{"success":true,"database":"eegdash","data":{"_id":"6953f4239276ef1ee07a32b5","dataset_id":"ds002885","associated_paper_doi":"10.1016/j.neuroimage.2020.117057","authors":["Ahmet Levent Kandemir","Vladimir Litvak","Esther Florin"],"bids_version":"1.2","contact_info":null,"contributing_labs":null,"data_processed":false,"dataset_doi":"10.18112/openneuro.ds002885.v1.0.1","datatypes":["meg"],"demographics":{"subjects_count":2,"ages":[],"age_min":null,"age_max":null,"age_mean":null,"species":null,"sex_distribution":null,"handedness_distribution":null},"experimental_modalities":null,"external_links":{"paper_url":"https://doi.org/10.1016/j.neuroimage.2020.117057"},"funding":["Volkswagen Foundation (Lichtenberg program 89387)"],"ingestion_fingerprint":"098baf79add1219bed0d37cc1d5d4cb7469046de9749608fb76ff3e1704cc59a","license":"CC0","n_contributing_labs":null,"name":"DBS Phantom Recordings","readme":"This 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.'\nPlease use the latest version of the dataset.\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.\nThe customized Matlab code for artefact rejection methods is available at:  https://gitlab.com/lkandemir/dbs-artefact-rejection.\n--------------\nCTF Phantom Measurement\nStimulation reference signal is captured with EEG001\nMovement trigger is captured with UPPT001\nDipole activity is captured with HADC006","recording_modality":["meg"],"senior_author":null,"sessions":[],"size_bytes":21543252582,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["DMW","DSMW","EmptyRoom","Reference"],"timestamps":{"digested_at":"2026-05-31T16:12:22.781869+00:00","dataset_created_at":null,"dataset_modified_at":null},"total_files":7,"storage":{"backend":"s3","base":"s3://openneuro.org/ds002885","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.tsv"]},"tagger_meta":{"config_hash":"4a051be509a0e3d0","metadata_hash":"bd60318788d25063","model":"openai/gpt-5.2","tagged_at":"2026-01-20T10:12:12.560263+00:00"},"tags":{"pathology":["Other"],"modality":["Other"],"type":["Other"],"confidence":{"pathology":0.65,"modality":0.7,"type":0.8},"reasoning":{"few_shot_analysis":"Closest few-shot by PURPOSE is the 'Phantom EEG Dataset with Motion, Muscle, and Eye Artifacts...' example (phantom/non-human recording used to validate artifact handling). That example shows the convention that phantom/mechanical validation datasets are typically not treated as a clinical cohort and are labeled with a non-clinical pathology (they used 'Healthy'), and that method/validation-focused datasets may not map cleanly onto cognitive Types. However, unlike that example, the present metadata explicitly describes a CTF phantom MEG measurement for DBS artifact rejection, with no human participants/tasks described, making 'Other' a better fit than 'Healthy' for Pathology under the catalog rule that Pathology reflects recruited clinical population.","metadata_analysis":"Key quoted facts:\n1) Dataset purpose/method focus: \"The comparative performance of DBS artefact rejection methods for MEG recordings\".\n2) Non-human/phantom context: \"CTF Phantom Measurement\".\n3) Signals are technical channels rather than participant stimuli/tasks: \"Stimulation reference signal is captured with EEG001\" and \"Movement trigger is captured with UPPT001\" and \"Dipole activity is captured with HADC006\".\nThese indicate a methodological phantom recording rather than a human sensory/cognitive experiment.","paper_abstract_analysis":"No useful paper information (only a citation/DOI is provided in metadata; no abstract text included).","evidence_alignment_check":"Pathology: Metadata says this is a \"CTF Phantom Measurement\" (no recruited patient group) and focuses on MEG DBS artifact rejection methods; few-shot phantom example suggests using a non-clinical label (they used 'Healthy'). ALIGN in being non-clinical, but differs because this dataset appears to be phantom-only (not a healthy human cohort). Decision: choose 'Other' because it is not a recruited clinical population nor clearly a healthy participant cohort.\n\nModality: Metadata says nothing about sensory stimuli; it describes technical recordings/signals (\"Stimulation reference signal\", \"Movement trigger\", \"Dipole activity\"). Few-shot examples map modality to stimulus channel (auditory/visual/tactile/etc.), but here there is no stimulus modality. ALIGN: lack of sensory paradigm. Decision: 'Other' modality.\n\nType: Metadata explicitly frames the dataset as evaluating \"DBS artefact rejection methods\" and provides code for artifact rejection; no cognitive construct/task is described. Few-shot phantom example is also methods/artifact-centric and does not reflect a cognitive task. ALIGN: methodological/technical purpose. Decision: 'Other' type.","decision_summary":"Top-2 candidates and selection:\n\nPathology:\n- Other: Supported by \"CTF Phantom Measurement\" and absence of any recruited clinical/healthy participant description; signals described are technical channels.\n- Healthy: Possible by convention for non-clinical datasets (as in the few-shot phantom example).\nHead-to-head: 'Other' wins because metadata indicates phantom-only measurement rather than a human healthy cohort. Confidence limited by sparse metadata.\n\nModality:\n- Other: Supported by lack of sensory stimuli and purely technical channel descriptions (\"Stimulation reference signal...\", \"Movement trigger...\", \"Dipole activity...\").\n- Unknown: Also plausible if modality cannot be inferred.\nHead-to-head: 'Other' wins because we can positively infer this is not a standard sensory modality paradigm but a technical phantom/stimulation setup.\n\nType:\n- Other: Strongly supported by \"comparative performance of DBS artefact rejection methods\" and provided artifact-rejection code link.\n- Clinical/Intervention: Could be considered because DBS relates to clinical therapy, but the dataset described here is phantom validation, not an intervention trial.\nHead-to-head: 'Other' wins because the explicit aim is methodological artifact rejection evaluation, not patient treatment/clinical outcomes."}},"nemar_citation_count":1,"computed_title":"DBS Phantom Recordings","nchans_counts":[{"val":306,"count":4},{"val":314,"count":3}],"sfreq_counts":[{"val":19200.0,"count":4},{"val":3000.0,"count":3}],"stats_computed_at":"2026-05-31T19:34:32.517381+00:00","total_duration_s":1438.5,"author_year":"Kandemir2020","canonical_name":null,"bad_channels_info":null,"acknowledgements":"EF gratefully acknowledges support by the Volkswagen Foundation (Lichtenberg program 89387). The Wellcome Centre for Human Neuroimaging is supported by core funding from the Wellcome (203147/Z/16/Z). We thank David Bradbury, Peter Aston, Alphonso Reid and Daniel Bates for technical assistance with the phantom recording. The authors would like to thank Johannes Pfeifer, Jan Hirschmann, and Markus Butz for their critical review of the manuscript and fruitful discussions.","how_to_acknowledge":"Please acknowledge the authors and cite the following reference: Ahmet Levent Kandemir, Vladimir Litvak, Esther Florin, The comparative performance of DBS artefact rejection methods for MEG recordings, NeuroImage, 2020, 117057, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2020.117057.","references_and_links":["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."],"associated_paper_meta":{"channel":"text/how_to_acknowledge","confidence":"high","author_overlap":3,"is_oa":true,"oa_status":"gold","source":"paper_resolver"}}}