{"dataset":{"id":"61219","dataset_id":"on007162","name":"Adaptive recruitment of cortex-wide recurrence for visual object recognition (EEG)","description":"This dataset contains EEG recordings from 34 participants collected to investigate the adaptive recruitment of cortex-wide recurrent processing during visual object recognition. Participants viewed a stimulus set of 242 images, comprising 'challenge' and 'control' images selected based on discrepancies between human behavioral performance and AlexNet classification, while performing a rapid serial visual presentation task with a paper-clip detection component. The dataset includes derivatives with time-resolved decoding accuracy matrices for object identity, supporting analyses of recurrent cortical dynamics in visual processing.","owner_user_id":15,"status":"active","github_repo":"nemarDatasets/on007162","concept_doi":"10.82901/nemar.on007162","latest_version_doi":"10.82901/nemar.on007162.v1.0.0","created_at":"2026-06-30 01:31:35","updated_at":"2026-08-19 00:02:01","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"Adaptive recruitment of cortex-wide recurrence for visual object recognition (EEG)\",\n  \"description\": \"This dataset contains EEG recordings from 34 participants collected to investigate the adaptive recruitment of cortex-wide recurrent processing during visual object recognition. Participants viewed a stimulus set of 242 images, comprising 'challenge' and 'control' images selected based on discrepancies between human behavioral performance and AlexNet classification, while performing a rapid serial visual presentation task with a paper-clip detection component. The dataset includes derivatives with time-resolved decoding accuracy matrices for object identity, supporting analyses of recurrent cortical dynamics in visual processing.\",\n  \"methods_description\": \"Each trial presented a single image for 200 ms followed by a 100 ms blank interval. Trials were grouped into sequences of 14 images, and at the end of each sequence participants reported whether a paper clip had appeared anywhere within it. Stimuli consisted of 242 images (121 'challenge' and 121 'control') selected based on divergence between human behavioral performance and AlexNet model predictions.\",\n  \"license\": \"CC0\",\n  \"dataset_type\": \"raw\",\n  \"authors\": {\n    \"[Unspecified1]\": {},\n    \"[Unspecified2]\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"visual object recognition\"\n    },\n    {\n      \"term\": \"recurrent processing\"\n    },\n    {\n      \"term\": \"decoding analysis\"\n    },\n    {\n      \"term\": \"AlexNet\"\n    },\n    {\n      \"term\": \"Pattern Recognition, Visual\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D010364\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/on007162\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/on007162\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.1101/2025.10.17.682937\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsSupplementTo\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds007162\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsVersionOf\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds007162.v1.0.0\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"64.0 GB (307 files)\"\n  ],\n  \"formats\": [\n    \".eeg\",\n    \".json\",\n    \".md\",\n    \".npz\",\n    \".tsv\",\n    \".vhdr\",\n    \".vmrk\",\n    \".yml\"\n  ],\n  \"source_hash\": \"db60e53d5d5cb8b7188a841d51db315382e616feebef5e79f88345f99d47e3ae\"\n}","last_activity_at":"2026-06-30 01:31:35","source":"openneuro","source_id":"ds007162","subject_count":34,"modalities":"eeg","age_min":null,"age_max":null,"file_size":65680314545,"total_files":662,"tasks":"rcor","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"[Unspecified1], [Unspecified2]","license":"CC0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on007162-blue)](https://doi.org/10.82901/nemar.on007162)\n\n# Dataset Description\n\nThis dataset contains the EEG data accompanying the study \n**\"Adaptive recruitment of cortex-wide recurrence for visual object recognition\"** (Link to preprint: https://www.biorxiv.org/content/10.1101/2025.10.17.682937v2).\nPlease cite the above paper if you use this data.\n\n---\n\n## Dataset Overview\n- 34 participants, each with 1 session  \n\n---\n\n## Experimental Design\nThe EEG experiment used a stimulus set of 242 images (121 “challenge” and 121 “control” images) derived from comparisons between human behavioural performance and AlexNet.  \n\n- **Main task:** Each trial consisted of a single image presented for 200 ms followed by a 100 ms blank. Trials were grouped into sequences of 14 images. At the end of each sequence, participants reported whether a paper clip appeared anywhere in that sequence.\n\n---\n\n## Derivatives\nThe derivatives/ folder contains outputs from the decoding analyses, including time-resolved decoding accuracy matrices for object identity.","bids_version":"1.7.0","sessions_count":1,"publish_date":null,"embedding_dirty":0,"license_tier":"public","zarr_status":"ready","zarr_converted_at":"2026-08-23 06:30:11","zarr_store_count":69,"zarr_index_etag":"41f492974c6d55d76ce3f8a607686ea2","zarr_source_commit":"68db160795f44bc0226c73ee1bc039ac629d9117","archive_status":"ready","archive_size":50345356050,"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":1,"n_channels":63,"electrode_system":"10-10","has_hed":0,"hed_version":null,"is_exemplar":0,"bytes_present":64005825048,"data_complete":1,"withdrawn_at":null,"withdrawn_reason":null,"archive_complete":null,"archive_absent_files":null,"archive_declared_files":null,"zarr_pool_breaks":null,"total_recording_duration":258539.0400000001,"recording_duration_min":323.88,"recording_duration_max":5456.94,"recording_count":69,"recordings_unavailable":0,"recordings_measured":69,"channel_count_min":63,"channel_count_max":63,"sampling_frequency":1000,"power_line_frequency":null,"eeg_reference":null,"placement_scheme":"based on the extended 10/20 system","sweep_stamps":"{\"enrichment_updated_at\":\"2026-08-19 00:01:43\",\"metadata_updated_at\":\"2026-08-19 00:01:57\",\"archive_checked_at\":\"2026-06-30 02:20:14\",\"zarr_checked_at\":null,\"records_checked_at\":\"2026-06-30 01:43:51\",\"citations_updated_at\":\"2026-09-08 03:00:53\",\"channel_montage_checked_at\":null,\"hed_checked_at\":\"2026-06-30 05:42:34\",\"data_checked_at\":\"2026-09-01 03:00:45\",\"availability_report_at\":\"2026-07-23 01:31:37\",\"recording_stats_at\":\"2026-09-02 11:33:55\",\"signal_defaults_at\":\"2026-09-02 12:52:38\"}","participants":34,"num_citations":1,"latest_version":"v1.0.0","zarr_verify_status":null,"zarr_verified_at":null,"owner_username":"nemarAdmin","owner_github":"nemarAdmin","file_size_formatted":"61.17 GB","zarr_data_failures":null,"zarr_index_url":"https://zarr.nemar.org/on007162/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}}