{"dataset":{"id":"53729","dataset_id":"on003620","name":"Runabout: A mobile EEG study of auditory oddball processing in laboratory and real-world conditions","description":"This dataset comprises mobile EEG recordings from 44 healthy adults performing an auditory oddball task across three environmental conditions: laboratory, outdoor field, and campus navigation. Participants attended to or ignored complex tone stimuli (piano and horn) while 32-channel EEG was recorded using a Brain Vision LiveAmp amplifier. The study investigates how cognitive task demands, motor demands, and environmental complexity modulate attentional processing and event-related potentials during naturalistic behavior.","owner_user_id":15,"status":"active","github_repo":"nemarDatasets/on003620","concept_doi":"10.82901/nemar.on003620","latest_version_doi":"10.82901/nemar.on003620.v1.0.0","created_at":"2026-06-22 07:01:31","updated_at":"2026-07-10 22:17:35","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"Runabout: A mobile EEG study of auditory oddball processing in laboratory and real-world conditions\",\n  \"description\": \"This dataset comprises mobile EEG recordings from 44 healthy adults performing an auditory oddball task across three environmental conditions: laboratory, outdoor field, and campus navigation. Participants attended to or ignored complex tone stimuli (piano and horn) while 32-channel EEG was recorded using a Brain Vision LiveAmp amplifier. The study investigates how cognitive task demands, motor demands, and environmental complexity modulate attentional processing and event-related potentials during naturalistic behavior.\",\n  \"methods_description\": \"EEG signals were recorded from 32 active electrodes using a Brain Vision LiveAmp 32 amplifier. Participants performed an oddball task with complex tone stimuli in three settings: sitting in a quiet laboratory, walking around a sports field, and navigating a university campus route. Each environmental condition was performed twice: once while counting deviant tones (COUNT) and once while ignoring stimuli (IGNORE). Data were preprocessed using EEGLAB with ICA-based artifact removal, bandpass filtering, and epoch extraction.\",\n  \"license\": \"CC0\",\n  \"dataset_type\": \"raw\",\n  \"authors\": {\n    \"Magnus Liebherr\": {\n      \"orcid\": \"0000-0001-8580-2464\"\n    },\n    \"Andrew W. Corcoran\": {\n      \"orcid\": \"0000-0002-0449-4883\"\n    },\n    \"Phillip M. Alday\": {\n      \"orcid\": \"0000-0002-9984-5745\"\n    },\n    \"Scott Coussens\": {\n      \"orcid\": \"0000-0002-3564-5859\"\n    },\n    \"Valeria Bellan\": {\n      \"orcid\": \"0000-0002-8512-5288\"\n    },\n    \"Caitlin A. Howlett\": {\n      \"orcid\": \"0000-0002-4584-8641\"\n    },\n    \"Maarten A. Immink\": {\n      \"orcid\": \"0000-0002-4652-0090\"\n    },\n    \"Mark Kohler\": {\n      \"orcid\": \"0000-0001-7265-6242\"\n    },\n    \"Matthias Schlesewsky\": {\n      \"orcid\": \"0000-0003-4131-8077\"\n    },\n    \"Ina Bornkessel-Schlesewsky\": {\n      \"orcid\": \"0000-0002-3238-6492\"\n    }\n  },\n  \"keywords\": [\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"auditory