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929 results for “eeg”
Dataset In-vivo estimation of axonal morphology from MRI and EEG data
<p>This dataset includes the data underlying the conclusions made in the scientific article:<br> "In-vivo estimation of axonal morphology from MRI and EEG data"<br> Rita Oliveira, Andria Pelentritou, Giulia Di Domenicantonio, Marzia De Lucia, Antoine Lutti</p> <p><a href="https://www.frontiersin.org/articles/10.3389/fnins.2022.874023/full">https://www.frontiersin.org/articles/10.3389/fnins.2022.874023</a></p> <p>The main objective is to use data collected in-vivo in humans to estimate microscopic morphologic features of the white matter tracts.</p> <p>The in-vivo data estimated along a white matter tract of interest includes:<br> • the MRI g-ratio sampled along the visual transcallosal white matter tract<br> • a measure of conduction velocity estimated from an EEG measure of interhemispheric transfer time (IHTT)</p> <p>The microscopic morphologic features of white matter we estimate are:<br> • the axonal radius distribution, P(r)<br> • the g-ratio dependence on the radius, g(r)</p> <p>-------------------------------------------------------------------------<br> CONTENT:</p> <p>This package includes data for all the 14 subjects used in the corresponding scientific article:<br> • G-ratio values sampled along the transcallosal visual tract <br> double vector (# MRI_gratio samples x 1): G_ratio_samples.mat<br> • Length of the transcallosal visual tract <br> double: Tract_length.mat<br> • Current source densities (pA.m) of each trial, brain vertice and time <br> point for the left brain visual cortex <br> double 3 matrix (#trials x #vertices x #timepoints): Source_reconstruction_left_brain_V1V2.mat <br> • Current source densities (pA.m) of each trial, brain vertice and time <br> point for the right brain visual cortex <br> double 3 matrix (#trials x #vertices x #timepoints): Source_reconstruction_right_brain_V1V2.mat <br> • Vector of the time sample of the EEG epochs<br> double vector (1 x #time points): time_vec.mat</p> <p>The codes used in the analysis of this data are available on our online repository: <a href="https://github.com/LREN-physics/AxonalMorphology">https://github.com/LREN-physics/AxonalMorphology</a>.</p> <p>-------------------------------------------------------------------------<br> AUTHORS:</p> <p>Author: Rita Oliveira<br> PIs: Marzia De Lucia, Antoine Lutti</p> <p>Laboratory for Neuroimaging Research</p> <p>Lausanne University Hospital & University of Lausanne, Lausanne, Switzerland</p> <p>Copyright (C) 2022 Laboratory for Neuroimaging Research</p>
Post-hoc labeling of arbitrary EEG recordings for data-efficient evaluation of neural decoding methods
<p>EEG signals recorded from seven healthy subjects. On average, Seventy-three minutes of EEG data were recorded from 31 electrodes placed according to the extended 10-20 system. Signals are used in the paradigm-agnostic post-hoc labeled dataset generation framework for benchmarking of oscillatory neural decoding methods.</p>
Emotion-Antecedent Appraisal Checks: EEG and EMG datasets for Novelty and Pleasantness
<p>The Electroencaphalography (EEG) and facial Electromyography (EMG) signals included in this data set were collected in the context of a previous study conducted by van Peer, Grandjean and Scherer (2014). That study addressed three fundamental questions regarding the mechanisms underlying the appraisal process: Whether appraisal criteria are processed (a) in a fixed sequence, (b) independent of each other, and (c) by different neural structures or circuits. In that study, an oddball paradigm with affective pictures was used to experimentally manipulate novelty and intrinsic pleasantness appraisals. EEG was recorded during task performance, together with facial EMG, to measure, respectively, cognitive processing and efferent responses stemming from the appraisal manipulations. The data set made here publicly available contains the exact same data used by Coutinho, Gentsch, van Peer, Scherer and Schuller (to appear). The only difference in relation to the original data is that the some of the pre-processing steps (i.e., the processing of the raw data) were changed in order to improve the detection of artifacts. The full details of the original study, data collected, pre-processing steps and final data set are included in the paper distributed with the data (study1_dataset.pdf).</p> <p><strong>References</strong></p> <p>Coutinho E, Gentsch k, van Peer JM, Scherer KR & Schuller BW (to appear). Evidence of Emotion-Antecedent Appraisal Checks in Electroencephalography and Facial Electromyography. <em>PloS One</em>.</p> <p>van Peer JM, Grandjean D, Scherer KR (2014). Sequential unfolding of appraisals: EEG evidence for the interaction of novelty and pleasantness. <em>Emotion, </em>14(1), 51-63.</p>
Emotion-Antecedent Appraisal Checks: EEG and EMG datasets for Goal Conduciveness, Control and Power
<p>The Electroencaphalography (EEG) and facial Electromyography (EMG) signals included in this dataset was collected in the context of a previous study (Gentsch, Grandjean & Scherer, 2013). This dataset contains the exact data used in Coutinho, Gentsch, van Peer, Scherer & Schuller (to appear). The only difference in relation to the original data is that the some of the pre-processing steps (i.e., the processing of the the raw data) were changed. The full details of the data collected and pre-processing are included in a file distributed with the data (dataset-details.pdf).</p> <p>References</p> <p>Coutinho, Gentsch, van Peer, Scherer & Schuller (to appear). Evidence of Emotion-Antecedent Appraisal Checks in Electroencephalography and Facial Electromyography. PloS One.</p> <p>Gentsch K, Grandjean D, Scherer KR. Temporal dynamics of event-related potentials related to goal conduciveness and power appraisals. Psychophysiology. 2013;50(10):1010–1022. </p>
