{"success":true,"database":"eegdash","data":{"_id":"69d16e04897a7725c66f4c55","dataset_id":"ds007602","associated_paper_doi":null,"authors":["Motoshige Sato","Masakazu Inoue","Kenichi Tomeoka","Ilya Horiguchi","Eri Hatakeyama","Yuya Kita","Atsushi Yamamoto","Ippei Fujisawa","Shuntaro Sasai"],"bids_version":"1.9.0","contact_info":["Ilya Horiguchi"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds007602.v1.0.1","datatypes":["eeg"],"demographics":{"subjects_count":3,"ages":[],"age_min":null,"age_max":null,"age_mean":null,"species":null,"sex_distribution":{"m":4},"handedness_distribution":{"r":4}},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds007602","osf_url":null,"github_url":null,"paper_url":null},"funding":["JST, Moonshot R&D Grant Number JPMJMS2012"],"ingestion_fingerprint":"927c8a4c5e6e294c9fe58b52b840cd14a42c06d7debae33f136b8e701e42c9ee","license":"CC0","n_contributing_labs":null,"name":"EEG-Speech Brain Decoding Dataset","readme":"# EEG-Speech Brain Decoding Dataset\n## Overview\nThis dataset contains EEG recordings and audio data.\n## Sessions\nSessions are labeled by recording date in YYYYMMDD format.\n- Example: `ses-20240401` = recorded on April 1, 2024\nMultiple recordings on the same day are distinguished by run numbers:\n- `run-N`: Nth recording of the day\n## Tasks\n- **speechopen**: Overt speech production task\n  - Participants vocalize visually presented text\n## File Format Notes\n### EEG Data\nRaw EEG data is stored:\n- **Path**: `sub-*/ses-*/eeg/*_eeg.edf`\n- **Note**: EDF format is not officially part of BIDS-EEG specification\n- Files are excluded in `.bidsignore` but documented here for reference\n- Future releases may include EDF conversions for full BIDS compliance\n### Behavioral Data (Audio)\nVocal recordings are stored in `beh/` directories:\n- **Path**: `sub-*/ses-*/beh/*_recording-vocal_beh.wav`\n- **Note**: Not officially part of BIDS-EEG spec, but included for analysis convenience\n- Excluded in `.bidsignore`\n## Directory Structure\n```\ndataset_root/\n├── README                          (this file)\n├── CHANGES                         (version history)\n├── dataset_description.json        (dataset metadata)\n├── participants.tsv                (participant information)\n├── participants.json               (participant column descriptions)\n├── task-speechopen_eeg.json        (task-level EEG metadata)\n├── task-speechopen_events.json     (events column descriptions)\n├── .bidsignore                     (files to ignore in validation)\n│\n├── code/                           (analysis and preprocessing code)\n│   ├── preprocessing/              (EEG and audio preprocessing)\n│   ├── training/                   (model training scripts)\n│   ├── evaluation/                 (evaluation metrics)\n│   └── bids/                       (BIDS conversion scripts)\n│\n├── sub-01/                         (participant data)\n│   └── ses-YYYYMMDD/              (session by date)\n│       ├── eeg/                    (EEG recordings)\n│       └── beh/                    (behavioral/audio data)\n│\n└── derivatives/                    (processed data)\n    └── pipeline-standard/          (standard preprocessing)\n```","recording_modality":["eeg"],"senior_author":"Shuntaro Sasai","sessions":["20230829","20230830","20230831","20230901","20230904","20240821","20240822","20240829","20240902","20240906","20250522","20250523","20250526","20250527","20250528"],"size_bytes":53309491557,"source":"openneuro","storage":{"backend":"s3","base":"s3://openneuro.org/ds007602","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv","task-speechopen_acq-pangolin_eeg.json"]},"study_design":null,"study_domain":null,"tasks":["speechopen"],"timestamps":{"digested_at":"2026-04-22T12:30:33.378447+00:00","dataset_created_at":"2026-03-30T05:29:04.859Z","dataset_modified_at":"2026-03-31T06:40:12.000Z"},"total_files":113,"computed_title":"EEG-Speech Brain Decoding