oddball\"\n    },\n    {\n      \"term\": \"attention\"\n    },\n    {\n      \"term\": \"mobile neuroimaging\"\n    },\n    {\n      \"term\": \"event-related potentials\"\n    },\n    {\n      \"term\": \"naturalistic environments\"\n    },\n    {\n      \"term\": \"motor demands\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/on003620\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataexplorer/detail?dataset_id=on003620\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.1038/s41598-021-01772-8\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds003620.v1.1.1\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"Australian Research Council\",\n      \"award_number\": \"FT160100437\"\n    },\n    {\n      \"funder_name\": \"University of South Australia\",\n      \"award_title\": \"Research Themes Investment Scheme\"\n    }\n  ],\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"75.2 GB (676 files)\"\n  ],\n  \"formats\": [\n    \".R\",\n    \".csv\",\n    \".eeg\",\n    \".erp\",\n    \".gitattributes\",\n    \".json\",\n    \".m\",\n    \".md\",\n    \".png\",\n    \".rds\",\n    \".set\",\n    \".tsv\",\n    \".txt\",\n    \".vhdr\",\n    \".vmrk\",\n    \".wav\",\n    \".yml\"\n  ],\n  \"source_hash\": \"d43e606d643af3332dc2e292c2106535164c9e2fe9a7d021fec6eb88e7f9a819\"\n}","last_activity_at":"2026-06-22 07:01:31","source":"openneuro","source_id":"ds003620","subject_count":44,"modalities":"eeg","age_min":18,"age_max":39,"file_size":95807761920,"total_files":1889,"tasks":"oddball","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":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","license":"CC0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on003620-blue)](https://doi.org/10.82901/nemar.on003620)\n\n### Overview\r\n\r\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).\r\n\r\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.\r\n\r\nSee below for software details.\r\n\r\n### Citing this dataset\r\n\r\nIn addition to citing this dataset, please cite the original manuscript reporting data collection and experimental procedures.\r\nFor more information, see the `dataset_description.json` file.\r\n\r\n### License\r\n\r\nODC Open Database License (ODbL). For more information, see the `LICENCE` file.\r\n\r\n### Format\r\n\r\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:\r\n\r\nhttps://docs.google.com/document/d/1PmcVs7vg7Th-cGC-UrX8rAhKUHIzOI-uIOh69_mvdlw/edit#heading=h.mqkmyp254xh6\r\n\r\nNote that BEP021 is still a work in progress as of 2021-03-01.\r\n\r\nGenerally, you can find data in the .tsv files and descriptions in the\r\naccompanying .json files.\r\n\r\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:\r\n\r\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.\r\n\r\n### Details about the experiment\r\n\r\nForty-four healthy adults aged 18-40 performed an oddball task involving complex tone (piano and horn) stimuli in three settings: \r\n(1) sitting in a quiet room in the lab (LAB);\r\n(2) walking around a sports field (FIELD);\r\n(3) navigating a route through a university campus (CAMPUS).