathematical Problems in Engineering ◽

10.1155/2015/689869 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Kuojun Yang ◽

Shulin Tian ◽

Peng Ye ◽

Peng Zhang ◽

Yuanjin Zheng

Keyword(s):

Sampling Rate ◽

Calibration Method ◽

Wide Frequency Range ◽

Accurate Estimation ◽

Analog To Digital ◽

Average Value ◽

Gain Error ◽

Sampling Points ◽

Interleaved Adc ◽

Time Interleaved

Time-interleaved technique is widely used to increase the sampling rate of analog-to-digital converter (ADC). However, the channel mismatches degrade the performance of time-interleaved ADC (TIADC). Therefore, a statistic-based calibration method for TIADC is proposed in this paper. The average value of sampling points is utilized to calculate offset error, and the summation of sampling points is used to calculate gain error. After offset and gain error are obtained, they are calibrated by offset and gain adjustment elements in ADC. Timing skew is calibrated by an iterative method. The product of sampling points of two adjacent subchannels is used as a metric for calibration. The proposed method is employed to calibrate mismatches in a four-channel 5 GS/s TIADC system. Simulation results show that the proposed method can estimate mismatches accurately in a wide frequency range. It is also proved that an accurate estimation can be obtained even if the signal noise ratio (SNR) of input signal is 20 dB. Furthermore, the results obtained from a real four-channel 5 GS/s TIADC system demonstrate the effectiveness of the proposed method. We can see that the spectra spurs due to mismatches have been effectively eliminated after calibration.

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