{"dataset":{"id":"47572","dataset_id":"nm000263","name":"Visual object ERP EEG dataset (Kaneshiro et al. 2015)","description":"A high-density 124-channel EEG dataset comprising event-related potentials (ERPs) from 10 healthy participants during a visual object recognition task. Participants viewed 5,184 photographs from six object categories (human body, human face, animal body, animal face, fruit/vegetable, and inanimate objects), with 72 photographs per category, presented for 500 ms each. The dataset is suitable for investigating neural representations of object categories through single-trial EEG classification and representational similarity analysis.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000263","concept_doi":"10.82901/nemar.nm000263","latest_version_doi":"10.82901/nemar.nm000263.v1.0.3","created_at":"2026-06-19 23:22:49","updated_at":"2026-08-18 18:20:06","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"enriched\",\n  \"title\": \"Visual object ERP EEG dataset (Kaneshiro et al. 2015)\",\n  \"description\": \"A high-density 124-channel EEG dataset comprising event-related potentials (ERPs) from 10 healthy participants during a visual object recognition task. Participants viewed 5,184 photographs from six object categories (human body, human face, animal body, animal face, fruit/vegetable, and inanimate objects), with 72 photographs per category, presented for 500 ms each. The dataset is suitable for investigating neural representations of object categories through single-trial EEG classification and representational similarity analysis.\",\n  \"methods_description\": \"EEG data were acquired from 10 healthy participants using a 124-channel EGI Net Amps 300 system with HydroCel Geodesic Sensor Net montage (GSN-HydroCel-128) at a sampling rate of 62.5 Hz with average reference. Participants viewed 5,184 trials comprising 72 photographs from each of six object categories presented for 500 ms each with a 750 ms interstimulus interval.\",\n  \"license\": \"CC-BY-3.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Blair Kaneshiro\": {},\n    \"Marcos Perreau Guimaraes\": {},\n    \"Hyung-Suk Kim\": {},\n    \"Anthony M. Norcia\": {},\n    \"Patrick Suppes\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"event-related potentials\"\n    },\n    {\n      \"term\": \"visual object recognition\"\n    },\n    {\n      \"term\": \"single-trial classification\"\n    },\n    {\n      \"term\": \"representational similarity analysis\"\n    },\n    {\n      \"term\": \"P300\"\n    },\n    {\n      \"term\": \"Healthy\"\n    },\n    {\n      \"term\": \"Visual ERP\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.1371/journal.pone.0135697\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000263\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.21105/joss.01896\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.1038/s41597-019-0104-8\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/nm000263\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"Not specified\"\n    },\n    {\n      \"funder_name\": \"National Institutes of Health\",\n      \"award_number\": \"R01-MH087450\"\n    },\n    {\n      \"funder_name\": \"National Science Foundation\",\n      \"award_number\": \"BCS-1228261\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"3.7 GB (21 files)\"\n  ],\n  \"formats\": [\n    \".edf\",\n    \".json\",\n    \".mat\",\n    \".md\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"828c0ee2ed2b589419d5e3e6f67acf4102b863a7756dea50d28b252f3601e1fd\"\n}","last_activity_at":"2026-08-16 13:36:47","source":null,"source_id":null,"subject_count":10,"modalities":"eeg","age_min":null,"age_max":null,"file_size":3652595587,"total_files":131,"tasks":"p300","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Blair Kaneshiro, Marcos Perreau Guimaraes, Hyung-Suk Kim, Anthony M. Norcia, Patrick Suppes","license":"CC-BY-3.