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41 results for “EEG analysis”
Training Datasets for Epilepsy Analysis: Preprocessing and Feature Extraction from EEG Time Series
<h2>The files include the 20 training datasets, in csv format, from 20 epileptic patients. Each set of data is described by 1080 features extracted using the sliding window technique.</h2>
SleepEEGpy: a Python-based software integration package to organize preprocessing, analysis, and visualization of sleep EEG data
<p>This dataset includes three high-density sleep EEG recordings of healthy participants, downsampled to 250 Hz and stored in FIF format:</p> <ol> <li>Nap recording of a young adult participant</li> <li>Overnight recording of a young adult participant</li> <li>Overnight recording of an older adult participant</li> </ol> <p>Additionally, the dataset includes three text files for each recording:</p> <ul> <li>bad_channels.txt: Indexes of noisy channels</li> <li>annotations.txt: Onset and duration of noisy temporal intervals</li> <li>staging.txt: Sleep staging vector</li> </ul> <p>The corresponding package can be found on <a href="https://github.com/NirLab-TAU/sleepeegpy">GitHub.</a></p> <p>For citation, please use:<br>Falach, R., G. Belonosov, J. F. Schmidig, M. Aderka, V. Zhelezniakov, R. Shani-Hershkovich, E. Bar, and Y. Nir. "SleepEEGpy: a Python-based software integration package to organize preprocessing, analysis, and visualization of sleep EEG data." Computers in Biology and Medicine 192 (2025): 110232.<br><a href="https://doi.org/10.1016/j.compbiomed.2025.110232" rel="nofollow">https://doi.org/10.1016/j.compbiomed.2025.110232</a></p>
EEG functional connectivity analysis for the study of the brain maturation in the first year of life
<p>This dataset is related to 146 typically developing infants who underwent baseline electrophysiological (EEG) data recording at 6 (T6) and 12 (T12) months of age. The recordings were made using a dense-array EGI system (Geodesic EEG System (GES) 300 or 400, Electric Geodesic, In., Eugene, Oregon, USA) equipped with 60/64-electrode or 128-electrode caps (HydroCel Geodesic Sensor net).<br>Whole-brain functional connectivity (FC) metrics were extracted, for multiple frequency bands with the aim to evaluate brain maturation in the first year of life in terms of EEG functional connectivity. In addition, Bayley test was administered at 24 months of age to explore possible relation between brain network connectivity and cognitive functions.</p> <p>The dataset includes subjects’ sociodemographic and individual factors (such as age, sex, socioeconomic status, gestational week and birth weight); functional connectivity metrics (such as the magnitude-squared coherence index, phase lag index (PLI), and parameters characterizing the minimum spanning tree built from the PLI index) computed in the delta (2-4 Hz), theta (4-6 Hz), low-alpha (6-9-Hz), high-alpha (9-13 Hz), beta (13-30 Hz) and gamma (30-45 Hz) frequency bands; and the Bayley test raw scores, assessed at 24 months of age. </p> <p>In particular, each row in the database corresponds to a subject and each column to a different variable.<br>Column A, Subject code;<br>Column B, Time point: 1 = data related to the EEG recording performed at six months of age; 2 = data related to the EEG recording performed at twelve months of age;<br>Column C, Sex: 0= males; 1=females;<br>Column D, Age (expressed in days) at T6;<br>Column E, Age (expressed in days) at T12;<br>Column F, Family socio-economic status;<br>Column G, Gestational age expressed in weeks;<br>Column H, Birth weight expressed in grams;<br>Column I and J, Bayley Cognitive Composite Score and Griffiths developmental quotient both assessed at 6 months of age;<br>Column K and L, Number of electrodes of the used electrode-caps for T6 and T12, respectively;<br>Columns from M to BM, FC metrics for all frequency bands;<br>Columns from BO to BQ, raw cognitive, receptive and expressive Bayley test scores assessed at 24 months of age;<br>Column BR, Composite language metric derived from the expressive and receptive Bayley scores.</p> <p> </p> <p>If you use this dataset please cite the following manucript: Falivene, A.; Cantiani, C.; Dondena, C.; Riboldi, E.M.; Riva, V.; Piazza, C. EEG Functional Connectivity Analysis for the Study of the Brain Maturation in the First Year of Life. Sensors 2024, 24, 4979. All details about subjects, data acquisition and signal processing pipeline are described in the manuscript.</p>
BRAIN Journal-Brain signal analysis using EEG and Entropy to study the effect of physical and mental tasks on cognitive performance-Figure 2. Energy-VAS subjective measures for the participants (n=12) under two conditions (control and exercise involved cognitive task).