EEG and audio dataset for auditory attention decoding
<p>This dataset contains EEG recordings from 18 subjects listening to one of two competing speech audio streams. Continuous speech in trials of ~50 sec. was presented to normal hearing listeners in simulated rooms with different degrees of reverberation. Subjects were asked to attend one of two spatially separated speakers (one male, one female) and ignore the other. Repeated trials with presentation of a single talker were also recorded. The data were recorded in a double-walled soundproof booth at the Technical University of Denmark (DTU) using a 64-channel Biosemi system and digitized at a sampling rate of 512 Hz. Full details can be found in:</p> <ul> <li><strong>Søren A. Fuglsang, Torsten Dau & Jens Hjortkjær (2017): Noise-robust cortical tracking of attended speech in real-life environments. <em>NeuroImage</em>, 156, 435-444</strong></li> </ul> <p>and</p> <ul> <li><strong>Daniel D.E. Wong, Søren A. Fuglsang, Jens Hjortkjær, Enea Ceolini, Malcolm Slaney & Alain de Cheveigné: A Comparison of Temporal Response Function Estimation Methods for Auditory Attention Decoding. Frontiers in Neuroscience, </strong><a href="https://doi.org/10.3389/fnins.2018.00531">https://doi.org/10.3389/fnins.2018.00531</a></li> </ul> <p>The data is organized in format of the publicly available <a href="https://zenodo.org/record/1198430">COCOHA Matlab Toolbox</a>. The preproc_script.m demonstrates how to import and align the EEG and audio data. The script also demonstrates some EEG preprocessing steps as used the Wong et al. paper above. The AUDIO.zip contains wav-files with the speech audio used in the experiment. The EEG.zip contains MAT-files with the EEG/EOG data for each subject. The EEG/EOG data are found in <strong>data.eeg</strong> with the following channels:</p> <ul> <li>channels 1-64: scalp EEG electrodes</li> <li>channel 65: right mastoid electrode</li> <li>channel 66: left mastoid electrode</li> <li>channel 67: vertical EOG below right eye</li> <li>channel 68: horizontal EOG right eye</li> <li>channel 69: vertical EOG above right eye</li> <li>channel 70: vertical EOG below left eye</li> <li>channel 71: horizontal EOG left eye</li> <li>channel 72: vertical EOG above left eye</li> </ul> <p>The <strong>expinfo</strong> table contains information about experimental conditions, including what what speaker the listener was attending to in different trials. The expinfo table contains the following information:</p> <ul> <li>attend_mf: attended speaker (1=male, 2=female)</li> <li>attend_lr: spatial position of the attended speaker (1=left, 2=right)</li> <li>acoustic_condition: type of acoustic room (1= anechoic, 2= mild reverberation, 3= high reverberation, see Fuglsang et al. for details)</li> <li>n_speakers: number of speakers presented (1 or 2)</li> <li>wavfile_male: name of presented audio wav-file for the male speaker</li> <li>wavfile_female: name of presented audio wav-file for the female speaker (if any)</li> <li>trigger: trigger event value for each trial also found in data.event.eeg.value</li> </ul> <p>DATA_preproc.zip contains the preprocessed EEG and audio data as output from preproc_script.m.</p> <p>The dataset was created within the <a href="https://cocoha.org/">COCOHA</a><a href="https://cocoha.org/"> Project</a>: Cognitive Control of a Hearing Aid</p>
BIDS formatted EEG meditation experiment data
<p>This meditation experiment contains 24 subjects. Subjects were meditating and were interrupted about every 2 minutes to indicate their level of concentration and mind wandering. The scientific article (see Reference file) contains all methodological details.</p> <p>- Arnaud Delorme (October 17, 2018)</p>
An EEG dataset for cross-session mental workload estimation: Passive BCI competition of the Neuroergonomics Conference 2021
<p>The dataset is part of a new open EEG database designed to answer a need for more publicly available EEG-based dataset to design and benchmark passive brain-computer interface pipelines (as detailed in [Hinss2021]). This database is currently being created and will be fully released before the end of the year. It will include data acquired over 30 participant, 4 tasks and 3 sessions. For this competition, hosted by the Neuroergonomics Conference 2021, only one task and half the participants will be analyzed. Hence, this competition focuses on a renowned task that elicits various levels of mental/cognitive workload: the Multi-Atribute Task Battery-II (MATB-II) developed by NASA (https://matb.larc.nasa.gov/). It is composed of 4 sub-tasks: system monitoring, tracking, resource management and communications. By varying the number and complexity of the sub-tasks, 3 levels of workload were elicited (verified through statistical analyzes of both subjective and objective -behavioral and cardiac- data). Each difficulty level was performed by 15 subjects (6 female; 9 average 25 y.o.) during 5 minutes per session, in a pseudo-randomized order. Each session was separated by 7 days. We used a 62 actiChamp EEG channels device (BrainProducts; electrode placement 10-20 system).</p> <p> </p> <p><strong>For the competition, your goal is to predict the mental workload for a given subject (intra-subject estimation) using the EEG data from another session (inter-session adaptation). More information on the conference website and in the documentation file.</strong></p>
COG-BCI database: A multi-session and multi-task EEG cognitive dataset for passive brain-computer interfaces
