{"dataset":{"id":"58523","dataset_id":"on004706","name":"Spatial memory and non-invasive closed-loop stimulus timing","description":"This dataset contains behavioral events and electrophysiological recordings from a hybrid spatial-navigation and free recall experiment conducted at the University of Pennsylvania (2021-2022). Participants performed a virtual courier task delivering items across a town, followed by recall testing. The experiment comprised two phases: read-only sessions for generating classifier training data, and closed-loop sessions where stimulus presentation timing was optimized based on real-time neural predictions of memory encoding. The dataset supports investigation of spatial memory dynamics and the efficacy of classifier-based closed-loop stimulation for memory enhancement.","owner_user_id":15,"status":"active","github_repo":"nemarDatasets/on004706","concept_doi":"10.82901/nemar.on004706","latest_version_doi":"10.82901/nemar.on004706.v1.0.0","created_at":"2026-06-25 08:31:56","updated_at":"2026-07-10 22:37:38","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"Spatial memory and non-invasive closed-loop stimulus timing\",\n  \"description\": \"This dataset contains behavioral events and electrophysiological recordings from a hybrid spatial-navigation and free recall experiment conducted at the University of Pennsylvania (2021-2022). Participants performed a virtual courier task delivering items across a town, followed by recall testing. The experiment comprised two phases: read-only sessions for generating classifier training data, and closed-loop sessions where stimulus presentation timing was optimized based on real-time neural predictions of memory encoding. The dataset supports investigation of spatial memory dynamics and the efficacy of classifier-based closed-loop stimulation for memory enhancement.\",\n  \"methods_description\": \"The experiment employed a hybrid spatial-navigation and free recall paradigm in which subjects delivered items to stores in a virtual town and subsequently recalled their deliveries. Read-only sessions collected training data without classifier-based timing manipulation. Closed-loop sessions used classifier models to predict recall performance and timed stimulus presentation to coincide with predicted good or bad memory encoding states.\",\n  \"license\": \"CC0\",\n  \"dataset_type\": \"raw\",\n  \"authors\": {\n    \"Joseph H. Rudoler\": {},\n    \"Matthew R. Dougherty\": {},\n    \"Brandon S. Katerman\": {},\n    \"James P. Bruska\": {},\n    \"Woohyeuk Chang\": {},\n    \"David J. Halpern\": {\n      \"orcid\": \"0000-0002-7490-5692\"\n    },\n    \"Nicholas B. Diamond\": {},\n    \"Michael J. Kahana\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"Spatial Memory\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D065852\"\n    },\n    {\n      \"term\": \"episodic memory\"\n    },\n    {\n      \"term\": \"closed-loop stimulation\"\n    },\n    {\n      \"term\": \"neural decoding\"\n    },\n    {\n      \"term\": \"memory encoding\"\n    },\n    {\n      \"term\": \"Electrophysiology\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D004594\"\n    },\n    {\n      \"term\": \"virtual reality\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/on004706\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataexplorer/detail?dataset_id=on004706\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.1101/2022.11.30.518606\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds004706.v1.0.0\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"U.S. Army Medical Research and Development Command\",\n      \"award_number\": \"MTEC-20-06-MOM-013\",\n      \"award_title\": \"Restoring memory with task-independent semi-chronic closed-loop direct brain stimulation and non-invasive closed-loop stimulus timing optimization\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"Neuroimaging Dataset\",\n  \"modalities\": [\n    \"beh\",\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"1.4 TB (301 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".json\",\n    \".md\",\n    \".tsv\",\n    \".txt\",\n    \".yml\"\n  ],\n  \"source_hash\": \"acd21f31f7a9f6b2b0ea1fc992d5caa90233c9960ca8938eeb8af6b9e3547320\"\n}","last_activity_at":"2026-06-25 08:31:56","source":"openneuro","source_id":"ds004706","subject_count":34,"modalities":"beh,eeg","age_min":null,"age_max":null,"file_size":1426144567177,"total_files":2715,"tasks":"NiclsCourierClosedLoop,NiclsCourierReadOnly","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Joseph