<p>As shown in Figure 2, it was found that there was a statistically significant interaction in the<br> percentage of mental fatigue between the condition type and time-on-task factor times (F(6, 22) =<br> 492.19, p < 0.001) as well as there was a significant main effect of time-on-task (time5 to time30)<br> (F(5, 22) = 463.794, p < 0.001). In addition, there was also a significant main effect in the condition type (F (1, 22) = 713.133, p < 0.001) which represented a large effect size. For the physical fatigue<br> subjective measure, there was a significant difference between the two experimental conditions (p <<br> 0.001), and within the subject test times (p < 0.001). However, there was no significant difference<br> between the means of the concentration visual analogue scale for these two experimental conditions<br> (p = 0.057) despite a significant difference (p < 0.001) in the time-on-task repeated measures.</p>
BRAIN Journal-Brain signal analysis using EEG and Entropy to study the effect of physical and mental tasks on cognitive performance-Figure 1. The 10-20 international system electrode placement showing the EEG electrode placement
<p>For the EEG analysis, the average power in the theta band (4 – 8 Hz), and alpha band (8 – 12<br> Hz) were computed at the frontal and parietal electrodes Fz and Pz respectively. Next, the ratio of<br> these two powers was determined, and named the ‘cognitive ratio’ as several researchers found that<br> the fronto-parietal network play important roles in cognitive activities.</p>
Time-generalized multivariate analysis of EEG responses reveals a cascading architecture of semantic mismatch processing
<p>This entry includes the required data to conduct the analysis from our paper. It includes recordings, a look-up csv for target word onsets and montage file. Github page for analysis code: https://github.com/heikele/GAT_n4-p6</p> <p> </p> <p>Abstract from submitted paper:</p> <p>Event-related brain potentials have a strong impact on neurocognitive models, as they inform about the temporal sequence of cognitive processes. Nevertheless, their value for deciding among alternative cognitive architectures is partly limited by component overlap and the possibility of ambiguity regarding component identity. Here, we apply temporally-generalized multivariate pattern analysis – a recently-proposed machine learning method capable of tracking the evolution of neurocognitive processes over time – to constrain possible alternative architectures underlying the processing of semantic incongruency in sentences. In a spoken sentence paradigm, we replicate established N400/P600 correlates of semantic mismatch. Time-generalized decoding indicatesthat early vs. late mismatch-sensitive processes are (i) distinct in their neural substrate, arguing against recurrent or latency-shifted single process architectures, and (ii) partially overlapping in time, inconsistent withpredictions of strictly serial models. These results are in accordance withan incremental-cascading neurocognitive organization of semantic mismatch processing. We propose time-generalized multivariate decoding as a valuable tool for neurocognitive language studies.</p> <p> </p> <p>Keywords: EEG; ERP; semantic mismatch; N400; P600; multivariate pattern analysis; generalization across time decoding</p>
Simulated EEG and EMG data for Reference Phase Analysis evaluation.
<p><span>This dataset comprises simulated EEG (Electroencephalography) signals recorded from 29 channels along with an EMG (Electromyography) signal. The signals have been artificially generated using real EEG and EMG signals as the basis.</span></p> <p><span>Five distinct source models were generated, each comprising a different number</span><span> of <span>sources. We created models with 2, 3, 4, 5, and 6 sources</span>. Furthermore, we add additive noise to the signals encompassing</span> <span>SNRs ranging from 90 to 0 dB and a phase jitter ranging from 0.1 to 1.5 radians.</span></p> <p><span>We generated EEG and EMG signals using MATLAB and employed a forward model, created with FieldTrip, to convert signals from the source space to the electrode space. EEG measures were computed utilizing a linear mixture model.</span></p> <p><span>The simulated dataset aims to mimic the characteristics of real EEG and EMG signals, including their temporal dynamics, noise, and frequency spectra. It serves as a valuable resource for developing and testing signal processing algorithms, and brain-computer interfaces in the fields of neuroscience and biomedical engineering.</span></p>
Effect of Pantoprazole 40mg Daily vs Placebo on Power Spectral Analysis of the Sleep EEG of Patients With GERD.
ClinicalTrials.gov study NCT00674245. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Linear and Non-linear Analysis of EEG Changes in Schizophrenia After Atypical Antipsychotics Medication
ClinicalTrials.gov study NCT00698295. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Analysis of Correlation of Amplitude-Integrated EEG and Neurodevelopmental Outcome in Preterm Infants
ClinicalTrials.gov study NCT00728234. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Analysis of the Analgesic Mechanism of TENS-WAA During Non-anesthetized Colonoscopy Using EEG-fNIRS System
ClinicalTrials.gov study NCT06813703. IPD Sharing: YES. Countries: 1. Publications: 1.
EEG Analysis During Light Propofol Sedation
ClinicalTrials.gov study NCT03362775. IPD Sharing: NO. Countries: 1. Publications: 4.
EEG Recordings and Analysis in Parkinson's Patients: Towards Adaptive Deep Brain Stimulation by Machine Learning
ClinicalTrials.gov study NCT05284526. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Frequency Analysis of Raw EEG During Sevoflurane Anesthesia in Children Younger Than 2 Years Old
ClinicalTrials.gov study NCT05148065. IPD Sharing: NO. Countries: 1. Publications: 12.
Analysis of Deep Brain Nuclei LFP and Cortical EEG Signals During the Recovery From General Anesthesia
ClinicalTrials.gov study NCT06400901. IPD Sharing: NO. Countries: 1. Publications: 2.
Tutorial data for basic EEG ERP analysis
<p>This dataset represents a curated subset of https://freidok.uni-freiburg.de/data/154576 (DOI: 10.6094/UNIFR/154576) to be used as example data in a tutorial notebook.</p> <p>For in-depth details please refer to the original dataset.</p>
Progressive encephalopathy in m.3243A > G/MT-TL1 mutation carriers: a quantitative EEG analysis
Open the record for dataset details and reuse information.
EEG and EMG Analysis of Ideomotor Apraxia
ClinicalTrials.gov study NCT00024999. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Hand Lateralization by Using EEG Analysis Method in Amputees
ClinicalTrials.gov study NCT05339776. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluating a Novel Method of EEG Evoked Response Potential Analysis in Concussion Assessment
ClinicalTrials.gov study NCT03265912. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.