{"success":true,"database":"eegdash","data":{"_id":"6953f4239276ef1ee07a32dc","dataset_id":"ds003620","associated_paper_doi":null,"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"],"bids_version":"1.4","contact_info":["Andrew W Corcoran","Scott Coussens"],"contributing_labs":null,"data_processed":true,"dataset_doi":"doi:10.18112/openneuro.ds003620.v1.1.1","datatypes":["eeg"],"demographics":{"subjects_count":44,"ages":[23,25,21,21,39,19,26,19,21,24,22,22,22,30,30,18,22,39,24,22,18,28,30,34,23,21,23,22,18,27,22,32,22,19,19,18,19,21,19,24,22,18,19,19],"age_min":18,"age_max":39,"age_mean":23.318181818181817,"species":null,"sex_distribution":{"f":29,"m":15},"handedness_distribution":{"r":40,"l":1}},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds003620","osf_url":null,"github_url":null,"paper_url":null},"funding":["FT160100437"],"ingestion_fingerprint":"c30e6da92ca0e2483c23cff2f2151a9d0911d4e6d51511189c712436946616bd","license":"CC0","n_contributing_labs":null,"name":"Runabout: A mobile EEG study of auditory oddball processing in laboratory and real-world conditions","readme":"### Overview\nThis 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).\nAll 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.\nSee below for software details.\n### Citing this dataset\nIn addition to citing this dataset, please cite the original manuscript reporting data collection and experimental procedures.\nFor more information, see the `dataset_description.json` file.\n### License\nODC Open Database License (ODbL). For more information, see the `LICENCE` file.\n### Format\nDataset 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:\nhttps://docs.google.com/document/d/1PmcVs7vg7Th-cGC-UrX8rAhKUHIzOI-uIOh69_mvdlw/edit#heading=h.mqkmyp254xh6\nNote that BEP021 is still a work in progress as of 2021-03-01.\nGenerally, you can find data in the .tsv files and descriptions in the\naccompanying .json files.\nAn 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:\n> 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.\n### Details about the experiment\nForty-four healthy adults aged 18-40 performed an oddball task involving complex tone (piano and horn) stimuli in three settings:\n(1) sitting in a quiet room in the lab (LAB);\n(2) walking around a sports field (FIELD);\n(3) navigating a route through a university campus (CAMPUS).\nParticipants 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).\nEEG signals were recorded from 32 active electrodes using a Brain Vision LiveAmp 32 amplifier. See manuscript for further details.\n### MATLAB software details\nMATLAB Version: 9.7.0.1319299 (R2019b) Update 5\nMATLAB License Number: 678256\nOperating System: Microsoft Windows 10 Enterprise Version 10.0 (Build 18363)\nJava Version: Java 1.8.0_202-b08 with Oracle Corporation Java HotSpot(TM) 64-Bit Server VM mixed mode\n* MATLAB (v9.7)\n* Simulink (v10.0)\n* Curve Fitting Toolbox (v3.5.10)\n* DSP System Toolbox (v9.9)\n* Image Processing Toolbox (v11.0)\n* MATLAB Compiler (v7.1)\n* MATLAB Compiler SDK (v6.7)\n* Parallel Computing Toolbox (v7.1)\n* Signal Processing Toolbox (v8.3)\n* Statistics and Machine Learning Toolbox (v11.6)\n* Symbolic Math Toolbox (v8.4)\n* Wavelet Toolbox (v5.3)\n**The following toolboxes/helper functions were also used:**\n* EEGLAB (v2019.1)\n* ERPLAB (v8.10)\n* ICLabel (v1.3)\n* clean_rawdata (v2.3)\n* bids-matlab-tools (v5.2)\n* dipfit (v3.4)\n* firfilt (v2.4)\n* export_fig (v3.12)\n* ColorBrewer (v3.1.0)\n### R software details\n**R version 3.6.2 (2019-12-12)**\n**Platform:** x86_64-w64-mingw32/x64 (64-bit)\n**locale:**\n_LC_COLLATE=English_Australia.1252_, _LC_CTYPE=English_Australia.1252_, _LC_MONETARY=English_Australia.1252_, _LC_NUMERIC=C_ and _LC_TIME=English_Australia.1252_\n**attached base packages:**\n* stats\n* graphics\n* grDevices\n* utils\n* datasets\n* methods\n* base\n**other attached packages:**\n* sjPlot(v.2.8.7)\n* emmeans(v.1.5.1)\n* car(v.3.0-10)\n* carData(v.3.0-4)\n* lme4(v.1.1-23)\n* Matrix(v.1.2-18)\n* data.table(v.1.13.0)\n* forcats(v.0.5.0)\n* stringr(v.1.4.0)\n* dplyr(v.1.0.2)\n* purrr(v.0.3.4)\n* readr(v.1.4.0)\n* tidyr(v.1.1.2)\n* tibble(v.3.0.4)\n* ggplot2(v.3.3.2)\n* tidyverse(v.1.3.0)\n**loaded via a namespace (and not attached):**\n* nlme(v.3.1-149)\n* pbkrtest(v.0.4-8.6)\n* fs(v.1.5.0)\n* lubridate(v.1.7.9)\n* insight(v.0.12.0)\n* httr(v.1.4.2)\n* numDeriv(v.2016.8-1.1)\n* tools(v.3.6.2)\n* backports(v.1.1.10)\n* utf8(v.1.1.4)\n* R6(v.2.4.1)\n* sjlabelled(v.1.1.7)\n* DBI(v.1.1.0)\n* colorspace(v.1.4-1)\n* withr(v.2.3.0)\n* tidyselect(v.1.1.0)\n* curl(v.4.3)\n* compiler(v.3.6.2)\n* performance(v.0.5.0)\n* cli(v.2.1.0)\n* rvest(v.0.3.6)\n* xml2(v.1.3.2)\n* sandwich(v.3.0-0)\n* labeling(v.0.3)\n* bayestestR(v.0.7.2)\n* scales(v.1.1.1)\n* mvtnorm(v.1.1-1)\n* digest(v.0.6.25)\n* foreign(v.0.8-76)\n* minqa(v.1.2.4)\n* rio(v.0.5.16)\n* pkgconfig(v.2.0.3)\n* dbplyr(v.1.4.4)\n* rlang(v.0.4.8)\n* readxl(v.1.3.1)\n* rstudioapi(v.0.11)\n* farver(v.2.0.3)\n* generics(v.0.0.2)\n* zoo(v.1.8-8)\n* jsonlite(v.1.7.1)\n* zip(v.2.1.1)\n* magrittr(v.1.5)\n* parameters(v.0.8.6)\n* Rcpp(v.1.0.5)\n* munsell(v.0.5.0)\n* fansi(v.0.4.1)\n* abind(v.1.4-5)\n* lifecycle(v.0.2.0)\n* stringi(v.1.4.6)\n* multcomp(v.1.4-14)\n* MASS(v.7.3-53)\n* plyr(v.1.8.6)\n* grid(v.3.6.2)\n* blob(v.1.2.1)\n* parallel(v.3.6.2)\n* sjmisc(v.2.8.6)\n* crayon(v.1.3.4)\n* lattice(v.0.20-41)\n* ggeffects(v.0.16.0)\n* haven(v.2.3.1)\n* splines(v.3.6.2)\n* pander(v.0.6.3)\n* sjstats(v.0.18.1)\n* hms(v.0.5.3)\n* knitr(v.1.30)\n* pillar(v.1.4.6)\n* boot(v.1.3-25)\n* estimability(v.1.3)\n* effectsize(v.0.3.3)\n* codetools(v.0.2-16)\n* reprex(v.0.3.0)\n* glue(v.1.4.2)\n* modelr(v.0.1.8)\n* vctrs(v.0.3.4)\n* nloptr(v.1.2.2.2)\n* cellranger(v.1.1.0)\n* gtable(v.0.3.0)\n* assertthat(v.0.2.1)\n* xfun(v.0.18)\n* openxlsx(v.4.2.2)\n* xtable(v.1.8-4)\n* broom(v.0.7.1)\n* coda(v.0.19-4)\n* survival(v.3.2-7)\n* lmerTest(v.3.1-3)\n* statmod(v.1.4.34)\n* TH.data(v.1.0-10)\n* ellipsis(v.0.3.1)","recording_modality":["eeg"],"senior_author":"Ina Bornkessel-Schlesewsky","sessions":[],"size_bytes":18299512036,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["oddball"],"timestamps":{"digested_at":"2026-04-22T12:25:35.933767+00:00","dataset_created_at":"2021-04-15T18:46:27.142Z","dataset_modified_at":"2021-11-09T19:10:35.000Z"},"total_files":100,"storage":{"backend":"s3","base":"s3://openneuro.org/ds003620","raw_key":"dataset_description.json","dep_keys":["CHANGES","LICENSE","README","participants.json","participants.tsv"]},"tagger_meta":{"config_hash":"4a051be509a0e3d0","metadata_hash":"6a25026908a372a4","model":"openai/gpt-5.2","tagged_at":"2026-01-20T10:17:18.037530+00:00"},"tags":{"pathology":["Healthy"],"modality":["Auditory"],"type":["Attention"],"confidence":{"pathology":0.9,"modality":0.9,"type":0.85},"reasoning":{"few_shot_analysis":"Most similar few-shot paradigms are the Oddball tasks. Example: the Parkinson's “Cross-modal