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

### 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)