<p>Brain-Computer Interfaces, and especially passive Brain-Computer Interfaces (pBCI), with their ability to estimate and detect mental states, are receiving increasing attention from both the scientific and the research and development communities. Many pBCIs aim to increase the safety of complex work environments such as in the aeronautical domain. Therefore, mental workload, vigilance and decision-making are some of the most commonly examined aspects of cognition within this field of research. A large proportion of pBCIs involve a component of machine learning and signal processing as the data that are collected need to be transformed into a reliable estimate of the users’ current mental state (e.g. mental workload). Improving this component is a major challenge for researchers, requiring large quantities of data. While data sharing is common for the active BCI community, open pBCI datasets are scarcer and generally incomplete with regards to the information they report. This is particularly true for datasets encompassing several tasks or sessions, which are of importance for tackling the challenges of transfer learning. Testing new pipelines, feature extraction algorithms and classifiers are central issues for future advances in research within this domain, as well as for algorithm benchmark and research reproducibility.The COG-BCI database presented here is comprised of the recordings of 29 participants over 3 individual sessions with 4 different tasks designed to elicit different cognitive states. This results in a total of over 100 hours of open electrophysiological (EEG) and electrocardiogram (ECG) data. The project was validated by the local ethical committee of the University of Toulouse (CER number 2021-342). The dataset was validated on a subjective, behavioral and physiological level (i.e. cardiac and cerebral activity), to ensure its usefulness to the pBCI community. This body of work represents a large effort to promote the use of pBCIs, as well as the use of open science.</p> <p> </p> <p><strong>The data are in the Brain Imaging Data Structure (BIDS) format. For more information, please read the COG-BCI_info.pdf file.</strong></p> <p><strong>Please note that version 4 corrected an electrode name mismatch, for which we sincerely apologize. The answers to the RSME and KSS questionnaires are provided in two separate .txt files.</strong></p>
Dataset Single-subject EEG measurement of interhemispheric transfer-time for the in-vivo estimation of axonal morphology
<p>This dataset is a subset of the data presented in the article Single‐subject electroencephalography measurement of interhemispheric transfer time for the in‐vivo estimation of axonal morphology Rita Oliveira, Marzia De Lucia, Antoine Lutti</p> <p><a href="https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.26420">https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.26420</a></p> <p><br> For a complete description of our approach for axonal morphology estimation in-vivo, see: <em>Oliveira, R., Pelentritou, A., Di Domenicantonio, G., De Lucia, M., and Lutti, A. (2022). In vivo Estimation of Axonal Morphology From Magnetic Resonance Imaging and Electroencephalography Data. Front. Neurosci. 16, 1–18. doi: 10.3389/fnins.2022.874023.</em></p> <p>This dataset contains the following Matlab files:</p> <ul> <li>CD_CondNameVisualField_LeftBrainOccipital.mat - Current source densities (pA.m) of each brain vertice, EEG trial, and time point for the left brain occipital cortex [#trials x #vertices x #timepoints]</li> <li>CD_CondNameVisualField_RightBrainOccipital.mat - Current source densities (pA.m) of each brain vertice, EEG trial, and time point for the right brain occipital cortex [#trials x #vertices x #timepoints]</li> <li>Stats_Source_CondNameVF_Occipital_LeftBrainOccipital.mat - Result of the cluster permutation for the left brain cortex for the CondNameVF, CondName being Left or Right visual stimulation (Fieldtrip stat structure)</li> <li>Stats_Source_CondNameVF_Occipital_RightBrainOccipital.mat - Result of the cluster permutation for the right brain cortex for the CondNameVF, CondName being Left or - Right visual stimulation (Fieldtrip stat structure)</li> <li>time_vec.mat - Time vector associated with the timecourses [1 x #timepoints]</li> <li>Occipital_vertices.mat - Structure containing the vertices of the brain mesh of the region of interest. Occipital_vertices.Vertices [1 x #vertices]</li> <li>G_ratio_samples.mat - MRI g-ratio sampled along the occipital transcallosal tract [#samples x 1]</li> <li>Tract_length.mat - Length of the occipital transcallosal tract (double)</li> </ul> <p>The analysis scripts that allow the users to replicate the results of the original publication can be found here: <a href="https://github.com/DNC-EEG-platform/SingleSubjectIHTTEstimation">https://github.com/DNC-EEG-platform/SingleSubjectIHTTEstimation</a><br> <br> Funding: Swiss National Science Foundation (grant no 320030 184784 and 32003B 212981), ROGER DE SPOELBERCH Foundation and Bertarelli Catalyst Foundation.</p> <p> </p> <p>Author: Rita Oliveira<br> PIs: Marzia De Lucia, Antoine Lutti</p> <p>Laboratory for Neuroimaging Research</p> <p>Lausanne University Hospital & University of Lausanne, Lausanne, Switzerland</p> <p>Copyright (C) 2022 Laboratory for Neuroimaging Research</p> <p> </p>
ESAA: an EEG-Speech auditory attention detection database
<p>We build a database for AAD research, which consists of competing speech stimuli and associated human neural responses, i.e, electroencephalography (EEG) recordings, namely EEG-Speech AAD (ESAA) database. This is the first AAD database with speech stimuli in a tonal language (Mandarin). Moreover, we develop an AAD baseline as a reference model for decoding which speech stream a listening subject is attending to (speaker attention detection), and a baseline for decoding which spatial locus a listening subject is attending to (speaker locus attention detection) on the ESAA database.</p> <p>We release the source code and the database for use in research purpose.</p> <p>This database consists of response data for 17 normal-hearing subjects (S1-S17). It includes:</p> <p>- 64-channel EEG data: responses to two-speaker speech stimuli<br> - Auditory stimuli data (clean): Chinese short stories narrated by a female and a male professional story teller. <br> - Auditory stimuli data (hrtf): Auditory stimuli after head-related transfer function (HRTF) filtering (simulating sound coming from ± 90 deg).<br> - Preprocessing code<br> - AAD baseline (CNN model)</p>
