{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a3371","dataset_id":"ds004624","associated_paper_doi":null,"authors":["F. Mivalt","F. Lampert","M.A. van den Boom","P. Brunner","J. Kim","Andrea Duque-lopez","M. Krakorova","V. Kremen","D. Hermes","G.A. Worrell","K. J. Miller"],"bids_version":"Brain Imaging Data Structure Specification v1.8.0","contact_info":["Filip Mivalt"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds004624.v2.0.0","datatypes":["ieeg"],"demographics":{"subjects_count":3,"ages":[],"age_min":null,"age_max":null,"age_mean":null,"species":null,"sex_distribution":{"f":2,"m":1},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds004624","osf_url":null,"github_url":null,"paper_url":null},"funding":["NIH U01NS128612"],"ingestion_fingerprint":"92df58cf9a616be039ae342981a8daf82838a2984af9a9002b5a5413849d2ac8","license":"CC0","n_contributing_labs":null,"name":"Intracranial recordings using BCI2000 and the CorTec BrainInterchange","readme":"An Ecosystem of Technology and Protocols for Adaptive Neuromodulation Research in Humans\nThis study aims to develop an ecosystem for the purpose of neurmodulation using the Cortec BCI device and BCI2000 software.\nContact: For questions regarding this dataset, please contact\nmivalt.filip@mayo.edu or Miller.Kai@mayo.edu\nFunding: NIH U01NS128612","recording_modality":["ieeg"],"senior_author":"K. J. Miller","sessions":["Bseps","Closedloop","Functional","Intraopdata","Longterm","Noisefloor","Sleep"],"size_bytes":20739089774,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["39db","45db","51db","57db","Alpha","Auditory","Awake","Beta","Broadband","Lhpc2ma001","Lhpc3ma001","Lhpc4ma001","Passive","Rant2ma001","Rant3ma001","Rant4ma001","Rant5ma001","Recording","Rhpc2ma001","Rhpc3ma001","Rhpc4ma001","Sensorimotor","Sleep","Socialreinforcement","Somatosensory","Stimulation","Visual","rec"],"timestamps":{"digested_at":"2026-04-21T23:07:22.230244+00:00","dataset_created_at":"2023-06-30T19:55:34.648Z","dataset_modified_at":"2025-06-14T03:31:56.000Z"},"total_files":614,"storage":{"backend":"s3","base":"s3://openneuro.org/ds004624","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv"]},"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"5ba03a88b6292499","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Surgery"],"modality":["Multisensory"],"type":["Clinical/Intervention"],"confidence":{"pathology":0.6,"modality":0.8,"type":0.8},"reasoning":{"few_shot_analysis":"Closest convention matches in the few-shot set are the clinically oriented datasets labeled as Type=Clinical/Intervention (e.g., the Parkinson’s cross-modal oddball task and the pediatric epilepsy HFO dataset). Those examples show that when the dataset’s primary aim is to support/assess clinically relevant neurophysiology/biomarkers or intervention-like protocols (rather than a single cognitive construct), the catalog convention is to label Type as \"Clinical/Intervention\". For Modality, the Parkinson’s cross-modal oddball example illustrates using \"Multisensory\" when both auditory and visual inputs are core parts of the paradigm; here the task list likewise includes multiple stimulus channels (auditory, visual, somatosensory/sensorimotor). No few-shot example directly covers implanted intracranial BCI/neuromodulation ecosystems, so Pathology must rely primarily on metadata/inference.","metadata_analysis":"Key quoted metadata facts:\n1) Title indicates invasive recordings: \"Intracranial recordings using BCI2000 and the CorTec BrainInterchange\".\n2) Study aim is neuromodulation: \"This study aims to develop an ecosystem for the purpose of neurmodulation using the Cortec BCI device and BCI2000 software.\" \n3) Very small implanted cohort: \"Subjects: 3\".\n4) Task list spans multiple sensory domains and stimulation: tasks include \"Auditory\", \"Visual\", \"Somatosensory\", \"Sensorimotor\", and \"Stimulation\" (also \"Socialreinforcement\", \"Passive\", \"Sleep\", \"Awake\").