Query: econnectome    EEG

title: eConnectome: A MATLAB toolbox for mapping and imaging of brain functional connectivity
abstract: We have developed a MATLAB based toolbox, eConnectome (electrophysiological connectome) for mapping and imaging functional connectivity at both the scalp and cortical levels from the electroencephalogram (EEG) as well as from the electrocorticogram (ECoG) Graphical user interfaces were designed for interactive and intuitive use of the toolbox. Major functions of eConnectome include EEG/ECoG preprocessing, scalp spatial mapping, cortical source estimation, connectivity analysis, and visualization. Granger causality measures such as directed transfer function and adaptive directed transfer function were implemented to estimate the directional interactions of brain functional networks, over the scalp and cortical sensor spaces. Cortical current density inverse imaging was implemented using a generic realistic geometry brain head model from scalp EEGs. Granger causality could be further estimated over the cortical source domain from the inversely reconstructed cortical source signals as derived from the scalp EEG. Users may implement other connectivity estimators in the framework of eConnectome for various applications. The toolbox package is open source and freely available at http:/econnectome.umn.edu under the GNU general public license for noncommercial and academic uses.

title: MEG based brain functional connectivity analysis using eConnectome
abstract: eConnectome (electrophysiological Connectome) is an open source MATLAB software platform with graphical user interfaces for mapping and imaging brain functional connectivity from electrophysiological signals including EEG, ECoG and MEG. We introduce the software platform and report the newly included functionality of MEG connectivity analysis. Simulated and real MEG data were analyzed using the eConnectome. The results indicate the validity of the eConnectome for brain activity and connectivity mapping from MEG data.

title: Research of EEG from Patients with Temporal Lobe Epilepsy on Causal Analysis of Directional Transfer Functions
abstract: 16 channel EEG data during intermittent episodes of epilepsy is recoded and analyzed to find lesions source and relationship between brain areas for temporal lobe epilepsy (TLE) patients by causal analysis method.  There are 8 patients with temporal lobe epilepsy, 5 males and 3 females, aged between 19 to 47 years, the average age of 30.63 years. 16 channel EEG in 8 patients was recorded by Stellate Video EEG. Sample time 20s (sample points 4000) Sampling frequency fs 2 0 0 Hz. Directional transfer functions is used to direct the information transduction between each channel of the EEG signals, which can reflect the causal relationship between each channel and determine the location of the lesions source. (In this paper, we used eConnectome software that developed by Biomedical Functional Imaging and Neuroengineering Laboratory at the University of Minnesota, directed by Dr. Bin He)  Causality results of 8 patients during intermittent episodes of EEG 20s are as follows: 6 patients' lesions source are located on channels T5 and F7 in left tempora, One of 5 cases' are located on channel T5 in the left posterior temporal, One of 1 case is located on channel F7 in the left anterior temporal. And 2 patients' lesions source are located on channels T4 and T6 in the right tempora, in the 2 patients, 1 case's lesions source is located on channel T4 in right middle temporal, 1 case's lesions source is located on channel T6 in right posterior temporal. Causality results consistent with the clinical diagnosis.  Research of EEG on causal analysis of directional transfer function can effectively determine the lesions source of seizure, and effectively calculate the transmission direction of the multi channel information, which is to provide support in the clinic for determine the source of seizure.

title: FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data
abstract: This paper describes FieldTrip, an open source software package that we developed for the analysis of MEG, EEG, and other electrophysiological data. The software is implemented as a MATLAB toolbox and includes a complete set of consistent and user friendly high level functions that allow experimental neuroscientists to analyze experimental data. It includes algorithms for simple and advanced analysis, such as time frequency analysis using multitapers, source reconstruction using dipoles, distributed sources and beamformers, connectivity analysis, and nonparametric statistical permutation tests at the channel and source level. The implementation as toolbox allows the user to perform elaborate and structured analyses of large data sets using the MATLAB command line and batch scripting. Furthermore, users and developers can easily extend the functionality and implement new algorithms. The modular design facilitates the reuse in other software packages.