EEG: Visual Working Memory + Cabergoline Challenge
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EEG: Visual Working Memory in Acute TBI
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Raw EEG-EOG data used in the publication "Auditory Electrooculogram-based Communication System for ALS Patients in Transition from Locked-in to Complete Locked-in State"
<p>The dataset includes raw EEG and EOG recordings during BCI experiments for three patients: p11, p13, p15, and p16. The structure of the dataset is the following: patient/visit/day.</p> <p>The experiment is described in detail in the publication "Auditory Electrooculogram-based Communication System for ALS Patients in Transition from Locked-in to Complete Locked-in State". The correspondence between raw file and BCI session is reported in the attached pdf file "Supplementary Table S5 Session to Raw File Recordings Correspondence".</p> <p>The datasets include EEG and EOG channels. The data are raw (i.e. non filtered and non processed). Data have been acquired with a sampling rate of 500Hz using active electrodes and the amplifier V-Amp DC (Brain Products, Germany). EOG channels are labeled EOGU, EOGD, EOGR, EOGL namely for EOG up, down, right, left; the location in the 10-20 system are respectively SO1, IO1, LO1, LO2.</p> <p>The data are marked with triggers: for each session two markers indicate start and end of the session; for each trial markers indicate start of baseline, start of presentation of question, start of response time, start of feedback. Each trial was marked in a different way if it was a yes trial belonging to a training or feedback session, a no trial belonging to a training or feedback session, or a trial belonging to a speller session. The markers that have been used are the following:<br> <strong>start</strong> 9<br> <em> yes no speller</em><br> <strong>baseline</strong> 10 11 12<br> <strong>presentation</strong> 5 6 7<br> <strong>response</strong> 4 8 13<br> <strong>feedback</strong> 1 2 3</p> <p><strong>end</strong><strong> </strong> 15</p>
Resting-State High-Density EEG using EGI GES 300 with 256 Channels of Healthy Elders, People with Subjective and Mild Cognitive Impairment and Alzheimer's Disease
<p>This repository contains Matlab files including 4 samples of resting-state EEG recording for Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), Subjective Cognitive Decline (SCD), and Healthy Controls (HC) using the HD-EEG EGI GES 300.</p> <p><strong>[AD: i108, MCI: i100, SCD: i090, HC: s055]</strong></p> <p> </p> <p><strong>Participants & Settings</strong></p> <p>In total 230 participants have been recruited from the memory and dementia clinic of the Greek Association of Alzheimer’s Disease and Related Disorders (GAADRD) and the 1st Department of Neurology, U.H. AHEPA, Aristotle University of Thessaloniki, Greece.</p> <p>The full dataset includes:</p> <p><strong>Healthy Controls Elders (60+ years old)</strong>: 33 participants</p> <p><strong>Subjective Cognitive Decline:</strong> 34 participants</p> <p><strong>Mild Cognitive Impairment</strong>: 79 participants</p> <p><strong>Alzheimer's Disease</strong>: 48 participants</p> <p><strong>Healthy Young (25-40 years old):</strong> 36 participants</p> <p>The study was carried out in accordance with the Declaration of Helsinki and received approval by the Scientific and Ethics Committee of GAADRD (No56_27/11/2016), and written informed consent was obtained from all participants prior to their participation in the study. The diagnosis of AD was conducted by a neuropsychiatrist according to their medical history, neuropsychological performance, structural magnetic resonance imaging (MRI), and clinical and neurological examinations.</p> <p>Participants with AD fulfilled the National Institute of Neurological and Communication Disorders and Stroke/Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA) criteria for probable AD, as well as the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) criteria for dementia of Alzheimer’s type (American Psychological Association, 1994). On the other hand, the MCI participants fulfilled the Petersen criteria, while the SCD group met International Working Group-2 guidelines and the recent National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease (NI-AA), as well as the SCD-I Working Group instructions. </p> <p><strong>Resting-State EEG Recording</strong></p> <p>Fifteen-minute resting EEG activity was recorded for all the participants. For the whole duration of the resting state EEG recording, participants were advised to keep themselves relaxed as much as possible, close their eyes and open them after the researcher’s demand, sit still, minimize blinking or mouth movements and let their mind wander. The experimental procedure was monitored by a research assistant aiming to identify cases of horizontal eye movements, continued blinking, or excessive movement by visually inspecting the EEG traces during the experiment. More specifically, an EEG was registered for both resting conditions (eyes open, EO and eyes closed, EC) for at least 2–3 min for each period.