\r\n\r\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).\r\n\r\nEEG signals were recorded from 32 active electrodes using a Brain Vision LiveAmp 32 amplifier. See manuscript for further details.\r\n\r\n### MATLAB software details\r\n\r\nMATLAB Version: 9.7.0.1319299 (R2019b) Update 5\r\nMATLAB License Number: 678256\r\nOperating System: Microsoft Windows 10 Enterprise Version 10.0 (Build 18363)\r\nJava Version: Java 1.8.0_202-b08 with Oracle Corporation Java HotSpot(TM) 64-Bit Server VM mixed mode\r\n\r\n* MATLAB (v9.7)\r\n* Simulink (v10.0)\r\n* Curve Fitting Toolbox (v3.5.10)\r\n* DSP System Toolbox (v9.9)\r\n* Image Processing Toolbox (v11.0)\r\n* MATLAB Compiler (v7.1)\r\n* MATLAB Compiler SDK (v6.7)\r\n* Parallel Computing Toolbox (v7.1)\r\n* Signal Processing Toolbox (v8.3)\r\n* Statistics and Machine Learning Toolbox (v11.6)\r\n* Symbolic Math Toolbox (v8.4)\r\n* Wavelet Toolbox (v5.3)\r\n\r\n**The following toolboxes/helper functions were also used:**\r\n\r\n* EEGLAB (v2019.1)\r\n* ERPLAB (v8.10)\r\n* ICLabel (v1.3)\r\n* clean_rawdata (v2.3)\r\n* bids-matlab-tools (v5.2)\r\n* dipfit (v3.4)\r\n* firfilt (v2.4)\r\n* export_fig (v3.12)\r\n* ColorBrewer (v3.1.0)\r\n\r\n### R software details\r\n\r\n**R version 3.6.2 (2019-12-12)**\r\n\r\n**Platform:** x86_64-w64-mingw32/x64 (64-bit) \r\n\r\n**locale:**\r\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_\r\n\r\n**attached base packages:** \r\n\r\n* stats \r\n* graphics \r\n* grDevices \r\n* utils \r\n* datasets \r\n* methods \r\n* base \r\n\r\n**other attached packages:** \r\n\r\n* sjPlot(v.2.8.7) \r\n* emmeans(v.1.5.1) \r\n* car(v.3.0-10) \r\n* carData(v.3.0-4) \r\n* lme4(v.1.1-23) \r\n* Matrix(v.1.2-18) \r\n* data.table(v.1.13.0) \r\n* forcats(v.0.5.0) \r\n* stringr(v.1.4.0) \r\n* dplyr(v.1.0.2) \r\n* purrr(v.0.3.4) \r\n* readr(v.1.4.0) \r\n* tidyr(v.1.1.2) \r\n* tibble(v.3.0.4) \r\n* ggplot2(v.3.3.2) \r\n* tidyverse(v.1.3.0) \r\n\r\n**loaded via a namespace (and not attached):** \r\n\r\n* nlme(v.3.1-149) \r\n* pbkrtest(v.0.4-8.6) \r\n* fs(v.1.5.0) \r\n* lubridate(v.1.7.9) \r\n* insight(v.0.12.0) \r\n* httr(v.1.4.2) \r\n* numDeriv(v.2016.8-1.1) \r\n* tools(v.3.6.2) \r\n* backports(v.1.1.10) \r\n* utf8(v.1.1.4) \r\n* R6(v.2.4.1) \r\n* sjlabelled(v.1.1.7) \r\n* DBI(v.1.1.0) \r\n* colorspace(v.1.4-1) \r\n* withr(v.2.3.0) \r\n* tidyselect(v.1.1.0) \r\n* curl(v.4.3) \r\n* compiler(v.3.6.2) \r\n* performance(v.0.5.0) \r\n* cli(v.2.1.0) \r\n* rvest(v.0.3.6) \r\n* xml2(v.1.3.2) \r\n* sandwich(v.3.0-0) \r\n* labeling(v.0.3) \r\n* bayestestR(v.0.7.2) \r\n* scales(v.1.1.1) \r\n* mvtnorm(v.1.1-1) \r\n* digest(v.0.6.25) \r\n* foreign(v.0.8-76) \r\n* minqa(v.1.2.4) \r\n* rio(v.0.5.16) \r\n* pkgconfig(v.2.0.3) \r\n* dbplyr(v.1.4.4) \r\n* rlang(v.0.4.8) \r\n* readxl(v.1.3.1) \r\n* rstudioapi(v.0.11) \r\n* farver(v.2.0.3) \r\n* generics(v.0.0.2) \r\n* zoo(v.1.8-8) \r\n* jsonlite(v.1.7.1) \r\n* zip(v.2.1.1) \r\n* magrittr(v.1.5) \r\n* parameters(v.0.8.6) \r\n* Rcpp(v.1.0.5) \r\n* munsell(v.0.5.0) \r\n* fansi(v.0.4.1) \r\n* abind(v.1.4-5) \r\n* lifecycle(v.0.2.0) \r\n* stringi(v.1.4.6) \r\n* multcomp(v.1.4-14) \r\n* MASS(v.7.3