Oddball Task” shows that oddball paradigms are labeled with modality based on stimulus channels (visual+auditory => Multisensory) and type can reflect cognitive control/clinical focus when the cohort is clinical. Example: “EEG: DPX Cog Ctl Task in Acute Mild TBI” demonstrates that tasks explicitly framed around cognitive control/attention are labeled Type=Attention. These conventions guide mapping this dataset’s oddball/count-vs-ignore design to Type=Attention and Modality=Auditory, while Pathology follows the explicitly stated healthy cohort.","metadata_analysis":"Key population facts: \"Forty-four healthy adults aged 18-40\" and participants are described as \"healthy adults\".\nKey task/stimulus facts: \"performed an oddball task involving complex tone (piano and horn) stimuli\" and \"counting the number of presented deviant tones\" with an IGNORE condition: \"disregarding or ignoring the tone stimuli\".\nKey study aim/construct: the study investigates \"effects of cognitive task demands, motor demands, and environmental complexity on attentional processing\".","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology: Metadata says participants are \"Forty-four healthy adults\". Few-shot pattern: when a dataset explicitly states a clinical group (e.g., Parkinson's, TBI), Pathology reflects that; otherwise Healthy. ALIGN.\nModality: Metadata says \"oddball task involving complex tone (piano and horn) stimuli\" and \"deviant tones\" (auditory). Few-shot pattern: modality is based on stimulus channel (e.g., cross-modal oddball => Multisensory; auditory oddball => Auditory). ALIGN.\nType: Metadata explicitly frames the purpose as \"attentional processing\" and includes attend/count vs ignore manipulation. Few-shot pattern: cognitive control/attention-demanding paradigms (e.g., DPX cognitive control) are labeled Attention; oddball paradigms used to probe attention also map naturally to Attention when the stated construct is attention. ALIGN.","decision_summary":"Pathology top-2: (1) Healthy — supported by \"Forty-four healthy adults aged 18-40\" and repeated use of \"healthy\"; (2) Unknown — would apply only if recruitment status were unclear. Winner: Healthy (explicit recruitment statement). Confidence=0.9 based on clear explicit population statement.\nModality top-2: (1) Auditory — \"complex tone (piano and horn) stimuli\", \"deviant tones\"; (2) Multisensory — possible because participants also walked/navigated, but these are motor/environmental demands rather than stimulus modality. Winner: Auditory. Confidence=0.9 based on explicit auditory stimulus description.\nType top-2: (1) Attention — explicitly \"attentional processing\" plus attend/count vs ignore manipulation; (2) Perception — could be argued due to tone discrimination/oddball detection, but the stated goal centers on attention under varying demands. Winner: Attention. Confidence=0.85 based on explicit construct statement plus task manipulation."}},"nemar_citation_count":4,"computed_title":"Runabout: A mobile EEG study of auditory oddball processing in laboratory and real-world conditions","nchans_counts":[{"val":35,"count":100}],"sfreq_counts":[{"val":500.0,"count":100}],"stats_computed_at":"2026-04-22T23:16:00.222272+00:00","total_duration_s":214332.886,"canonical_name":null,"name_confidence":0.93,"name_meta":{"suggested_at":"2026-04-14T10:18:35.342Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"canonical","author_year":"Liebherr2021"}}