{"success":true,"database":"eegdash","data":{"_id":"69d16e04897a7725c66f4c54","dataset_id":"ds007591","associated_paper_doi":null,"authors":["Motoshige Sato","Yasuo Kabe","Sensho Nobe","Akito Yoshida","Masakazu Inoue","Mayumi Shimizu","Kenichi Tomeoka","Shuntaro Sasai"],"bids_version":"1.9.0","contact_info":["Motoshige Sato"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds007591.v1.0.1","datatypes":["eeg"],"demographics":{"subjects_count":3,"ages":[],"age_min":null,"age_max":null,"age_mean":null,"species":null,"sex_distribution":null,"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds007591","osf_url":null,"github_url":null,"paper_url":null},"funding":["JST, Moonshot R&D Grant Number JPMJMS2012"],"ingestion_fingerprint":"ca0969d2737094040a4c293089706039909ada71ef6c0156fd62bcdfb8d637c2","license":"CC0","n_contributing_labs":null,"name":"Delineating neural contributions to EEG-based speech decoding","readme":"# Delineating neural contributions to EEG-based speech decoding\n## Overview\n128-channel EEG recordings during speech production tasks.\nParticipants produced one of 5 color words (green, magenta, orange, violet, yellow)\nunder three speech conditions: overt, minimally overt, and covert.\nEach trial consists of 5 repetitions of the same word (1.25 sec per repetition).\n## Channel layout (139 channels total)\n- Channels 1-128: EEG\n- Channels 129-130: DISPLAY (bipolar pair, misc)\n- Channels 131-132: MIC (bipolar pair, misc)\n- Channels 133-134: EOG (bipolar pair)\n- Channels 135-136: EMG upper orbicularis oris (bipolar pair)\n- Channels 137-138: EMG lower orbicularis oris (bipolar pair)\n- Channel 139: TRIGGER (marks trial onsets)\n## Session types\n- calibration: Offline data collection for decoder training\n- online: Real-time decoding with trained decoder\n## Preprocessing note\nThe EEG channels were recorded with a 10x preamp gain.\nRaw values have been converted to Volts (×1e-6).\n## Code\nCode for data loading, preprocessing, and decoding models is available at:\nhttps://github.com/arayabrain/uhd-gmail-public","recording_modality":["eeg"],"senior_author":"Shuntaro Sasai","sessions":["20230511","20230512","20230516","20230523","20230524","20230529"],"size_bytes":1737849881,"source":"openneuro","storage":{"backend":"s3","base":"s3://openneuro.org/ds007591","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","events.json","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["covert","minimallyovert","overt"],"timestamps":{"digested_at":"2026-04-22T12:30:33.261786+00:00","dataset_created_at":"2026-03-28T11:25:10.663Z","dataset_modified_at":"2026-03-31T19:35:29.000Z"},"total_files":21,"computed_title":"Delineating neural contributions to EEG-based speech decoding","nchans_counts":[{"val":139,"count":21}],"sfreq_counts":[{"val":256.0,"count":21}],"stats_computed_at":"2026-04-22T23:16:00.312972+00:00","total_duration_s":null,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"1660f1cfae4ba134","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Healthy"],"modality":["Unknown"],"type":["Motor"],"confidence":{"pathology":0.6,"modality":0.5,"type":0.75},"reasoning":{"few_shot_analysis":"Most similar few-shot convention is the “EEG Motor Movement/Imagery Dataset” example (Healthy + Visual + Motor). That example maps overt movement and motor imagery tasks to Type=“Motor” (even when cued by visual targets). This guides labeling here because the current dataset explicitly includes overt and covert (imagined) speech production, which is a motor/speech-action construct. However, unlike the few-shot motor example, this dataset’s metadata does not explicitly state the sensory modality of the cue/prompt (e.g., whether the word was shown visually or played auditorily), so we should not assume Visual just because many BCI tasks use screens.","metadata_analysis":"Key explicit facts from metadata:\n1) Task/construct: “128-channel EEG recordings during speech production tasks.”\n2) Conditions consistent with overt execution and imagery: “under three speech conditions: overt, minimally overt, and covert.”\n3) Content to be produced: “Participants produced one of 5 color words (green, magenta, orange, violet, yellow)” and “Each trial consists of 5 repetitions of the same word”.\n4) Participants: only “Subjects: 3” with no diagnosis/clinical recruitment described.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n- Metadata says: no clinical group is mentioned; only “Subjects: 3”.\n- Few-shot pattern suggests: when no disorder is stated, label as Healthy (as in multiple few-shot healthy datasets).\n- Alignment: ALIGN (metadata absence of pathology + convention).\n\nModality:\n- Metadata says: speech production is performed (\"speech production tasks\"; overt/minimally overt/covert), but does NOT state how the word cue was presented (no explicit “visual” or “auditory” stimulus description).\n- Few-shot pattern suggests: motor/imagery paradigms are sometimes labeled Visual when explicit screen targets are described (motor movement/imagery example), or Auditory when sounds are explicitly presented (digit span example).\n- Alignment: PARTIAL/UNCERTAIN (few-shot indicates we should follow explicit stimulus descriptions; here they are missing). Therefore choose Unknown rather than inferring Visual/Auditory.\n\nType:\n- Metadata says: “speech production tasks” with “overt… and covert” conditions.\n- Few-shot pattern suggests: overt action + imagery maps to Type=Motor (motor movement/imagery example).\n- Alignment: ALIGN (task is fundamentally motor/speech-action and includes covert condition akin to imagery).","decision_summary":"Top-2 candidates per category with head-to-head selection:\n\nPathology:\n1) Healthy — Evidence: no diagnosis or patient recruitment mentioned; only “Subjects: 3”. Matches few-shot convention that non-clinical datasets are Healthy.\n2) Unknown — Would apply if recruitment/health status were unclear or hinted clinical, but there is no such hint.\nWinner: Healthy. Alignment status: aligned.\nConfidence basis: absence of any pathology mention + clear small nonclinical-style description (“Subjects: 3”), but not explicitly stating “healthy participants”.\n\nModality:\n1) Unknown — Evidence: no explicit cue/stimulus modality is described; only production conditions (“overt… covert”).\n2) Visual — Possible inference if the color words were displayed (DISPLAY channels exist: “Channels 129-130: DISPLAY”), but this does not explicitly say words were visually presented.\nWinner: Unknown (insufficient explicit stimulus information to label Visual/Auditory). Alignment status: uncertain; conservative choice.\nConfidence basis: explicit lack of stimulus-modality statements, despite indirect hints.\n\nType:\n1) Motor — Evidence: “speech production tasks” and conditions “overt… and covert” (imagery-like) strongly indicate motor production/imagery as the research construct, consistent with few-shot motor conventions.\n2) Other — Could be used if the focus were purely decoding/BCI methods rather than motor cognition, but metadata centers on production conditions.\nWinner: Motor. Alignment status: aligned.\nConfidence basis: 2 explicit metadata quotes about speech production and overt/covert conditions + strong few-shot analog to motor/imagery mapping."}},"canonical_name":null,"name_confidence":0.84,"name_meta":{"suggested_at":"2026-04-14T10:18:35.343Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"author_year","author_year":"Sato2026_Delineating"}}