Dataset","nchans_counts":[{"val":134,"count":113}],"sfreq_counts":[{"val":1200.0,"count":113}],"stats_computed_at":"2026-04-22T23:16:00.312984+00:00","total_duration_s":null,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"153532cb5c2c0710","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Motor"],"confidence":{"pathology":0.7,"modality":0.8,"type":0.7},"reasoning":{"few_shot_analysis":"Closest few-shot by task purpose is the \"EEG Motor Movement/Imagery Dataset\" example (labeled Modality=Visual, Type=Motor): it uses a visually presented cue and the research focus is movement execution/imagery. This guides mapping overt speech production (a motor act) to Type=Motor, with Visual modality because the prompt is shown on-screen. For modality conventions, the schizophrenia visual discrimination example (Modality=Visual, Type=Perception) reinforces that when stimuli are displayed on a screen, modality is labeled Visual even if responses include actions/clicks.","metadata_analysis":"Key task/stimulus facts from metadata:\n- Task is overt speech production: \"**speechopen**: Overt speech production task\".\n- Stimulus is visual text: \"Participants vocalize **visually presented text**\".\n- Dataset includes audio recordings but they are behavioral recordings of the vocal output: \"This dataset contains EEG recordings and **audio data**\" and \"Vocal recordings are stored in `beh/` directories\".\n- No clinical recruitment language is present; participants section only lists counts/sex/handedness: \"Subjects: 3; Sex: {'m': 4}; Handedness: {'r': 4}\".","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n1) Metadata says: no diagnosis/clinical recruitment mentioned; only \"Subjects: 3\" with sex/handedness.\n2) Few-shot pattern suggests: absent clinical terms typically maps to Healthy cohorts.\n3) ALIGN (no conflict).\n\nModality:\n1) Metadata says: \"Participants vocalize **visually presented text**\".\n2) Few-shot pattern suggests: screen-based stimuli -> Visual modality (as in the motor imagery and visual discrimination examples).\n3) ALIGN.\n\nType:\n1) Metadata says: \"**Overt speech production task**\" and \"Participants **vocalize**...\" indicating movement/articulation is central.\n2) Few-shot pattern suggests: when the primary experimental focus is movement execution (even if visually cued), Type=Motor (motor imagery/movement example).\n3) ALIGN (speech is a specialized motor behavior; no better dedicated 'Language' type exists in allowed labels).","decision_summary":"Top-2 candidates and selection:\n\nPathology:\n- Healthy: Supported by lack of any stated clinical diagnosis/recruitment and minimal demographics only (\"Subjects: 3...\").\n- Unknown: Also plausible because \"Healthy\" is not explicitly stated.\nWinner: Healthy (dataset description does not indicate any disorder-based recruitment).\nEvidence alignment: aligned.\n\nModality:\n- Visual: Explicit \"visually presented text\".\n- Multisensory: Dataset includes \"audio data\" (vocal recordings), but these appear to be recorded responses rather than presented stimuli.\nWinner: Visual (stimulus/input channel is clearly visual text; audio is output/behavioral recording).\nEvidence alignment: aligned.\n\nType:\n- Motor: \"Overt speech production\" / \"vocalize\" implies articulatory motor activity is the core task.\n- Other: Could be considered speech/language decoding rather than generic motor control, but no dedicated language label exists.\nWinner: Motor (best match to allowed labels given overt movement production is central).\nEvidence alignment: aligned.\n\nConfidence justification:\n- Pathology 0.7: inferred from absence of clinical terms (no explicit 'healthy').\n- Modality 0.8: directly supported by the explicit phrase \"visually presented text\" plus strong few-shot convention.\n- Type 0.7: directly supported by \"Overt speech production\"/\"vocalize\" but some ambiguity between Motor vs Other (speech/language decoding framing)."}},"canonical_name":null,"name_confidence":0.86,"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_Speech"}}