Published September 28, 2017 | Version v1
Dataset Open

Infant Sibling Project: Sample Files

  • 1. Department of Neurology, Boston Children's Hospital, Boston, MA, USA; Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
  • 2. Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
  • 3. Department of Psychological and Brain Sciences, Boston University, Boston MA USA
  • 4. Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Boston Children's Hospital, Harvard Medical School, Boston MA USA; Graduate School of Education, Harvard University, Cambridge MA USA

Description

These electroencephalography (EEG) data files were collected through the Infant Sibling Project (ISP), a prospective investigation examining infants at high versus low familial risk for autism spectrum disorder over the first 3 years of life.  Here we provide a subset of the full dataset, as example files for the Batch EEG Automated Processing Pipeline (BEAPP), and the Harvard Automated Processing Pipeline for EEG (HAPPE).  Both BEAPP and HAPPE may be downloaded at www.github.com.

Baseline EEG data was collected while a young child sat in a parent’s lap watching a research assistant blow bubbles or show toys for several minutes.1  12 sample baseline EEGs are provided here, in .mat format.  Auditory (event-related) EEG data was collected using an auditory double oddball paradigm, in which a stream of consonant-vowel stimuli was presented.   Stimuli included a “Standard” /ɖa/ sound 80% of the time, “Native” /ta/ sound 10% of the time, and “Non-Native” /da/ sound 10% of the time.2    10 sample auditory EEGs are provided here, in .mff format.

This sample dataset was chosen for demonstration of BEAPP and HAPPE for several reasons.  First, the longitudinal nature of the study led to data collection with different sampling rates (250 Hz and 500 Hz) and acquisition setups (64-channel Geodesic Sensor Net v2.0, and 128-channel HydroCel Geodesic Sensor Net, both from Electrical Geodesics, Inc., Eugene, OR).  Additionally, because young children cannot follow instructions to “rest” or remain still, EEG in these children typically contains greater amounts of artifact than EEG in typical adults.  BEAPP and HAPPE are targeted towards addressing these challenges.

The Infant Sibling Project was carried out in accordance with the recommendations of the Institutional Review Board at Boston University and Boston Children’s Hospital (#X06-08-0374), with written informed consent from all caregivers prior to their child’s participation in the study.  All files here have been deidentified, including alteration of exact acquisition dates.  Acquisition times have not been altered.

For additional information about data collection paradigms, and sample studies published on the larger ISP data set, please see the following references:

1. Levin, A. R., Varcin, K. J., O’Leary, H. M., Tager-Flusberg, H., and Nelson, C. A. (2017). EEG power at 3 months in infants at high familial risk for autism. J. Neurodev. Disord. 9, 1–13.

2. Seery A, Tager-Flusberg H, Nelson CA. Event-related potentials to repeated speech in 9-month-old infants at risk for autism spectrum disorder. J. Neurodev. Disord. 2014;6:43.

Notes

Acquisition information: baselineEEG01.mat: Sampling rate 250, Net type HydroCel GSN 128 1.0 baselineEEG02.mat: Sampling rate 250, Net type Geodesic Sensor Net 64 2.0 baselineEEG03.mat: Sampling rate 250, Net type Geodesic Sensor Net 64 2.0 baselineEEG04.mat: Sampling rate 250, Net type HydroCel GSN 128 1.0 baselineEEG05.mat: Sampling rate 250, Net type HydroCel GSN 128 1.0 baselineEEG06.mat: Sampling rate 250, Net type HydroCel GSN 128 1.0 baselineEEG07.mat: Sampling rate 250, Net type HydroCel GSN 128 1.0 baselineEEG08.mat: Sampling rate 500, Net type HydroCel GSN 128 1.0 baselineEEG09.mat: Sampling rate 500, Net type HydroCel GSN 128 1.0 baselineEEG10.mat: Sampling rate 500, Net type HydroCel GSN 128 1.0 baselineEEG11.mat: Sampling rate 250, Net type HydroCel GSN 128 1.0 baselineEEG12.mat: Sampling rate 250, Net type HydroCel GSN 128 1.0 Note that for several of the event-tagged files, an offset was introduced by the EEG acquisition equipment. Therefore, the event tag in the file occurs prior to when the participant was exposed to the stimulus. Offsets are detailed below for each file. auditoryEEG01.mff: Sampling rate 250, Net type HydroCel GSN 128 1.0, Offset 0 msec auditoryEEG02.mff: Sampling rate 250, Net type Geodesic Sensor Net 64 2.0, Offset 0 msec auditoryEEG03.mff: Sampling rate 250, Net type Geodesic Sensor Net 64 2.0, Offset 0 msec auditoryEEG04.mff: Sampling rate 250, Net type HydroCel GSN 128 1.0, Offset 0 msec auditoryEEG05.mff: Sampling rate 250, Net type HydroCel GSN 128 1.0, Offset 0 msec auditoryEEG06.mff: Sampling rate 250, Net type HydroCel GSN 128 1.0, Offset 0 msec auditoryEEG07.mff: Sampling rate 250, Net type HydroCel GSN 128 1.0, Offset 8 msec auditoryEEG08.mff: Sampling rate 500, Net type HydroCel GSN 128 1.0, Offset 18 msec auditoryEEG09.mff: Sampling rate 500, Net type HydroCel GSN 128 1.0, Offset 18 msec auditoryEEG10.mff: Sampling rate 500, Net type HydroCel GSN 128 1.0, Offset 18 msec

Files

auditoryEEG01.mff.zip

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