H. Rudoler, Matthew R. Dougherty, Brandon S. Katerman, James P. Bruska, Woohyeuk Chang, David J. Halpern, Nicholas B. Diamond, Michael J. Kahana","license":"CC0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on004706-blue)](https://doi.org/10.82901/nemar.on004706)\n\nThis dataset contains behavioral events and electrophysiological recordings from an experiment run in the Computational Memory Lab at the University of Pennsylvania from 2021-2022 with funding from U.S. Army Medical Research and Development Command (USAMRDC) through the Medical Technology Enterprise Consortium (MTEC) project MTEC-20-06-MOM-013, \"Restoring memory with task-independent semi-chronic closed-loop direct brain stimulation and non-invasive closed-loop stimulus timing optimization\". This experiment constitutes the non-invasive portion of the project, which targeted memory improvement through classifier-based stimulus presentation. \n\nThe experiment is a hybrid spatial-navigation and free recall paradigm in which subjects play the role of a courier delivering items to stores across a virtual town, and are subsequently asked to recall their deliveries. There are two phases - \"read-only\" and \"closed-loop\". In read-only sessions, there is no classifier-based timing manipulation and participants simply perform the task in order to generate training data for the models used in subsequent closed-loop sessions. After collecting sufficient training data, classifier models predict recall in closed-loop sessions and the stimulus presentation is timed to coincide with predicted good or bad memory encoding.   \n\nTwo publications are based on this experiment:\n[\"Neural correlates of memory in an immersive spatiotemporal context\"](https://www.biorxiv.org/content/10.1101/2022.11.30.518606) studies the navigation and memory dynamics in read-only sessions, and \"Optimizing learning via real-time neural decoding\" (link pending) explores the results of the closed-loop manipulation.\n\nNote: memory dynamics in closed-loop sessions are potentially influenced by the closed-loop timing manipulation, and so may be biased in a way that precludes them from analyses of general mnemonic function. The read-only sessions, however, were not subject to this manipulation and therefore can be used for studying spatial and episodic memory (as in the first paper mentioned above).\n","bids_version":"1.6.0","sessions_count":9,"publish_date":null,"embedding_dirty":0,"license_tier":"public","zarr_status":"ready","zarr_converted_at":"2026-08-13 22:10:02","zarr_store_count":298,"zarr_index_etag":"f13061bd76d641fecb966b3a54f06850","zarr_source_commit":"70f99cd2a064518b90337971524b02eb1a345ce6","archive_status":null,"archive_size":null,"archive_retry_count":0,"records_status":"ready","archive_skip_reason":"dataset 1328.2 GB exceeds 100.0 GB archive limit; use direct download","zarr_errors":0,"zarr_failure_count":0,"zarr_deterministic":0,"zarr_failed_at":null,"num_dataset_citations":1,"num_datapaper_citations":4,"n_channels":128,"electrode_system":"biosemi","has_hed":0,"hed_version":null,"is_exemplar":0,"bytes_present":1426050963160,"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":1694182,"recording_duration_min":5,"recording_duration_max":9410,"recording_count":298,"recordings_unavailable":0,"recordings_measured":298,"channel_count_min":137,"channel_count_max":137,"sampling_frequency":2048,"power_line_frequency":60,"eeg_reference":null,"placement_scheme":null,"sweep_stamps":"{\"enrichment_updated_at\":\"2026-06-25 09:05:40\",\"metadata_updated_at\":\"2026-06-25 09:05:51\",\"archive_checked_at\":\"2026-06-25 09:06:28\",\"zarr_checked_at\":null,\"records_checked_at\":\"2026-06-25 09:07:06\",\"citations_updated_at\":\"2026-09-08 03:00:50\",\"channel_montage_checked_at\":\"2026-06-28 23:32:28\",\"hed_checked_at\":\"2026-06-30 05:05:18\",\"data_checked_at\":\"2026-08-14 03:00:45\",\"availability_report_at\":\"2026-07-23 01:20:34\",\"recording_stats_at\":\"2026-09-02 11:32:55\",\"signal_defaults_at\":\"2026-09-02 12:19:37\"}","participants":34,"num_citations":5,"latest_version":"v1.0.0","zarr_verify_status":null,"zarr_verified_at":null,"owner_username":"nemarAdmin","owner_github":"nemarAdmin","file_size_formatted":"1.30 TB","zarr_data_failures":null,"zarr_index_url":"https://zarr.nemar.org/on004706/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}}