{"dataset":{"id":"52079","dataset_id":"on002885","name":"DBS Phantom Recordings","description":"This dataset comprises magnetoencephalography (MEG) recordings from a CTF phantom used to evaluate and compare deep brain stimulation (DBS) artifact rejection methods. The phantom recordings serve as a controlled reference for assessing the comparative performance of various artifact rejection techniques in MEG data contaminated by DBS-related artifacts, providing a standardized benchmark for methodological validation.","owner_user_id":15,"status":"active","github_repo":"nemarDatasets/on002885","concept_doi":"10.82901/nemar.on002885","latest_version_doi":"10.82901/nemar.on002885.v1.0.0","created_at":"2026-06-21 13:09:42","updated_at":"2026-07-10 22:12:41","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"enriched\",\n  \"title\": \"DBS Phantom Recordings\",\n  \"description\": \"This dataset comprises magnetoencephalography (MEG) recordings from a CTF phantom used to evaluate and compare deep brain stimulation (DBS) artifact rejection methods. The phantom recordings serve as a controlled reference for assessing the comparative performance of various artifact rejection techniques in MEG data contaminated by DBS-related artifacts, providing a standardized benchmark for methodological validation.\",\n  \"methods_description\": \"MEG phantom recordings were acquired using a CTF system. Stimulation reference signal was captured with EEG001, movement trigger with UPPT001, and dipole activity with HADC006. The dataset includes measurements designed to evaluate DBS artifact rejection methods.\",\n  \"license\": \"CC0\",\n  \"dataset_type\": \"raw\",\n  \"authors\": {\n    \"Ahmet Levent Kandemir\": {},\n    \"Vladimir Litvak\": {},\n    \"Esther Florin\": {\n      \"orcid\": \"0000-0001-8276-2508\"\n    }\n  },\n  \"keywords\": [\n    {\n      \"term\": \"MEG\"\n    },\n    {\n      \"term\": \"deep brain stimulation\"\n    },\n    {\n      \"term\": \"artifact rejection\"\n    },\n    {\n      \"term\": \"phantom recordings\"\n    },\n    {\n      \"term\": \"signal processing\"\n    },\n    {\n      \"term\": \"CTF phantom\"\n    },\n    {\n      \"term\": \"DBS artifact\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.1016/j.neuroimage.2020.117057.\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/on002885\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataexplorer/detail?dataset_id=on002885\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.1016/j.neuroimage.2020.117057\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds002885.v1.0.1\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"Volkswagen Foundation\",\n      \"award_number\": \"89387\",\n      \"award_title\": \"Lichtenberg program\"\n    },\n    {\n      \"funder_name\": \"Wellcome\",\n      \"award_number\": \"203147/Z/16/Z\"\n    }\n  ],\n  \"resource_type_specific\": \"MEG Dataset\",\n  \"modalities\": [\n    \"meg\"\n  ],\n  \"sizes\": [\n    \"21.5 GB (64 files)\"\n  ],\n  \"formats\": [\n    \".acq\",\n    \".bak\",\n    \".cls\",\n    \".de\",\n    \".ds/BadChannels\",\n    \".eeg\",\n    \".fif\",\n    \".gitattributes\",\n    \".hc\",\n    \".hist\",\n    \".infods\",\n    \".json\",\n    \".md\",\n    \".meg4\",\n    \".mrk\",\n    \".newds\",\n    \".res4\",\n    \".segments\",\n    \".tsv\",\n    \".txt\",\n    \".yml\"\n  ],\n  \"source_hash\": \"4bfecc50ad548a9a873442f4938c198d03a22695c04eee9c8345e0514c19faad\"\n}","last_activity_at":"2026-06-21 13:09:42","source":"openneuro","source_id":"ds002885","subject_count":2,"modalities":"meg","age_min":null,"age_max":null,"file_size":21543182414,"total_files":120,"tasks":"DMW,DSMW,EmptyRoom,Reference","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Ahmet Levent Kandemir, Vladimir Litvak, Esther Florin","license":"CC0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on002885-blue)](https://doi.org/10.82901/nemar.on002885)\n\nThis dataset is a part of the data used for the study: 'Kandemir, A.L., Litvak, V., Florin, E., 2020. The comparative performance of DBS artefact rejection methods for MEG recordings, NeuroImage, 2020, https://doi.org/10.1016/j.neuroimage.2020.117057.'\n\r\n\r\nPlease use the latest version of the dataset.\n \r\n\r\nFor detailed information about measurement protocol please refer to https://doi.org/10.1016/j.neuroimage.2020.117057. Additional information about CTF Phantom measurement is provided below. \r\nThe customized Matlab code for artefact rejection methods is available at:  https://gitlab.com/lkandemir/dbs-artefact-rejection.\r\n\r\n--------------\r\nCTF Phantom Measurement \r\nStimulation reference signal is captured with EEG001\r\nMovement trigger is captured with UPPT001\r\nDipole activity is captured with HADC006\r\n\r\n","bids_version":"1.2","sessions_count":null,"publish_date":null,"embedding_dirty":0,"license_tier":"public","zarr_status":"ready","zarr_converted_at":"2026-09-08 02:10:02","zarr_store_count":2,"zarr_index_etag":"7b3a1adb22b7e2b88cd88557e796d44e","zarr_source_commit":"9cdb0b653d2633d041e6d6ac3196ec3e88c59a07","archive_status":"ready","archive_size":11116296052,"archive_retry_count":0,"records_status":"ready","archive_skip_reason":null,"zarr_errors":5,"zarr_failure_count":1,"zarr_deterministic":0,"zarr_failed_at":"2026-09-08 02:10:02","num_dataset_citations":0,"num_datapaper_citations":36,"n_channels":null,"electrode_system":null,"has_hed":0,"hed_version":null,"is_exemplar":0,"bytes_present":21542950278,"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":238.5,"recording_duration_min":117.5,"recording_duration_max":121,"recording_count":7,"recordings_unavailable":5,"recordings_measured":2,"channel_count_min":314,"channel_count_max":314,"sampling_frequency":null,"power_line_frequency":null,"eeg_reference":null,"placement_scheme":null,"sweep_stamps":"{\"enrichment_updated_at\":\"2026-06-21 13:24:34\",\"metadata_updated_at\":\"2026-06-21 13:24:34\",\"archive_checked_at\":\"2026-06-21 13:30:15\",\"zarr_checked_at\":null,\"records_checked_at\":\"2026-06-21 13:21:07\",\"citations_updated_at\":\"2026-09-08 03:00:48\",\"channel_montage_checked_at\":null,\"hed_checked_at\":\"2026-06-30 04:40:09\",\"data_checked_at\":\"2026-07-30 03:00:57\",\"availability_report_at\":\"2026-07-23 01:12:56\",\"signal_defaults_at\":\"2026-09-02 11:57:16\",\"recording_stats_at\":\"2026-09-08 03:00:53\"}","participants":2,"num_citations":36,"latest_version":"v1.0.0","zarr_verify_status":null,"zarr_verified_at":null,"owner_username":"nemarAdmin","owner_github":"nemarAdmin","file_size_formatted":"20.06 GB","zarr_data_failures":{"count":1,"detail_ref":"zarr/index.json","pending":4,"discovered":7},"zarr_index_url":"https://zarr.nemar.org/on002885/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}}