</p> <p><strong>EEG Data Acquisition</strong></p> <p>The EEG data were collected by using the EGI 300 Geodesic EEG system (GES 300, CERTH-ITI, Thessaloniki, Greece) with a 256-channel HydroCel Geodesic Sensor Net (HCGSN) and a sampling rate of 250 Hz (EGI Eugene, OR). Moreover, the researcher placed the electrodes in accordance with the 256 HCGSN adult 1.0 montage system, while the signals were recorded relative to a vertex reference electrode (Cz), with AFz as the ground electrode with the electrodes’ impedance below 50 kΩ throughout the experimental procedure, as recommended for the high-input impedance amplifier. In detail, the HD-EEG data were analyzed offline in order to detect any artifact, as well as to conduct pre-processing (filtering, segmentation, bad channel replacement) using Net Station 4.3 software (EGI). HD-EEG data were initially filtered with a 5th-order bandpass Butterworth IIR filter of 0.3–30 Hz. Once the segmentation was completed, the detection of artifacts was performed by using the Net Station artifact detection tool for the automatic detection of excessive eye blinking and movement. Afterward, the signals were baseline corrected using 200 msec before the start of the experiment period and average re-referenced to transform them into reference-independent values.</p> <p> </p> <p><strong>Full Dataset Access</strong></p> <p>More information about the sample dataset and access to the full dataset can be available after request via e-mail:</p> <p><strong>Ioulietta Lazarou</strong> BSc, MSc, PhD candidate</p> <p>Neuropsychologist - Clinical Research Associate </p> <p>Centre for Research and Technology Hellas (CERTH), Information Technologies Institute (ITI)</p> <p>6th km Charilaou-Thermi Road, P.O. Box 60361, 57001 Thermi-Thessaloniki, Greece</p> <p>E-mail: <a href="mailto:iouliettalaz@iti.gr">iouliettalaz@iti.gr</a></p>
Dataset of Concurrent EEG, ECG, and Behavior with Multiple Doses of transcranial Electrical Stimulation
<p><strong>Supporting materials for the GX Dataset.</strong> </p> <p> The GX Dataset is a dataset of combined tES, EEG, physiological, and behavioral signals from human subjects.</p> <p><strong>Publication</strong></p> <p>A full data descriptor is published in <a href="https://doi.org/10.1038/s41597-021-01046-y">Nature Scientific Data</a>. <strong>Please cite this work as</strong>:</p> <blockquote> <p>Gebodh, N., Esmaeilpour, Z., Datta, A. et al. Dataset of concurrent EEG, ECG, and behavior with multiple doses of transcranial electrical stimulation. Sci Data 8, 274 (2021). https://doi.org/10.1038/s41597-021-01046-y</p> </blockquote> <p> </p> <p><strong>Descriptions</strong></p> <p>A dataset combining high-density electroencephalography (EEG) with physiological and continuous behavioral metrics during transcranial electrical stimulation (tES; including tDCS and tACS). Data includes within subject application of nine High-Definition tES (HD-tES) types targeted three brain regions (frontal, motor, parietal) with three waveforms (DC, 5Hz, 30Hz), with more than 783 total stimulation trials over 62 sessions with EEG, physiological (ECG or EKG, EOG), and continuous behavioral vigilance/alertness metrics (CTT task).</p> <p><strong>Acknowledgments</strong></p> <p>Portions of this study were funded by X (formerly Google X), the Moonshot Factory. The funding source had no influence on study conduction or result evaluation. MB is further supported by grants from the National Institutes of Health: R01NS101362, R01NS095123, R01NS112996, R01MH111896, R01MH109289, and (to NG) NIH-G-RISE T32GM136499.</p> <p>We would like to thank Yuxin Xu and Michaela Chum for all their technical assistance.</p> <p> </p> <p><strong>Extras</strong></p> <p>For downsampled data (1 kHz ) please see (in .mat format):</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3840614">Experiment 1</a> : https://doi.org/10.5281/zenodo.3840614</li> <li><a href="https://doi.org/10.5281/zenodo.3840616">Experiment 2</a> : https://doi.org/10.5281/zenodo.3840616</li> </ul> <p> </p> <p>Code used to import, process, and plot this dataset can be found here:</p> <ul> <li><a href="https://github.com/ngebodh/GX_tES_EEG_Physio_Behavior">GitHub</a> : <a href="https://github.com/ngebodh/GX_tES_EEG_Physio_Behavior">https://github.com/ngebodh/GX_tES_EEG_Physio_Behavior</a></li> </ul> <p> </p> <p>Additional figures for this project have been shared on Figshare. Trial-wise figures can be found here:</p> <ul> <li><a href="https://figshare.com/articles/figure/Dataset_of_Concurrent_EEG_ECG_and_Behavior_with_Multiple_Doses_of_transcranial_Electrical_Stimulation-_Stimulation_Trials_PSD/14810517">PSD</a>: <a href="https://doi.org/10.6084/m9.figshare.14810517.v1">https://doi.org/10.6084/m9.figshare.14810517.v1</a></li> <li><a href="https://figshare.com/articles/figure/Dataset_of_Concurrent_EEG_ECG_and_Behavior_with_Multiple_Doses_of_transcranial_Electrical_Stimulation-_Stimulation_Trials_Topoplots/14810478">Topoplots During Stimulation:</a> <a href="https://doi.org/10.6084/m9.figshare.14810478">https://doi.org/10.6084/m9.figshare.14810478</a></li> <li><a href="https://doi.org/10.6084/m9.figshare.14810442.v1">Voltage timeseries, spectrogram and behavior:</a> <a href="https://doi.org/10.6084/m9.figshare.14810442">https://doi.org/10.6084/m9.figshare.14810442.v1</a></li> </ul> <p> </p> <p>The full dataset is also provided in <a href="https://bids.neuroimaging.io/">BIDS</a> format here:</p> <ul> <li><a href="https://doi.org/10.18112/openneuro.ds003670.v1.1.0">Data in BIDS format:</a> https://doi.org/10.18112/openneuro.ds003670.v1.1.0</li> </ul> <p><strong>Data License </strong><br><a href="https://creativecommons.org/licenses/by/4.0/">Creative Common 4.0 with attribution (CC BY 4.0)</a></p> <p> </p> <p><strong>NOTE</strong></p> <p><strong>Please email ngebodh01@citymail.cuny.edu with any questions.</strong></p> <p> </p>
Bipolar EEG dataset - music
