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ShareScore release 0.9.0
Dataset results
41 results for “EEG analysis”
Electro- Hemodynamic Activity in Children: Combined Analysis by Video -EEG Spectroscopy and Near-infrared (NIRS )
ClinicalTrials.gov study NCT02826915. IPD Sharing: Not stated. Countries: 1. Publications: 0.
EEG Analysis for the Objective Assessment of Drowsiness
ClinicalTrials.gov study NCT05453643. IPD Sharing: NO. Countries: 1. Publications: 0.
Electrohaemodynamic Activity in Epileptic Children : Combined Video-EEG and Near-infrared Spectroscopy (NIRS) Analysis
ClinicalTrials.gov study NCT02814331. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Power Spectral Analysis of EEG During Equi-MAC Inhalation Anesthesia
ClinicalTrials.gov study NCT02872389. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Real World Testing and Cost-effectiveness Analysis of Subcutaneous EEG
ClinicalTrials.gov study NCT06144047. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Evaluating a Novel Method of EEG Evoked Response Potential Analysis in Concussion Assessment, Young Adults and Adults
ClinicalTrials.gov study NCT03249675. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Using the Digital EEG Spectral Analysis in Assessing Neuroelectrical Processing Abnormalities in Autism
ClinicalTrials.gov study NCT00693953. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Analysis of the Impact of Patent Ductus Arteriosus on Brain Function in Preterm Neonates: Multimodal Approach Integrating EEG-NIRS, Ultrasound and Clinical Data
ClinicalTrials.gov study NCT02803671. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Quantitative Analysis of EEG Changes in Response to Nociception
ClinicalTrials.gov study NCT06342570. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Spectral Edge Frequency From Spectral EEG Analysis to Guide Deep Sedation in the Critical Care Setting (Pilot)
ClinicalTrials.gov study NCT04026451. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Central Motor Mechanisms in AIS Via HD-sEMG, EEG and Neuromuscular Coupling Analysis
ClinicalTrials.gov study NCT06758115. IPD Sharing: NO. Countries: 1. Publications: 0.
The Characteristic of Axial Pain and EEG Analysis of Patients After Laminoplasty
ClinicalTrials.gov study NCT04968639. IPD Sharing: NO. Countries: 1. Publications: 0.
Anesthetic Agent Titration With EEG Wave Analysis
ClinicalTrials.gov study NCT07266727. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Evaluating a Novel Method of EEG Evoked Response Potential Analysis in Sport Concussion Assessment - Test Stability and Effect of Concussion
ClinicalTrials.gov study NCT01535339. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Development of Automated Analysis to Electroencephelogram (EEG) Data in Patients Treated at the Sagol Hyperbaric Medicine and Research Center at the Years 2017-2019.
ClinicalTrials.gov study NCT04095377. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Evaluating Occupational Stress in Surgeons and Musicians: A Multi-modal Pilot Study Using Wearable EEG, Biomarker Analysis and Validated Questionnaires
ClinicalTrials.gov study NCT06875882. IPD Sharing: Not stated. Countries: 1. Publications: 0.
OASIS: RetrOspective Analysis on EEGs for Identifying Seizure Susceptibility in paediatrIcs Using biomarkerS
ClinicalTrials.gov study NCT06546410. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
A Clinical Trial With a Self-controlled, Multicenter, Pediatric EEG Intelligent Analysis System to Assist in Diagnosis
ClinicalTrials.gov study NCT06918457. IPD Sharing: NO. Countries: 0. Publications: 0.
Identified important EEG channels in visually impaired subjects using recurrent analysis
<p>This database contains calculated channel significance indicators based on the application of recurrent analysis to EEG data, following the example of the article Runnova A, Selskii A, Emelyanova E, Zhuravlev M, Popova M, Kiselev A, Shamionov R. Modification of Joint Recurrence Quantification Analysis ( JRQA) for assessing individual characteristics from short EEG time series. Chaos. 2021;31(9):093116. doi: 10.1063/5.0055550. For each EEG channel of each subject, the channel significance indicator was separately calculated for all stimulus presentations in the cognitive test. The following is a description of the EEG setup, subjects, and experimental design.</p><p>To compare the performance of the method for identifying important channels, the frequency corresponding to the maximum amplitude and the central frequency of the spectrum were calculated using Fourier spectrum analysis. The latter was calculated so that the integral value of the probability density to the left of it and to the right of it were the same. This approach makes it possible to distinguish between high and low frequencies in the spectrum and, if necessary, evaluate them separately.</p><p>The values of the channel significance indicators for each subject are located in the «SR» folder. Frequencies corresponding to maximum amplitude are in folders «MaxFrequency». Center frequencies for all channels of each subject in the folder «CentralFrequency».</p><p>Data were recorded at 250 Hz sampling rate using the conventional monopolar method of registration with two reference points and N = 21 electrodes, located according to the "10–20" scheme. The adhesive Ag/AgCl electrodes in prewired head caps were used to detect EEG signals. Two reference electrodes, A1 and A2, were located on mastoids, while the ground electrode N was placed above the forehead. The EEG signals were filtered by a band-pass filter with cutoff points at 1.5 Hz (HP) and 35 Hz (LP) and a 50 Hz notch filter.</p><p>Children of middle school age participated as study subjects. The biomedical data were processed honoring the confidentiality and anonymity of the study respondents. Experimental work was carried out in compliance with ethical standards and approved by the local committee on research ethics at Saratov State University (Saratov, RF). Prior to the study, written consent of the parents was obtained for conducting neuropsychological experimental work, the subsequent data processing and publication of the results. The parents were present during all experiments in accordance with the requirements of the Russian Federation legislation.