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000263-blue)](https://doi.org/10.82901/nemar.nm000263)\n\n# Visual object ERP EEG dataset (Kaneshiro et al. 2015)\n\n## Overview\n\nA visual event-related potential (ERP) dataset comprising 124-channel EEG recordings from 10 healthy participants viewing photographs from six object categories (human body, human face, animal body, animal face, fruit/vegetable, and inanimate objects). The dataset contains 5,184 trials collected during a visual object recognition task with 72 images per category, each presented for 500 ms with a 750 ms interstimulus interval. Data were acquired at 62.5 Hz using a high-density geodesic sensor net and are suitable for investigating neural representations of object categories through single-trial EEG classification and representational similarity analysis.\n\n## Dataset Summary\n\n| Property | Value |\n|---|---|\n| Subjects | 10 |\n| Channels | 124 |\n| Classes | 6 |\n| Trial length | 0.5 s |\n| Sampling frequency | 62.5 Hz |\n| Sessions | 1 |\n| Total trials | 5184 |\n| Paradigm | P300 |\n\n## Data Collection Methods\n\nEEG data were acquired from 10 healthy participants using a 124-channel EGI Net Amps 300 system with HydroCel Geodesic Sensor Net montage (GSN-HydroCel-128) at a sampling rate of 62.5 Hz with average reference. Participants viewed 72 photographs from six object categories presented for 500 ms each with a 750 ms interstimulus interval. Data were preprocessed and organized in BIDS format.\n\n## How to Access via MOABB\n\nInstall MOABB and load this dataset directly:\n\n```python\nfrom moabb.datasets import Kaneshiro2015\nfrom moabb.paradigms import P300\nparadigm = P300()\n\ndataset = Kaneshiro2015()\nX, y, metadata = paradigm.get_data(dataset)\n```\n\nFor more details see the [MOABB documentation](https://moabb.neurotechx.com/) and the\n[MOABB dataset page](https://moabb.neurotechx.com/docs/generated/moabb.datasets.Kaneshiro2015.html).\n\n## Citation\n\nIf you use this dataset please cite the primary publication:\n\n> DOI: [10.1371/journal.pone.0135697](https://doi.org/10.1371/journal.pone.0135697)\n\n## NEMAR / MOABB Benchmark Collection\n\nThis BIDS-formatted dataset was converted from the original data using the\n[MOABB](https://moabb.neurotechx.com/) pipeline and re-hosted on\n[NEMAR](https://nemar.org/) as part of the MOABB benchmark collection.\nThe original data and license terms apply — see `dataset_description.json` for details.\n","bids_version":"1.9.0","sessions_count":1,"publish_date":null,"embedding_dirty":0,"license_tier":"attribution","zarr_status":"ready","zarr_converted_at":"2026-08-22 07:36:53","zarr_store_count":10,"zarr_index_etag":"fe99efe9d0bda3aac893cce6c8aa06ab","zarr_source_commit":"ccd7b8e9cc3bd23689a1ee97e6c3f5ae95e16a72","archive_status":"ready","archive_size":3468706408,"archive_retry_count":0,"records_status":"ready","archive_skip_reason":null,"zarr_errors":0,"zarr_failure_count":0,"zarr_deterministic":0,"zarr_failed_at":null,"num_dataset_citations":0,"num_datapaper_citations":93,"n_channels":124,"electrode_system":"egi-geodesic","has_hed":1,"hed_version":"8.4.0","is_exemplar":0,"bytes_present":3650487785,"data_complete":1,"withdrawn_at":null,"withdrawn_reason":null,"archive_complete":1,"archive_absent_files":0,"archive_declared_files":131,"zarr_pool_breaks":null,"total_recording_duration":31777,"recording_duration_min":3177,"recording_duration_max":3179,"recording_count":10,"recordings_unavailable":0,"recordings_measured":10,"channel_count_min":124,"channel_count_max":124,"sampling_frequency":62.5,"power_line_frequency":60,"eeg_reference":"average","placement_scheme":"Geodesic Sensor Net 128","sweep_stamps":"{\"enrichment_updated_at\":\"2026-08-18 18:16:57\",\"metadata_updated_at\":\"2026-08-18 18:20:05\",\"archive_checked_at\":\"2026-08-18 18:25:56\",\"zarr_checked_at\":null,\"records_checked_at\":\"2026-08-18 18:24:06\",\"citations_updated_at\":\"2026-09-08 03:00:46\",\"channel_montage_checked_at\":\"2026-06-28 23:01:47\",\"hed_checked_at\":\"2026-06-30 04:32:01\",\"data_checked_at\":null,\"availability_report_at\":\"2026-08-22 03:01:15\",\"recording_stats_at\":\"2026-09-02 11:32:06\",\"signal_defaults_at\":\"2026-09-02 11:49:52\"}","participants":10,"num_citations":93,"latest_version":"v1.0.3","zarr_verify_status":null,"zarr_verified_at":null,"owner_username":"bruaristimunha","owner_github":"bruAristimunha","file_size_formatted":"3.40 GB","zarr_data_failures":null,"zarr_index_url":"https://zarr.nemar.org/nm000263/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}}