-53) \r\n* plyr(v.1.8.6) \r\n* grid(v.3.6.2) \r\n* blob(v.1.2.1) \r\n* parallel(v.3.6.2) \r\n* sjmisc(v.2.8.6) \r\n* crayon(v.1.3.4) \r\n* lattice(v.0.20-41) \r\n* ggeffects(v.0.16.0) \r\n* haven(v.2.3.1) \r\n* splines(v.3.6.2) \r\n* pander(v.0.6.3) \r\n* sjstats(v.0.18.1) \r\n* hms(v.0.5.3) \r\n* knitr(v.1.30) \r\n* pillar(v.1.4.6) \r\n* boot(v.1.3-25) \r\n* estimability(v.1.3) \r\n* effectsize(v.0.3.3) \r\n* codetools(v.0.2-16) \r\n* reprex(v.0.3.0) \r\n* glue(v.1.4.2) \r\n* modelr(v.0.1.8) \r\n* vctrs(v.0.3.4) \r\n* nloptr(v.1.2.2.2) \r\n* cellranger(v.1.1.0) \r\n* gtable(v.0.3.0) \r\n* assertthat(v.0.2.1) \r\n* xfun(v.0.18) \r\n* openxlsx(v.4.2.2) \r\n* xtable(v.1.8-4) \r\n* broom(v.0.7.1) \r\n* coda(v.0.19-4) \r\n* survival(v.3.2-7) \r\n* lmerTest(v.3.1-3) \r\n* statmod(v.1.4.34) \r\n* TH.data(v.1.0-10) \r\n* ellipsis(v.0.3.1) ","bids_version":"1.4","sessions_count":null,"publish_date":null,"embedding_dirty":0,"license_tier":"public","zarr_status":"ready","zarr_converted_at":"2026-09-08 11:55:59","zarr_store_count":1,"zarr_index_etag":"a14021bbd2fe5d67138fdb87a5596246","zarr_source_commit":"1310075275b091153dbe2c4f11b673b233d3a88f","archive_status":"ready","archive_size":77379233262,"archive_retry_count":0,"records_status":"failed","archive_skip_reason":null,"zarr_errors":55,"zarr_failure_count":0,"zarr_deterministic":0,"zarr_failed_at":"2026-09-08 11:55:59","num_dataset_citations":2,"num_datapaper_citations":30,"n_channels":35,"electrode_system":"10-10","has_hed":0,"hed_version":null,"is_exemplar":0,"bytes_present":75245370448,"data_complete":1,"withdrawn_at":null,"withdrawn_reason":null,"archive_complete":null,"archive_absent_files":null,"archive_declared_files":null,"zarr_pool_breaks":0,"total_recording_duration":837036.6780000007,"recording_duration_min":2172.264,"recording_duration_max":5894.344,"recording_count":308,"recordings_unavailable":41,"recordings_measured":264,"channel_count_min":3,"channel_count_max":35,"sampling_frequency":500,"power_line_frequency":50,"eeg_reference":"FCz","placement_scheme":null,"sweep_stamps":"{\"enrichment_updated_at\":\"2026-06-22 07:12:40\",\"metadata_updated_at\":\"2026-06-22 07:12:50\",\"archive_checked_at\":\"2026-06-22 08:03:35\",\"zarr_checked_at\":null,\"records_checked_at\":\"2026-06-22 07:13:03\",\"citations_updated_at\":\"2026-09-08 03:00:48\",\"channel_montage_checked_at\":\"2026-06-28 23:13:31\",\"hed_checked_at\":\"2026-06-30 04:45:04\",\"data_checked_at\":\"2026-08-01 03:01:13\",\"availability_report_at\":\"2026-07-23 01:14:32\",\"signal_defaults_at\":\"2026-09-02 12:01:16\"}","participants":44,"num_citations":32,"latest_version":"v1.0.0","zarr_verify_status":null,"zarr_verified_at":null,"owner_username":"nemarAdmin","owner_github":"nemarAdmin","file_size_formatted":"89.23 GB","zarr_data_failures":{"count":0,"detail_ref":"zarr/index.json","pending":55,"discovered":56},"zarr_index_url":"https://zarr.nemar.org/on003620/zarr/index.json","attestation_deposit_type":null,"attestation_key_status":null,"attestation_deidentified":null,"attestation_no_duplicate":null,"attestation_upstream_source":null,"attestation_accepted_at":null}}