Multi-subject, multi-modal (sMRI+EEG) neuroimaging dataset on face processing. Original data described at https://www.nature.com/articles/sdata20151 This is repackaged version of the EEG data in EEGLAB format. The data has gone through minimal preprocessing including (see wh_extracteeg_BIDS.m): - Ignoring fMRI and MEG data (sMRI preserved for EEG source localization) - Extracting EEG channels out of the MEG/EEG fif data - Adding fiducials - Renaming EOG and EKG channels - Extracting events from event channel - Removing spurious events 5, 6, 7, 13, 14, 15, 17, 18 and 19 - Removing spurious event 24 for subject 3 run 4 - Renaming events taking into account button assigned to each subject - Correcting event latencies (events have a shift of 34 ms) - Resampling data to 250 Hz (this is a step that is done because this dataset is used as tutorial for EEGLAB and need to be lightweight - Merging run 1 to 6 - Removing event fields urevent and duration - Filling up empty fields for events boundary and stim_file. - Saving as EEGLAB .set format

Ramon Martinez, Dung Truong, Scott Makeig, Arnaud Delorme (UCSD, La Jolla, CA, USA)

A collection of 32-channel EEG / ERP data from 14 subjects (7 males, 7 females) acquired using the Neuroscan software (3.6 Gb), made available by the laboratory of Arnaud Delormes, along with electrode files and images presented in the experiment. Subjects are performing a go-nogo categorization task and a go-no recognition task on natural photographs presented very briefly (20 ms). Images are only available for viewing. Each subject responded to a total of 2500 trials. Data is CZ referenced and is sampled at 1000 Hz (total data size is 4Gb). Alternate datasets are also compiled including one from the EEGLAB software tutorial.

This data is linked to the publication "Electrophysiological signatures of brain aging in autism spectrum disorder" by Dickinson, Jeste and Milne, in which it is referenced as Dataset 1.EEG data were acquired via Biosemi Active two EEG system. The original recordings have been converted to .set and .fdt files via EEGLAB as uploaded here. There is a .fdt and a .set file for each recording, the .fdt file contains the data, the .set file contains information about the parameters of the recording (see https://eeglab.org/tutorials/ for further information). The files can be opened within EEGLAB software.The data were acquired from 28 individuals with a diagnosis of an autism spectrum condition and 28 neurotypical controls aged between 18 and 68 years. The paradigm that generated the data was a 2.5 minute (150 seconds) period of eyes closed resting.Ethical approval for data collection and data sharing was given by the Health Research Authority [IRAS ID = 212171].Only data from participants who provided signed consent for data sharing were included in this work and uploaded here.

This data set contains intracranial EEG data, analysis code and results associated with the manuscript, "Hippocampal Sharp-wave Ripples Linked to Visual Episodic Recollection in Humans". [DOI: 10.1126/science.aax1030]

Data files (.mat) and associated scripts (.m) are divided into folders according to the subject of the analysis (e.g. ripple detection, ripple-triggered averages, multivariate pattern analysis etc.) and are all contained in the .zip file: “Norman_et_al_2019_data_and_code_zenodo.zip".

The code is written in Matlab R2018b and run on a desktop computer with a 3.4Ghz Intel Core i7-6700 CPU with 64GB RAM.

Matlab's Signal Processing Toolbox is required.

General notes:

1) The data does not contain identifying details about the patients, nor voice recordings.

2) Before running the analyses, make sure you set the correct paths in the "startup_script.m" located in the main folder where the zip file was extracted.

3) To run the code, the following open-source toolboxes are required:

EEGLAB (https://sccn.ucsd.edu/eeglab/download.php), version: "eeglab14_1_2b".

A. Delorme, S. Makeig, EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods. 134, 9–21 (2004).

Mass Univariate ERP Toolbox (https://openwetware.org/wiki/Mass_Univariate_ERP_Toolbox), version: "dmgroppe-Mass_Univariate_ERP_Toolbox-d1e60d4".

D. M. Groppe, T. P. Urbach, M. Kutas, Mass univariate analysis of event-related brain potentials/fields I: A critical tutorial review. Psychophysiology. 48, 1711–1725 (2011).

*** Make sure you download the relevant toolboxes and save them in the "path_to_toolboxes" before running the analysis scripts (see "startup_script.m")

4) Code developed by other authors (redistributed here as part of the analysis code):

DRtoolbox (https://lvdmaaten.github.io/drtoolbox/), version: 0.8.1b.

L.J.P. van der Maaten, E.O. Postma, and H.J. van den Herik. Dimensionality Reduction: A Comparative Review. Tilburg University Technical Report, TiCC-TR 2009-005, 2009.

Scott Lowe / superbar (https://github.com/scottclowe/superbar), version: 1.5.0.

Oliver J. Woodford, Yair M. Altman / export_fig (https://github.com/altmany/export_fig).