title: Quantitative EEG Analysis in Angelman Syndrome: Candidate Method for Assessing Therapeutics.
abstract: The goal of these studies was to use quantitative (q)EEG techniques on data from children with Angelman syndrome (AS) using spectral power analysis, and to evaluate this as a potential biomarker and quantitative method to evaluate therapeutics. Although characteristic patterns are evident in visual inspection, using qEEG techniques has the potential to provide quantitative evidence of treatment efficacy. We first assessed spectral power from baseline EEG recordings collected from children with AS compared to age matched neurotypical controls, which corroborated the previously reported finding of increased total power driven by elevated delta power in children with AS. We then retrospectively analyzed data collected during a clinical trial evaluating the safety and tolerability of minocycline (3 mg/kg/d) to compare pretreatment recordings from children with AS (4 12 years of age) to EEG activity at the end of treatment and following washout for EEG spectral power and epileptiform events. At baseline and during minocycline treatment, the AS subjects demonstrated increased delta power; however, following washout from minocycline treatment the AS subjects had significantly reduced EEG spectral power and epileptiform activity. Our findings support the use of qEEG analysis in evaluating AS and suggest that this technique may be useful to evaluate therapeutic efficacy in AS. Normalizing EEG power in AS therefore may become an important metric in screening therapeutics to gauge overall efficacy. As therapeutics transition from preclinical to clinical studies, it is vital to establish outcome measures that can quantitatively evaluate putative treatments for AS and neurological disorders with distinctive EEG patterns.

title: Quantitative EEG Findings in Patients With Psychogenic Nonepileptic Seizures
abstract: Psychogenic nonepileptic seizures (PNES) is one of the clinical manifestations of conversion disorder that epileptiform discharges do not accompany. Factors capable of increasing susceptibility to these seizures have not been adequately investigated yet. This study aims to investigate the quantitative electroencephalography (QEEG) findings for PNES by evaluating the resting EEG spectral power changes during the periods between seizures.  Thirty nine patients (29 females, 10 males) diagnosed with PNES (group 1) and 47 patients (23 females, 24 males) without any psychiatric diagnosis (group 2) were included in the study. The patients underwent a psychiatric examination at their first visit, were diagnosed and their EEGs were recorded. Using fast Fourier transformation (FFT) spectral power analysis was calculated for delta (0.5 4 Hz) theta (4 8 Hz) alpha (8 13 Hz) beta (15 30 Hz) high beta (25 30 Hz) gamma 1 (31 40 Hz) gamma 2 (41 50 Hz) and gamma (30 80 Hz) frequency bands.  Six separate EEG band power, namely (C3 high beta, C3 gamma, C3 gamma 1, C3 gamma 2, P3 gamma, P3 gamma 1) were found to be higher in the patients diagnosed with PNES than in the control group.  Our findings show that PNES correlate with high frequency oscillations on central motor and somatosensory cortices.

title: Development of Frontal EEG Differences Between Eyes Closed and Eyes Open Resting Conditions in Children: Data From a Single Channel Dry Sensor Portable Device
abstract: Changes in EEG when moving from an eyes closed to an eyes open resting condition result from bottom up sensory processing and have been referred to as activation. In children, activation is characterized by a global reduction in alpha, frontally present reductions for delta and theta, and a frontal increase for beta. The present study aimed to replicate frontal EEG activation effects using single channel, dry sensor EEG, and to extend current understanding by examining developmental change in children. Frontal EEG was recorded using a single channel, dry sensor EEG device while 182 children aged 7 to 12 years completed eyes closed resting (EC) eyes open resting (EO) and focus (FO) tasks. Results indicated that frontal delta, theta, and alpha power were reduced, and frontal beta power was increased, in the EO compared with the EC condition. Exploratory analysis of a form of top down activation showed that frontal beta power was increased in the FO compared with to the EO condition, with no differences for other bands. The activation effects were robust at the individual level. The bottom up activation effects reduced with age for frontal delta and theta, increased for frontal alpha, with no developmental change for top down or bottom up frontal beta activation. These findings contribute further to validation of the single channel, dry sensor, frontal EEG and provide support for use in a range of medical, therapeutic, and clinical domains.

