DOI: 10.5061/dryad.q573n5tph
title: Data for: Intracranial entrainment reveals statistical learning across levels of abstraction

description: The following submission contains the data reported from the manuscript
 "Intracranial entrainment reveals statistical learning across levels
 of abstraction". The dataset was obtained from 8 neurosurgical
 patients who had intracranially implanted electrodes for seizure
 monitoring. Intracranial EEG (iEEG) data were recorded while the patients
 viewed a rapid stream of scene images. In the Category-Level Structured
 condition, patients viewed a series of trial-unique scene images, in which
 the categories of scenes were paired across repetitions (e.g., images of
 beach always followed by images of canyons). In the Exemplar-level
 Structured condition, participants viewed a sequence of 6 repeating scene
 images (from non-overlapping scene categories), which were paired across
 repetitions (e.g., image A always followed by image B). In the baseline
 Random condition, participants again viewed a sequence of 6 repeating
 scene images, but the images were presented in a random temporal order.
 Each image was presented for 250 ms, followed by a 250 ms
 inter-stimulus-interval period. The data presented here contain the raw
 iEEG data from the 8 patients during these task conditions, as well as
 information about the anatomical placement of their electrodes.
description: iEEG data were recorded from 8 patients who had electrodes
 surgically implanted for seizure monitoring. EEG data were recorded on a
 NATUS NeuroWorks EEG recording system. Data were collected at a sampling
 rate of 4096 Hz. Signals were referenced to an electrode chosen by the
 clinical team to minimize noise in the recording. To synchronize EEG
 signals with the experimental task, a custom-configured data acquisition
 system (DAQ) was used to convert signals from the research computer to
 8-bit "triggers" that were inserted into an open EEG channel
 (the TRIG channel).  The iEEG data presented here are
 the raw data. The raw data have not been processed, except that the date
 information in the metadata has been randomized to preserve
 anonymity.  Electrode contact locations were identified
 using post-operative CT and MRI scans. Reconstructions were completed in
 BioImage Suite and were subsequently registered to the patient's
 pre-operative MRI scan, resulting in contact locations projected into the
 patient's pre-operative space. The resulting files were converted
 from the Bioimagesuite format (.MGRID) into native space coordinates using
 FieldTrip functions. The coordinates were then used to create a mask in
 FSL in each participant's native space, with the coordinates of each
 contact occupying one voxel in the mask.  We then registered each
 patient's pre-operative anatomical scan to the MNI T1 2mm standard
 brain using linear registration. We then used this registration matrix to
 transform each electrode mask into standard space. The electrode locations
 reported here are in standard space.
description: The iEEG files are in .edf (European Data Format). They can be
 loaded in by programs such as MATLAB (using the Fieldtrip or EEGlab
 toolboxes) or Python (using libraries such as MNE). The
 iEEG files also contain accompanying files (.json and .tsv) which can be
 read in by standard text editors. The electrode
 location files is a .csv which can be read in by standard text editors or
 spreadsheet programs, such as Excel.