<p>Dataset setting out to investigate neural responses to continuous musical pieces with bipolar EEG. Analysis code (and usage instructions) to derive neural responses to the temporal fine structure of the stimuli is <a href="https://github.com/octaveEtard/EEGmusic2020">on Github</a>. The EEG data processed to this end is provided here, as well as the raw data to enable different analyses (e.g. slower cortical responses).</p> <p><strong># Introduction</strong></p> <p>This dataset contains bipolar scalp EEG responses of 17 subjects listening to continuous musical pieces (Bach's Two-Part Inventions), and performing a vibrato detection task.</p> <p>Naming conventions:</p> <p>- The subject IDs are <code>EBIP01, EBIP02 ... EBIP17</code>.<br> - The different conditions are labelled to indicate the instrument that was being attended: <code>fG</code> and <code>fP</code> for the Guitar and Piano in quiet (Single Instrument (SI) conditions), respectively; and <code>fGc</code> and <code>fPc</code> for Competing conditions where both the instruments are playing together, but where the subjects should be selectively attending to the Guitar or Piano, respectively (Competing Instrument (CI) conditions).<br> - An appended index from 2 to 7 designates the invention that was played (index 1 corresponds to the training block for which no EEG data was recorded). Note that this index does not necessarily corresponds to the order in which the stimuli were played (order was pseudo-randomised).<br> <br> For example, the EEG file named <code>EBIP08_fGc_4</code> contains EEG data from subject <code>EBIP08</code> performing the competing instrument task (CI condition), attending to the guitar (ignoring the piano), and the stimulus that was played was the invention #4.</p> <p><strong># Content</strong></p> <p>The general organisation of the dataset is as follow:<br> <br> <code>data</code><br>  <code>├─── behav</code>              <em>folder containing the behavioural data</em><br>  <code>├─── EEG</code>            <em>folder containing the EEG data</em><br>  <code>│   ├─── processed</code><br>  <code>│   └─── raw</code><br>  <code>├─── linearModelResults</code>       <em>folder containing the results from the analysis code</em><br>  <code>└─── stimuli</code> <em>folder containing the stimuli</em><br>      <code>├─── features</code><br>      <code>├─── processedInventions</code><br>      <code>└─── rawInventions</code></p> <p>This general organisation is the one expected by the code. The location of the <code>data</code> folder and/or these main folders can be personalised in the <code>functions/+EEGmusic2020/getPath.m</code> function in the Github repository. The architecture of the sub-folders in each of these folders is specified by the functions <code>makePathEEGFolder</code>, <code>makePathFeatureFiles</code> and <code>makePathSaveResults</code>. The naming of the files within them is implemented by <code>makeNameEEGDataFile</code> and <code>makeNameEEGDataFile</code> (all these functions being in <code>functions/+EEGmusic2020</code>).</p> <p> </p> <p>- The <code>behav</code> folder is structured as follow:<br> <br> <code>behav</code><br> <code>├─── EBIP02</code><br> <code>│ ├─── EBIP02_keyboardInputs_fGc_2.mat</code> <em>file containing variables:</em><br> <code>│ │ ├─── timePressed</code> <em>key press time (in seconds, relative to stimulus onset)</em><br> <code>│ │ └─── keyCode</code> <em>ID of the keys that were pressed</em><br> <code>│ └─── ...</code><br> <code>├─── ...</code><br> <code>├─── vibTime </code><br> <code>│ ├─── vibTime_2.mat</code> <em>file containing variables:</em><br> <code>│ │ ├─── idxNoteVib</code> <em>index (in the MIDI files) of the notes in which vibratos were inserted</em><br> <code>│ │ ├─── instrumentOrder</code> <em>order of the instruments in <code>idxNoteVib</code> and <code>vibTiming</code> variables</em><br> <code>│ │ └─── vibTiming</code> <em>timing of vibrato onsets in the track (in s)</em><br> <code>│ ├─── ...</code><br> <code>└─── clickPerformance_RT_2.0.mat</code> <em>file containing behavioural results for all subjects (FPR, TPR, etc.):</em></p> <p><code>instrumentOrder</code> indicates to what instrument each column of <code>idxNoteVib</code> and <code>vibTiming</code> refers to. The data for <code>EBIP01</code> missing due to a technical error.</p> <p> </p> <p>- The <code>EEG/raw</code> folder contains unprocessed EEG data for all subjects, and files indicating the order in which the inventions were played. It is structured as follow:<br> <br> <code>EEG</code><br> <code>├─── raw</code><br> <code>│ ├─── EBIP01</code><br> <code>│ │ ├─── EBIP01_EEGExpParam.mat</code> <em>file containing variables:</em><br> <code>│ │ │ ├─── conditionOrder </code> <em>whether this subject started by listening to the guitar or piano</em><br> <code>│ │ │ └─── partsOrder </code> <em>order in which the inventions were presented to this subject</em><br> <code>│ │ ├─── EBIP01_fGc_2.[eeg/vhdr/vmrbk]</code> <em>raw EEG data files</em><br> <code>│ │ ├─── ...</code><br> <code>│ ├─── ...</code></p> <p>The <code>conditionOrder</code> variable can assume two values: either <code>{'fG','fP'}</code> indicating the subject started by listening to the guitar or <code>{'fP','fG'}</code> indicating the subject started by listening to the piano. The <code>partsOrder</code> variable is a 2 x 6 matrix containing the indices (2 to 7) of the inventions that were played, ordered in the presentation order. During the first block, the instrument <code>conditionOrder{1}</code> was attended, and the invention # <code>partsOrder(1,1)</code> was played. During the second block, the instrument <code>conditionOrder{2}</code> was attended, and the invention <code>#partsOrder(2,1)</code> was played, etc.