title: Prediction of Depression Severity Scores Based on Functional Connectivity and Complexity of the EEG Signal
abstract: Depression is one of the most common mental disorders and the leading cause of functional disabilities. This study aims to specify whether functional connectivity and complexity of brain activity can predict the severity of depression (Beck Depression Inventory II scores) Methods Resting state, eyes closed EEG data were recorded from 60 depressed patients. A phase synchronization measure was used to estimate functional connectivity between all pairs of the EEG channels in the delta (1 4 Hz) theta (4 8 Hz) alpha (8 13 Hz) and beta (13 30 Hz) frequency bands. To quantify the local value of functional connectivity, 2 graph theory metrics, degree, and clustering coefficient (CC) were measured. Moreover, Lempel Ziv complexity (LZC) and fuzzy entropy (FuzzyEn) were used to measure the complexity of the EEG signal.  Through correlation analysis, a significant negative relationship was found between graph metrics and depression severity in the alpha band. This association was strongly positive for the complexity measures in alpha and delta bands. Also, the linear regression model represented a substantial performance of depression severity prediction based on EEG features of the alpha band (r 0.839; P .0001, root mean square error score of 7.69)  We found that the brain activity of patients with depression was related to depression severity. Abnormal brain activity reflects an increase in the severity of depression. The presented regression model provides a quantitative depression severity prediction, which can inform the development of EEG state and exhibit potential desirable application for the medical treatment of the depressive disorder.

title: Assessing the Retest Reliability of Prefrontal EEG Markers of Brain Rhythm Slowing in the Eyes Closed Resting State
abstract: We examined whether prefrontal lobe EEG markers of slower brain rhythms, which are correlated with functional brain aging, can reliably reflect those of other brain lobes, as measured by a multichannel device.  EEG measurements were taken of 112 healthy individuals aged 20 to 69 years in the eyes closed resting state. A 5 minute measurement was taken at 8 regions (Fp1, Fp2, F3, F4, T3, T4, O1, O2) Indices (median frequency [MDF] peak frequency [PF] that quantitatively reflect the characteristics of EEG slowing, and traditional commonly used spectral indices (absolute powers as delta, theta, alpha, beta, and relative power as alpha to theta ratio [ATR] were extracted from the EEG signals. For these indices, the differences between the prefrontal lobe and other areas were analyzed and the test retest reproducibility was investigated.  The EEG slowing indicators showed high conformity over all brain lobes and stable reproducibility. On the other hand, the typical EEG spectral indicators delta, theta, alpha, beta, and ATR differed between brain regions.  It was found that EEG slowing markers, which were used for assessing the aging or degeneration of brain functions, could be reliably extracted from a prefrontal EEG alone.  These findings suggest that EEG prefrontal markers may reflect markers of other brain regions when a multi channel device is used. Thus, this method may constitute a low cost, wearable, wireless, easily accessible, and noninvasive tool for neurological assessment that could be used in the early detection of cognitive decline and in the prevention of dementia.

title: Entropy: A Promising EEG Biomarker Dichotomizing Subjects With Opioid Use Disorder and Healthy Controls
abstract: Electroencephalography (EEG) signals are known to be nonstationary and often multicomponential signals containing information about the condition of the brain. Since the EEG signal has complex, nonlinear, nonstationary, and highly random behaviour, numerous linear feature extraction methods related to the short time windowing technique do not satisfy higher classification accuracy. Since biosignals are highly subjective, the symptoms may appear at random in the time scale and very small variations in EEG signals may depict a definite type of brain abnormality it is valuable and vital to extract and analyze the EEG signal parameters using computers. The challenge is to design and develop signal processing algorithms that extract this subtle information and use it for diagnosis, monitoring, and treatment of subjects suffering from psychiatric disorders. For this purpose, finite impulse response based filtering process was employed rather than traditional time and frequency domain methods. Finite impulse response subbands were analyzed further to obtain feature vectors of different entropy markers and these features were fed into a classifier namely multilayer perceptron. The performances of the classifiers were finally compared considering overall classification accuracies, area under receiver operating characteristic curve scores. Our results underline the potential benefit of the introduced methodology is promising and is to be treated as a clinical interface in dichotomizing substance use disorders subjects and for other medical data analysis studies. The results also indicate that entropy estimators can distinguish normal and opioid use disorder subjects. EEG data and theta frequency band have distinctive capability for almost all types of entropies while nonextensive Tsallis entropy outperforms compared with other types of entropies.