\nThese collectively suggest a neuromodulation/BCI protocol dataset with multiple stimulus modalities rather than a single sensory paradigm.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n- Metadata says (quoted): \"Intracranial recordings\" and the device is an implanted neuromodulation/BCI system (\"CorTec BrainInterchange\"), but it does NOT explicitly name a recruiting diagnosis (e.g., epilepsy, Parkinson’s).\n- Few-shot pattern suggests: invasive/clinical biomarker datasets often have explicit diagnoses (e.g., \"epilepsy\", \"Parkinsons disease\"); however, that pattern cannot supply missing FACTS.\n- Alignment: PARTIAL (clinical context implied, diagnosis not stated). Therefore, pathology cannot be set to Epilepsy/Parkinson’s without an explicit statement; the best fit is \"Surgery\" as an inference from implantation/intracranial setting.\n\nModality:\n- Metadata says (quoted): task names include \"Auditory\", \"Visual\", \"Somatosensory\", \"Sensorimotor\".\n- Few-shot pattern suggests: when both auditory and visual cues are present (cross-modal oddball), label \"Multisensory\".\n- Alignment: ALIGNS. This dataset appears to include multiple sensory channels, so \"Multisensory\" is the best single label.\n\nType:\n- Metadata says (quoted): \"develop an ecosystem for the purpose of neurmodulation\" and tasks include \"Stimulation\".\n- Few-shot pattern suggests: clinically motivated protocol/biomarker/intervention datasets are labeled \"Clinical/Intervention\" (e.g., Parkinson’s cognitive dysfunction study; epilepsy HFO biomarker dataset).\n- Alignment: ALIGNS. The primary purpose is neuromodulation research infrastructure/protocols rather than a single cognitive construct (e.g., memory/attention).","decision_summary":"Top-2 candidates and selection:\n\nPathology:\n1) Surgery — Evidence: (i) \"Intracranial recordings\" implies implanted/invasive recording context; (ii) device named \"CorTec BrainInterchange\" used for \"neurmodulation\" implies an implanted system; (iii) very small N (\"Subjects: 3\") consistent with early implanted/surgical cohorts. Alignment: partial (inferred, not explicitly stated).\n2) Unknown — Evidence: no explicit diagnosis/clinical condition in provided metadata.\nDecision: Surgery (wins because implantation/intracranial neuromodulation strongly implies a surgical cohort even though the diagnosis is unstated). Confidence limited because no explicit diagnosis statement.\n\nModality:\n1) Multisensory — Evidence: tasks explicitly list multiple sensory channels: \"Auditory\", \"Visual\", \"Somatosensory\"/\"Sensorimotor\". Alignment: aligns with few-shot cross-modal convention.\n2) Other — Evidence: dataset also includes \"Passive\", \"Sleep\", \"Awake\", \"Recording\" which are not stimulus modalities and could indicate heterogeneous sessions.\nDecision: Multisensory (stronger because multiple explicit sensory modalities are named).\n\nType:\n1) Clinical/Intervention — Evidence: (i) \"ecosystem\" for \"neurmodulation\"; (ii) task list includes \"Stimulation\"; (iii) invasive intracranial setup suggests intervention-oriented protocol development.\n2) Other — Evidence: heterogeneous task set could be considered a methods/platform dataset rather than a single cognitive domain.\nDecision: Clinical/Intervention (primary aim is neuromodulation research platform/protocols). Confidence moderate because metadata doesn’t describe specific clinical outcomes, but does explicitly emphasize neuromodulation/stimulation."}},"nemar_citation_count":0,"computed_title":"Intracranial recordings using BCI2000 and the CorTec BrainInterchange","nchans_counts":[{"val":36,"count":363},{"val":34,"count":234},{"val":39,"count":17}],"sfreq_counts":[{"val":1000.0,"count":614}],"stats_computed_at":"2026-04-21T23:17:03.730295+00:00","total_duration_s":null,"canonical_name":null,"name_confidence":0.69,"name_meta":{"suggested_at":"2026-04-14T10:18:35.343Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"canonical","author_year":"Mivalt2025"}}