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30 results for “brain waves”
Universal theory of brain waves: from linear loops to nonlinear synchronized spiking and collective brain rhythms (supplemental material: brain wave loops movies)
<p>This is a collection of videos supplementing the paper "Universal theory of brain waves: from linear loops to nonlinear synchronized spiking and collective brain rhythms"</p> <p>Examples of wave trajectories and emergent persistent loop patterns for the spherical shell cortex model with<br> varying amounts of tensor anisotropy and inhomogeneous shell layer thickness.</p> <p><br> Examples of brain wave trajectories and emergent persistent loop patterns for cortical fold geometry with different<br> approaches used for estimation of inhomogeneity and anisotropy. Among those examples are several simple cases with variable inhomogeneity and fixed anisotropy (similar to the above spherical shell cortex model) as well as with more complex estimates of anisotropy based on multiple diffusion gradients MRI (dMRI) acquisitions.</p>
BRAIN Journal-Artificial Neuron Modelling Based on Wave Shape-Figure 1. Typical feedforward artificial neural network processing unit
<p>In Figure 1, each input X is weighted by a separate weight value w. These are then summed together to give a total input value for the neuron. This total can then be passed through a function, to transform it into the desired output value. This is then compared to the actual output value d; where the error or differences between the two sets is measured and used to correct the weight<br> values, to bring the two sets of values closer. One way to update the weights is after each individual pattern is presented and processed. Another option is to process the error after the presentation of the whole dataset, as part of a batch update.</p>
BRAIN Journal-Artificial Neuron Modelling Based on Wave Shape-Figure 2. New neuronal model, based on matching a wave-like representation of the output.
<p>For the new model, shown in Figure 2, the ‘differences’ between the outputs is measured and combined, to produce a kind of wave-like shape. If output point 1 has a value of 10, and output point 2 has a value of 5, for example, then this results in the shape moving down 5 points on the wave shape. This shape will, of course, change depending on the order that the dataset values are presented in. Also, if new data is presented, then that will produce a different shape. However, there is still an association between the input values and the output values and it is still this relation that is being learnt. The neural network needs to be able to learn a function that can generalise over the presented datasets, so that it can recognise the relation in previously unseen data as well. Generalising over trying to learn a wave-like shape, or trying to match the output values exactly looks quite similar, suggesting that the process is at least valid. In Figure 2, note that a transposition to move the learned shape up or down first is possible, before a weight value would try to scale it. If the initial match of the combined inputs can be as close as possible, then these adjustments will become less and will be more for fine tuning.</p>
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 9. Illustration of three different wavelets with three waves that have high amplitude values, as indicated by the dashed red circles
<p>The mother wavelet of Coiflet 5 contained triple-high oscillation amplitude (i.e., Figure 9a). We considered that this mother wavelet was inappropriate for our data because overall our data possibly contained only a few matches with the mother wavelet of Coiflet 5. Moreover, the Symlet 10 (i.e., Figure 9b) and 20 (i.e., Figure 9c) also provided supportive results that were lower than others in ANNSVM_WLHT because their mother wavelets also had a similar shape as that of Coiflet 5. For similar reasons, the Haar wavelet was not proper because it is a step function. </p>
Brain waves would include equipotential fluctuations in addition to oscillations as sine waves
<p>Raw 1200 LFP data sets of 60s are in /60s_125(250,500,1000)Hz_20h/12XX/eeg1_t0(1-1199) .txt. Results of tau and burst, whose state is identified by existing method (0:awake, 1:NREM, 2:REM), are in /60s_125-1000Hz_20h/12XX_m2 or S1/tau-burst_m2(s1)_thX.XXX.txt. th=threshold. Column 1:trial No., 2:state, 3:Ntau, 4:Mtau, 5:ratio of total tau, 6:number of burst, 7:mean burst, 8:Abst. Those identified by Ntau method are in /60s_125-1000Hz_20h/4state (mean per state), 4state_m2, or 4states _s1/12XX/tauburst-m2_4state_thx.XXX.txt.The state is listed in third column as 2:awake, 10:REM, 20:NREM, 30:light sleep. Data of SEF95 are in /60s_sef_4state/12XX_SEF4state_125(250,500,1000) Hz.txt.</p> <p>Raw EEG data sets of 64s are in /dogEEG/older(younger)_sev/20150XXX/50Hz_nonMovAve/20150XXX_sevX.X_int0(1,2)_cts2.txt. Results of tau and burst are contained in /dogEEG/older(younger)_sev/20150XXX/sratio_peak/non-movave/20150XXX_sratio_peak_tau-burst_thXX.XX.txt. Column 1:sevoflurane concentration, 2:int, 3:Ntau, 4:Mtau, 5:ratio of total tau, 6:number of burst, 7:mean burst, 8:Abst. Results of SEF95 are in /dogEEG/older(younger)_sev/20150XXX/50Hz_nonMovAve/20150XXX_sef95.txt. Data of maximum Ntau, minimum Abst, or Mtau=2.3, 2.5 sample interval are in /dogEEG/THnt_250Hz.</p>
Dataset to reproduce the figures of article "Information dynamics of in silico EEG Brain Waves"
<p>Data and code to reproduce results figures.</p>
Neonatal Brain Waves After Electrocoagulation
ClinicalTrials.gov study NCT00396773. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Accuracy of EEG Slow Wave Activity in Predicting Favourable Outcome in Patients With Hypoxic Brain Injury - A Substudy of STEPCARE Trial
ClinicalTrials.gov study NCT06564675. IPD Sharing: UNDECIDED. Countries: 2. Publications: 1.
EEG Slow Wave Activity in Hypoxic Brain Injury
ClinicalTrials.gov study NCT04506788. IPD Sharing: NO. Countries: 1. Publications: 2.
Use of Brain Wave Monitoring During Surgery to Reduce Postoperative Cognitive Dysfunction
ClinicalTrials.gov study NCT04189861. IPD Sharing: NO. Countries: 1. Publications: 7.
Second wave, late-phase neuroinflammation in the brain of aged 5xFAD transgenic Alzheimer's disease model mice iden-tified using macrolaser light sheet microscopy imaging with tissue clearing
Open the record for dataset details and reuse information.
Oculocardiac Reflex Brain Wave Monitor
ClinicalTrials.gov study NCT03663413. IPD Sharing: UNDECIDED. Countries: 0. Publications: 2.
Novel Protection Against Potential Brain, Hearing and Vision Injury During Blast Wave Exposure
ClinicalTrials.gov study NCT03017924. IPD Sharing: NO. Countries: 1. Publications: 0.
Periodic Heat Waves-induced neuronal etiology in the elderly is mediated by Gut-Liver-Brain axis: A Transcriptome Profiling Approach
GEO Series GSE252887. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
Stimulating Brain Waves During Deep Sleep
ClinicalTrials.gov study NCT05024578. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Efficacy of 128-channel EEG Combined With BESA Dipole Localization and Intervention on Brain Waves for Epilepsy
ClinicalTrials.gov study NCT02613234. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Acute Effects of Progressive Muscle Relaxation on Brain Waves
ClinicalTrials.gov study NCT07089537. IPD Sharing: NO. Countries: 1. Publications: 0.
Sugammadex and Brain Waves
ClinicalTrials.gov study NCT01142648. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Effect of Heartfulness Meditation on Brain Waves and How Calm One Feels During Meditation
ClinicalTrials.gov study NCT04721379. IPD Sharing: NO. Countries: 1. Publications: 0.
Automated Measures of Speech Intelligibility Using Brain Wave Recordings
ClinicalTrials.gov study NCT06402994. 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.