</p><p>The following design of experimental work was employed. All experiments were conducted in the mornings, on a typical school day. One of the classrooms was darkened with blinds. Children sat in comfortable chairs, adjusted to their heights to attain a comfortable stable position, leaning back in the chair. Their feet were flat on the floor. The total experimental work time was approximately 25 minutes for each subject.</p><p>The experiment included three short stages: background state of quiet wakefulness with eyes closed (about 3 minutes), simple cognitive test for counting sound signals (about 20 minutes), and repeated background state of quiet wakefulness with eyes closed (about 2 minutes). During the cognitive test, groups of short sounds were given: one to four per group. During listening, the subject was instructed to determine whether he perceived an even or an odd number of sounds in a single stimulus group. Depending on his assessment, the subject pressed the left or right buttons of a special remote control. Between groups of sound signals there was a silent pause lasting 5 to 11 seconds. Each child was analyzing these sound stimuli for 3 minutes, followed by a rest break lasting about 2 minutes.</p><p>The experimental work involved 4 boys 10 years of age (123,2±5,1 months). All children belonged to the health group I the Order of the Ministry of the Russian Federation Healthcare of March 13, 2019, No. 124n. They had no infectious or other acute illnesses one month prior to the experiment. Additionally, all children had no identified chronic diseases.</p>
Identified important EEG channels using recurrent analysis in patients with chronic migraine
<p>This database contains calculated indicators of channel significance based on the application of recurrent analysis to EEG data using the example of the article Runnova A, Selskii A, Emelyanova E, Zhuravlev M, Popova M, Kiselev A, Shamionov R. Modification of Joint Recurrence Quantification Analysis ( JRQA) for assessing individual characteristics from short EEG time series. Chaos. 2021;31(9):093116. doi: 10.1063/5.0055550. For each EEG channel of each subject, the channel significance indicator was separately calculated for all presentations of stimuli in the cognitive test, which are located in the SR folder. The RR_Simple_Hard folder contains the results of calculating significant channels separately for simple presentations - 3 and 4 squares, and complex presentations - 7 and 8 squares. A description of the EEG setup, subjects, and experimental design is provided below.</p><p>Test subjects volunteered to participate in the experiment. The biomedical data were processed honoring the confidentiality and anonymity of the study respondents. All procedures in studies involving human participants were conducted in accordance with the principles of the Declaration of Helsinki and approved by the Research Ethics Committee at Saratov State Medical University. Written informed consent was given by all participants. The participants were diagnosed in compliance with the diagnostic criteria of the International Classification of Headache Disorders (ICHD-3, beta version). We used the migraine questionnaire to evaluate the data on migraine attacks and disease courses. The exclusion criteria were acute headache, other neurological disorders, and intake of medicines targeting the central nervous system (including medications for migraine prophylaxis) within the preceding 24 h, as well as a Beck Depression Inventory (BDI) score >12 points. The sample comprised 23 subjects.</p><p>The study group included patients with chronic and recurrent migraine. The subjects had no other serious health problems, or drug or alcohol addictions. Patients were 27–66 years of age.</p><p>During all experiments, the multichannel EEG data were recorded using the EEG recorder Encephalan-EEGR-19/26 (Medicom MTD, Taganrog, Russian Federation). The data were recorded at a sampling rate of 500 Hz using the conventional monopolar recording technique with two reference electrodes and multiple recording electrodes (n = 31). EEG signals were obtained using special headcaps with prewired Ag/AgCl adhesive electrodes. Two reference electrodes, A1 and A2, were located on the mastoid processes, and the ground electrode (N) was placed above the forehead. EEG signals were filtered using a bandpass filter with cutoff frequencies of 0.5 Hz (high pass filter) and 30 Hz (low pass filter), and a notch filter of 50 Hz. The experiments were carried out in the early afternoon hours at a specially equipped laboratory. During monitoring, the subjects were in a comfortable reclining position in a neurophysiological chair with support for the neck and the back of the head in order to avoid the occurrence of artifacts associated with muscle tension in these areas. The neuropsychological experiment allowed for distinguishing cognitive ERPs. At the beginning and end of the experiment, passive wakefulness was recorded for 10 min each time. In between, the active part of the experiment was recorded for ~40 min. During the active part, short presentations of visual stimuli were delivered in a pseudorandom sequence. For all subjects, the order of stimulus presentation, duration of stimulus presentation, and duration of pauses between stimuli were identical. Each subject observed 350 stimuli. The stimulus was presented for 0.4–0.8 s. After its presentation, during a pause until the next stimulus, the subject saw solely a gray screen. Each visual stimulus per se was an image with a different number of squares (3–8). The task of the subject was to estimate an even or odd number of squares and make a choice by pressing one of two buttons on the remote control.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.