</p> <p>Each EEG files contains 3 channels: 2 are the bipolar electrophysiological channels, and one (labelled <code>Sound</code>) contains a recording of the stimuli that were played and that was simultaneously recorded at the same sampling rate as the EEG data (5 kHz) by the amplifier through an acoustic adapter. The files also contain triggers that indicate the beginning and end of the stimuli (labelled <code>S 1</code> and <code>S 2</code> respectively). The sound channel and triggers can be used to temporally align the EEG data and stimuli.</p> <p> </p> <p>The <code>EEG/processed</code> folder contains processed EEG data for all subjects, as required for the analyses carried out in the <a href="https://github.com/octaveEtard/EEGmusic2020">code.</a> It is organised as follow:<br> <br> <code>EEG</code><br> <code>├─── processed</code><br> <code>│ └─── Fs-5000</code> <em>sampling rate</em><br> <code>│ └─── HP-130</code> <em>processing that was applied</em><br> <code>│ │ ├─── EBIP01</code><br> <code>│ │ │ ├─── ...</code> <em>processed EEG data files</em><br> <code>│ │ ├─── ...</code><br> <code>│ └─── noProc</code><br> <code>│ ├─── ...</code></p> <p>This structure is specified by the <code>makePathEEGFolder</code> function, and the file names by <code>makeNameEEGDataFile</code>. In the files in the <code>noProc</code> folder, the EEG data was simply aligned with the stimuli, but is otherwise unprocessed. Events were added to mark stimulus onset and offset (labelled <code>stimBegin</code> and <code>stimEnd</code>). In the other folders, the EEG data was furthermore high-pass filtered at 130 Hz (HP-130).</p> <p> </p> <p>- The <em>linearModelResults</em> folder contains the results from the linear model analyses:<br> <br> <code>linearModelResults</code><br> <code>└─── Fs-5000</code> <em>sampling rate</em><br> <code>│ └─── HP-130</code> <em>processing of the EEG data</em><br> <code>│ │ └─── LP-2000</code> <em>processing of the stimulus feature</em><br> <code>│ │ ├─── ...</code> <em>result files</em><br> <code>│ ├─── ...</code></p> <p>This structure and file names are specified by the <code>makePathSaveResults</code> function.</p> <p> </p> <p>- The <code>rawInventions</code> folder contains the orignal data that was used to construct the stimuli:<br> <br> <code>rawInventions</code><br> <code>├─── invent1</code> <em>invention index</em><br> <code>│ ├─── invent1_60bpm.mid</code> <em>MIDI file</em><br> <code>│ ├─── invent1_60bpm_guitar.wav </code> <em>guitar track</em><br> <code>│ └─── invent1_60bpm_piano.wav </code> <em>piano track</em><br> <code>│</code><br> <code>├─── ...</code></p> <p>In this folder (and <strong>only </strong>in this folder), the numbering of the inventions differs from the one otherwise used throughout. The correspondence is as shown below:<br> Raw invention # | Feature, etc. #<br> 1, 2, 3, 4 -> 1, 2, 3, 4<br> 7, 8, 9 -> 5, 6,7</p> <p> </p> <p>- The <code>processedInventions</code> contains invention waveforms that have been transformed. The instrument and invention index are indicated by a suffix in the file names ('G': guitar, 'P': piano). 'zv' indicates that the vibratos were replaced by zeros. 'noOnset30ms' indicates that the onset of the notes was suppressed in a 30 ms window.</p> <p> </p> <p>- The <code>features</code> folder contains specific features of the stimuli for use in the models:<br> <br> <code>features</code><br> <code>└─── Fs-5000</code> <em>sampling rate of the feature</em><br> <code> └─── LP-2000</code> <em>processing of the feature </em><br> <code> ├─── waveform </code> <em>feature name (here: stimulus waveform)</em><br> <code> │ ├─── ... </code> <em>feature files</em><br> <code> └─── WNO</code> <em>Waveform No Onset (stimulus waveform with note onsets removed)</em><br> <code> ├─── ... </code></p> <p>The naming convention is as highlighted above for the <code>processedInventions</code> folder. SI conditions correspond to 'G' & 'P' files, and CI conditions to 'PG' files. In the latter case, 'fG' indicates the attended instrument is the guitar and 'fP' the piano.<br> These files notably contain the variables <code>attended</code> and <code>ignored</code> that contains the feature for the attended and ignored instruments (the <code>ignored</code> field is only present in the CI conditions).<br> Note that a pair of two files corresponding to the same invention in a CI condition ('PGfG' & 'PGfP') effectively contain the same information with the <code>attended</code> and <code>ignored</code> variables flipped.</p>
PIR data and EEG scoring for Wellcome Open Research methods paper (Brown et al 2016)
<p>PIR data and EEG-scored sleep in the Wellcome Open Research article:</p> <p>'COMPASS: Continuous Open Mouse Phenotyping of Activity and Sleep Status'</p> <p> </p> <p>1sensorPIRvsEEGdata.csv - PIR based actigraphy for mice to compare to EEG-scored sleep</p> <p>EEG_4mice10sec.csv - Manually scored sleep from EEG files (.edf) from 10.5281/zenodo.160118</p> <p>blandAltLandD.csv - paired estimates of sleep by PIR and EEG methods (sum of 4 mice over 1 day in 30min bins)</p> <p><br> 1monthPIRsleep.csv - 1 month of activity for for figure 4</p> <p><br> 24mice_activity_LD1week.csv - activity and sleep for 24 wt mice (for hierarchical clustering in figure 4)<br> 24mice_sleep_LD1week.csv </p> <p> </p> <p> </p>
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>
Dataset of Concurrent EEG, ECG, and Behavior with Multiple Doses of transcranial Electrical Stimulation-Exp1-Data Downsampled
