DS003620#

Runabout: A mobile EEG study of auditory oddball processing in laboratory and real-world conditions

Access recordings and metadata through EEGDash.

Citation: Magnus Liebherr, Andrew W. Corcoran, Phillip M. Alday, Scott Coussens, Valeria Bellan, Caitlin A. Howlett, Maarten A. Immink, Mark Kohler, Matthias Schlesewsky, Ina Bornkessel-Schlesewsky (2021). Runabout: A mobile EEG study of auditory oddball processing in laboratory and real-world conditions. 10.18112/openneuro.ds003620.v1.1.1

Modality: eeg Subjects: 44 Recordings: 370 License: CC0 Source: openneuro Citations: 4.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003620

dataset = DS003620(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Query & Filter

Filter by subject

dataset = DS003620(cache_dir="./data", subject="01")

Advanced query

dataset = DS003620(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

Cite This Dataset

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{ds003620,
  title = {Runabout: A mobile EEG study of auditory oddball processing in laboratory and real-world conditions},
  author = {Magnus Liebherr and Andrew W. Corcoran and Phillip M. Alday and Scott Coussens and Valeria Bellan and Caitlin A. Howlett and Maarten A. Immink and Mark Kohler and Matthias Schlesewsky and Ina Bornkessel-Schlesewsky},
  doi = {10.18112/openneuro.ds003620.v1.1.1},
  url = {https://doi.org/10.18112/openneuro.ds003620.v1.1.1},
}

About This Dataset#

Overview

This dataset contains raw and pre-processed EEG data from a mobile EEG study investigating the effects of cognitive task demands, motor demands, and environmental complexity on attentional processing (see below for experiment details).

All preprocessing and analysis code is deposited in the code directory. The entire MATLAB pipeline can be reproduced by executing the run_pipeline.m script. In order to run these scripts, you will need to ensure you have the required MATLAB toolboxes and R packages on your system. You will also need to adapt def_local.m to specify local paths to MATLAB and EEGLAB. Descriptive statistics and mixed-effects models can be reproduced in R by running the stat_analysis.R script.

See below for software details.

View full README

Overview

This dataset contains raw and pre-processed EEG data from a mobile EEG study investigating the effects of cognitive task demands, motor demands, and environmental complexity on attentional processing (see below for experiment details).

All preprocessing and analysis code is deposited in the code directory. The entire MATLAB pipeline can be reproduced by executing the run_pipeline.m script. In order to run these scripts, you will need to ensure you have the required MATLAB toolboxes and R packages on your system. You will also need to adapt def_local.m to specify local paths to MATLAB and EEGLAB. Descriptive statistics and mixed-effects models can be reproduced in R by running the stat_analysis.R script.

See below for software details.

Citing this dataset

In addition to citing this dataset, please cite the original manuscript reporting data collection and experimental procedures. For more information, see the dataset_description.json file.

License

ODC Open Database License (ODbL). For more information, see the LICENCE file.

Format

Dataset is formatted according to the EEG-BIDS extension (Pernet et al., 2019) and the BIDS extension proposal for common electrophysiological derivatives (BEP021) v0.0.1, which can be found here:

https://docs.google.com/document/d/1PmcVs7vg7Th-cGC-UrX8rAhKUHIzOI-uIOh69_mvdlw/edit#heading=h.mqkmyp254xh6

Note that BEP021 is still a work in progress as of 2021-03-01.

Generally, you can find data in the .tsv files and descriptions in the accompanying .json files.

An important BIDS definition to consider is the “Inheritance Principle” (see 3.5 in the BIDS specification: http://bids.neuroimaging.io/bids_spec.pdf), which states:

Any metadata file (.json, .bvec, .tsv, etc.) may be defined at any directory level. The values from the top level are inherited by all lower levels unless they are overridden by a file at the lower level.

Details about the experiment

Forty-four healthy adults aged 18-40 performed an oddball task involving complex tone (piano and horn) stimuli in three settings: (1) sitting in a quiet room in the lab (LAB); (2) walking around a sports field (FIELD); (3) navigating a route through a university campus (CAMPUS).

Participants performed each environmental condition twice: once while attending to oddball stimuli (i.e. counting the number of presented deviant tones; COUNT), and once while disregarding or ignoring the tone stimuli (IGNORE).

EEG signals were recorded from 32 active electrodes using a Brain Vision LiveAmp 32 amplifier. See manuscript for further details.

MATLAB software details

MATLAB Version: 9.7.0.1319299 (R2019b) Update 5 MATLAB License Number: 678256 Operating System: Microsoft Windows 10 Enterprise Version 10.0 (Build 18363) Java Version: Java 1.8.0_202-b08 with Oracle Corporation Java HotSpot(TM) 64-Bit Server VM mixed mode

MATLAB (v9.7)
Simulink (v10.0)
Curve Fitting Toolbox (v3.5.10)
DSP System Toolbox (v9.9)
Image Processing Toolbox (v11.0)
MATLAB Compiler (v7.1)
MATLAB Compiler SDK (v6.7)
Parallel Computing Toolbox (v7.1)
Signal Processing Toolbox (v8.3)
Statistics and Machine Learning Toolbox (v11.6)
Symbolic Math Toolbox (v8.4)
Wavelet Toolbox (v5.3)

The following toolboxes/helper functions were also used:

EEGLAB (v2019.1)
ERPLAB (v8.10)
ICLabel (v1.3)
clean_rawdata (v2.3)
bids-matlab-tools (v5.2)
dipfit (v3.4)
firfilt (v2.4)
export_fig (v3.12)
ColorBrewer (v3.1.0)

R software details

R version 3.6.2 (2019-12-12)

Platform: x86_64-w64-mingw32/x64 (64-bit)

locale: _LC_COLLATE=English_Australia.1252_, _LC_CTYPE=English_Australia.1252_, _LC_MONETARY=English_Australia.1252_, _LC_NUMERIC=C_ and _LC_TIME=English_Australia.1252_

attached base packages:

stats
graphics
grDevices
utils
datasets
methods
base

other attached packages:

sjPlot(v.2.8.7)
emmeans(v.1.5.1)
car(v.3.0-10)
carData(v.3.0-4)
lme4(v.1.1-23)
Matrix(v.1.2-18)
data.table(v.1.13.0)
forcats(v.0.5.0)
stringr(v.1.4.0)
dplyr(v.1.0.2)
purrr(v.0.3.4)
readr(v.1.4.0)
tidyr(v.1.1.2)
tibble(v.3.0.4)
ggplot2(v.3.3.2)
tidyverse(v.1.3.0)

loaded via a namespace (and not attached):

nlme(v.3.1-149)
pbkrtest(v.0.4-8.6)
fs(v.1.5.0)
lubridate(v.1.7.9)
insight(v.0.12.0)
httr(v.1.4.2)
numDeriv(v.2016.8-1.1)
tools(v.3.6.2)
backports(v.1.1.10)
utf8(v.1.1.4)
R6(v.2.4.1)
sjlabelled(v.1.1.7)
DBI(v.1.1.0)
colorspace(v.1.4-1)
withr(v.2.3.0)
tidyselect(v.1.1.0)
curl(v.4.3)
compiler(v.3.6.2)
performance(v.0.5.0)
cli(v.2.1.0)
rvest(v.0.3.6)
xml2(v.1.3.2)
sandwich(v.3.0-0)
labeling(v.0.3)
bayestestR(v.0.7.2)
scales(v.1.1.1)
mvtnorm(v.1.1-1)
digest(v.0.6.25)
foreign(v.0.8-76)
minqa(v.1.2.4)
rio(v.0.5.16)
pkgconfig(v.2.0.3)
dbplyr(v.1.4.4)
rlang(v.0.4.8)
readxl(v.1.3.1)
rstudioapi(v.0.11)
farver(v.2.0.3)
generics(v.0.0.2)
zoo(v.1.8-8)
jsonlite(v.1.7.1)
zip(v.2.1.1)
magrittr(v.1.5)
parameters(v.0.8.6)
Rcpp(v.1.0.5)
munsell(v.0.5.0)
fansi(v.0.4.1)
abind(v.1.4-5)
lifecycle(v.0.2.0)
stringi(v.1.4.6)
multcomp(v.1.4-14)
MASS(v.7.3-53)
plyr(v.1.8.6)
grid(v.3.6.2)
blob(v.1.2.1)
parallel(v.3.6.2)
sjmisc(v.2.8.6)
crayon(v.1.3.4)
lattice(v.0.20-41)
ggeffects(v.0.16.0)
haven(v.2.3.1)
splines(v.3.6.2)
pander(v.0.6.3)
sjstats(v.0.18.1)
hms(v.0.5.3)
knitr(v.1.30)
pillar(v.1.4.6)
boot(v.1.3-25)
estimability(v.1.3)
effectsize(v.0.3.3)
codetools(v.0.2-16)
reprex(v.0.3.0)
glue(v.1.4.2)
modelr(v.0.1.8)
vctrs(v.0.3.4)
nloptr(v.1.2.2.2)
cellranger(v.1.1.0)
gtable(v.0.3.0)
assertthat(v.0.2.1)
xfun(v.0.18)
openxlsx(v.4.2.2)
xtable(v.1.8-4)
broom(v.0.7.1)
coda(v.0.19-4)
survival(v.3.2-7)
lmerTest(v.3.1-3)
statmod(v.1.4.34)
TH.data(v.1.0-10)
ellipsis(v.0.3.1)

Dataset Information#

Dataset ID	`DS003620`
Title	Runabout: A mobile EEG study of auditory oddball processing in laboratory and real-world conditions
Year	2021
Authors	Magnus Liebherr, Andrew W. Corcoran, Phillip M. Alday, Scott Coussens, Valeria Bellan, Caitlin A. Howlett, Maarten A. Immink, Mark Kohler, Matthias Schlesewsky, Ina Bornkessel-Schlesewsky
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds003620.v1.1.1
Source links	OpenNeuro \| NeMAR \| Source URL

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

Subjects: 44
Recordings: 370
Tasks: 1

Channels & sampling rate

Channels: 32 (110), 35 (45)
Sampling rate (Hz): 500.0
Duration (hours): 0.0

API Reference#

Use the DS003620 class to access this dataset programmatically.

class eegdash.dataset.DS003620(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003620. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 44; recordings: 100; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir#

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query#

Merged query with the dataset filter applied.

Type:: dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003620 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003620

Examples

>>> from eegdash.dataset import DS003620
>>> dataset = DS003620(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

__init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

save(path, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

path (str or Path) – Destination file path.
overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None