<p><strong>GX Dataset downsampled - Experiment 1</strong></p> <p>The GX Dataset is a dataset of combined tES, EEG, physiological, and behavioral signals from human subjects.<br>Here the GX Dataset for <strong>Experiment 1</strong> is downsampled to 1 kHz and saved in .MAT format which can be used in both MATLAB and Python.</p> <p><strong>Publication</strong></p> <p>A full data descriptor is published in <a href="https://doi.org/10.1038/s41597-021-01046-y">Nature Scientific Data</a>. <strong>Please cite this work as</strong>:</p> <blockquote> <p>Gebodh, N., Esmaeilpour, Z., Datta, A. et al. Dataset of concurrent EEG, ECG, and behavior with multiple doses of transcranial electrical stimulation. Sci Data 8, 274 (2021). https://doi.org/10.1038/s41597-021-01046-y</p> </blockquote> <p><strong>Descriptions</strong></p> <p>A dataset combining high-density electroencephalography (EEG) with physiological and continuous behavioral metrics during transcranial electrical stimulation (tES). Data includes within subject application of nine High-Definition tES (HD-tES) types targeted three brain regions (frontal, motor, parietal) with three waveforms (DC, 5Hz, 30Hz), with more than 783 total stimulation trials over 62 sessions with EEG, physiological (ECG, EOG), and continuous behavioral vigilance/alertness metrics.</p> <p><strong>Acknowledgments</strong></p> <p>Portions of this study were funded by X (formerly Google X), the Moonshot Factory. The funding source had no influence on study conduction or result evaluation. MB is further supported by grants from the National Institutes of Health: R01NS101362, R01NS095123, R01NS112996, R01MH111896, R01MH109289, and (to NG) NIH-G-RISE T32GM136499.</p> <p><strong>Extras</strong></p> <p>Back to <a href="https://doi.org/10.5281/zenodo.4456079">Full GX Dataset</a> : https://doi.org/10.5281/zenodo.4456079</p> <p> </p> <p>For downsampled data (1 kHz ) please see (in .mat format):</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3840614">Experiment 1</a> : https://doi.org/10.5281/zenodo.3840614</li> <li><a href="https://doi.org/10.5281/zenodo.3840616">Experiment 2</a> : https://doi.org/10.5281/zenodo.3840616</li> </ul> <p> </p> <p>Code used to import, process, and plot this dataset can be found here:</p> <ul> <li><a href="https://github.com/ngebodh/GX_tES_EEG_Physio_Behavior">GitHub</a> : <a href="https://github.com/ngebodh/GX_tES_EEG_Physio_Behavior">https://github.com/ngebodh/GX_tES_EEG_Physio_Behavior</a></li> </ul> <p> </p> <p>Additional figures for this project have been shared on Figshare. Trial-wise figures can be found here:</p> <ul> <li><a href="https://figshare.com/articles/figure/Dataset_of_Concurrent_EEG_ECG_and_Behavior_with_Multiple_Doses_of_transcranial_Electrical_Stimulation-_Stimulation_Trials_PSD/14810517">PSD</a>: <a href="https://doi.org/10.6084/m9.figshare.14810517.v1">https://doi.org/10.6084/m9.figshare.14810517.v1</a></li> <li><a href="https://figshare.com/articles/figure/Dataset_of_Concurrent_EEG_ECG_and_Behavior_with_Multiple_Doses_of_transcranial_Electrical_Stimulation-_Stimulation_Trials_Topoplots/14810478">Topoplots During Stimulation:</a> <a href="https://doi.org/10.6084/m9.figshare.14810478">https://doi.org/10.6084/m9.figshare.14810478</a></li> <li><a href="https://doi.org/10.6084/m9.figshare.14810442.v1">Voltage timeseries, spectrogram and behavior:</a> <a href="https://doi.org/10.6084/m9.figshare.14810442">https://doi.org/10.6084/m9.figshare.14810442.v1</a></li> </ul> <p> </p> <p>The full dataset is also provided in <a href="https://bids.neuroimaging.io/">BIDS</a> format here:</p> <ul> <li><a href="https://doi.org/10.18112/openneuro.ds003670.v1.1.0">Data in BIDS format:</a> https://doi.org/10.18112/openneuro.ds003670.v1.1.0</li> </ul> <p><strong>Data License </strong><br><a href="https://creativecommons.org/licenses/by/4.0/">Creative Common 4.0 with attribution (CC BY 4.0)</a></p> <p> </p> <p><strong>NOTE</strong></p> <p><strong>Please email ngebodh01@citymail.cuny.edu with any questions.</strong></p> <p> </p> <p><br><strong>Updates</strong></p> <ul> <li><strong>Version 2.1.0</strong> <ul> <li>Behavioral data (ptracker) adjusted for all files. Previous version had one participant's data overwriting all behavioral data when downsampled. Mat format now explicitly >v7.3 </li> </ul> </li> <li><strong>Version 2</strong> <ul> <li>Stimulation trigger labels now adjusted. Previous labels were missmatched for Experiment 1's data. </li> </ul> </li> </ul> <p> </p>
MOVING: a Multi-MOdal dataset of EEG signals and VIrtual Glove hand trackING
<p>A new Multi-modal dataset comprising neural EEG signals and kinematic data associated with three hand movements — open/close, finger tapping, and wrist rotation — along with a rest period. The dataset, obtained from eleven subjects using a 32-channel dry wireless EEG system, also includes synchronized kinematic data captured by a Virtual Glove (VG) system equipped with two orthogonal Leap Motion Controllers. The use of these two devices allows for fast assembly (~ 1 minute) while introducing more noise than the gold standard devices for data acquisition. The data set, obtained from 11 subjects using a 32-channel dry wireless EEG system, also includes synchronized kinematic data captured by a Virtual Glove (VG) system equipped with two orthogonal Leap Motion Controllers. </p> <p>For citation please refer to the paper:<br>Mattei, E.; Lozzi, D.; Di Matteo, A.; Cipriani, A.; Manes, C.; Placidi, G. MOVING: A Multi-Modal Dataset of EEG Signals and Virtual Glove Hand Tracking. Sensors 2024,24, 5207. https://doi.org/10.3390/s24165207 </p> <p><strong>References</strong>:</p> <p>Placidi, Giuseppe. "<em>A smart virtual glove for the hand telerehabilitation.</em>" Computers in Biology and Medicine 37.8 (2007): 1100-1107.</p> <p>Placidi, Giuseppe, et al. "<em>Measurements by a LEAP-based virtual glove for the hand rehabilitation.</em>" Sensors 18.3 (2018): 834.</p> <p>Placidi, Giuseppe, et al. "<em>Patient–therapist cooperative hand telerehabilitation through a novel framework involving the virtual glove system.</em>" Sensors 23.7 (2023): 3463.</p>
ScienceDex guides
Understand access before you commit
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.