{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a3437","dataset_id":"ds006104","associated_paper_doi":"10.1038/s41597-025-05187-2","authors":["João Pedro Carvalho Moreira","Vinícius Rezende Carvalho","Eduardo Mazoni Andrade Marçal Mendes","Ariah Fallah","Terrence J. Sejnowski","Claudia Lainscsek","Lindy Comstock"],"bids_version":"1.6.0","contact_info":null,"contributing_labs":null,"data_processed":true,"dataset_doi":"doi:10.18112/openneuro.ds006104.v1.0.1","datatypes":["eeg"],"demographics":{"subjects_count":24,"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://www.nature.com/articles/s41597-025-05187-2.pdf"},"funding":[],"ingestion_fingerprint":"1336c9bb56777eed757875f6d63b22e5e29b16460487fc6cc20bc85f86627cb6","license":"CC0","n_contributing_labs":null,"name":"EEG dataset for speech decoding","readme":"EEG dataset for speech decoding\n============================\nDataset Overview\n---------------\nThis dataset contains EEG recordings from a phoneme discrimination task with TMS.\nThe data were collected during two related studies in 2019 and 2021.\nStudy 1 (2019, Session 01):\n- 8 participants (P01-P08)\n- Focus on CV and VC phoneme pairs\n- 2 blocks: CV pairs and VC pairs\n- TMS targeted to LipM1 (-56, -8, 46) and TongueM1 (-60, -10, 25)\nStudy 2 (2021, Session 02):\n- 16 participants (S01-S16)\n- Expanded to include single phonemes and phoneme triplets\n- 4 blocks: single phonemes, CV pairs, real words, and pseudowords\n- Additional TMS targets included Broca's area (BA 44: -51, 7, 23) and verbal memory region (BA 6: -46, 1, 41)\nTask Description\n---------------\nParticipants listened to speech sounds and identified stimuli with a button-press response.\nThe stimuli included:\n1. Single phonemes - Consonants (/b/, /p/, /d/, /t/, /s/, /z/) and vowels (/i/, /E/, /A/, /u/, /oU/)\n2. Phoneme pairs - CV and VC combinations of the phonemes\n3. Phoneme triplets - Real and pseudowords constructed of CVC sequences\nTMS Methodology\n--------------\nDetailed information about TMS parameters can be found in the sourcedata/tms_metadata/tms_parameters.json file.\nTMS was applied using a Magstim Super Rapid Plus1 stimulator with a figure-of-eight 40 mm coil.\nStimulation was delivered at 110% of resting motor threshold as paired pulses with 50ms interpulse interval.\nDetailed information about the methodology and results can be found in the associated publication:\nMoreira et al. \"An open-access EEG dataset for speech decoding: Exploring the role of articulation and coarticulation\"\nDirectory Structure\n------------------\nThe dataset follows BIDS convention with the following structure:\n/sub-[subject]/ses-[session]/eeg/\nWhere subject is P01-P08 for Study 1 and S01-S16 for Study 2.\nSession is 01 for Study 1 and 02 for Study 2.\nContact Information\n------------------\nFor questions about this dataset, please contact Lindy Comstock at lbcomstock@ucla.edu","recording_modality":["eeg"],"senior_author":null,"sessions":["01","02"],"size_bytes":90007298374,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["Words","phonemes","singlephoneme"],"timestamps":{"digested_at":"2026-05-31T16:23:21.803401+00:00","dataset_created_at":null,"dataset_modified_at":null},"total_files":56,"storage":{"backend":"s3","base":"s3://openneuro.org/ds006104","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv"]},"tagger_meta":{"config_hash":"4a051be509a0e3d0","metadata_hash":"dfb18adf9d94dcad","model":"openai/gpt-5.2","tagged_at":"2026-01-20T18:50:29.621067+00:00"},"tags":{"pathology":["Healthy"],"modality":["Auditory"],"type":["Perception"],"confidence":{"pathology":0.6,"modality":0.8,"type":0.8},"reasoning":{"few_shot_analysis":"Most similar few-shot by stimulus/channel is: (1) \"Subcortical responses to music and speech...\" which uses auditory speech/music stimuli and is labeled Modality=Auditory and Type=Perception; this guides mapping speech-sound listening paradigms to Auditory+Perception. A secondary convention reference is the digit-span dataset (Auditory+Memory), showing that when the goal is memorization/recall it maps to Memory, whereas the current dataset is explicit discrimination/identification of phonemes (more like Perception than Memory). None of the few-shots suggest a pathology label when participants are typical volunteers; they are labeled Healthy when no clinical recruitment is described.","metadata_analysis":"Key task/stimulus facts from the provided README:\n- \"EEG recordings from a phoneme discrimination task with TMS.\"\n- \"Participants listened to speech sounds and identified stimuli with a button-press response.\"\n- \"The stimuli included: 1. Single phonemes ... 2. Phoneme pairs ... 3. Phoneme triplets - Real and pseudowords ...\"\nPopulation facts present/absent:\n- Only participant counts/IDs are given (e.g., \"8 participants (P01-P08)\"; \"16 participants (S01-S16)\") with no mention of any diagnosis, patient group, or clinical recruitment criteria.","paper_abstract_analysis":"No useful paper information (only a citation string is provided; no abstract text to disambiguate aims/population).","evidence_alignment_check":"Pathology:\n- Metadata says: no diagnosis/clinical recruitment is stated; only \"8 participants\" and \"16 participants\" are described.\n- Few-shot pattern suggests: when no disorder is mentioned and participants are typical research volunteers, label as Healthy.\n- Alignment: ALIGN (no conflicting explicit clinical-population fact).\n\nModality:\n- Metadata says: \"Participants listened to speech sounds\" and stimuli are phonemes/words.\n- Few-shot pattern suggests: speech/music listening paradigms map to Auditory (e.g., music vs speech dataset).\n- Alignment: ALIGN.\n\nType:\n- Metadata says: \"phoneme discrimination task\" and participants \"identified stimuli\" (sensory/phoneme categorization).\n- Few-shot pattern suggests: discrimination/encoding of auditory stimuli maps to Perception (as in the music/speech auditory-response dataset), whereas Memory would require explicit memorization/recall demands (digit-span example).\n- Alignment: ALIGN (TMS is a manipulation method, but the described cognitive goal is discrimination/identification).","decision_summary":"Top-2 candidates with head-to-head comparisons:\n\nPathology candidates:\n1) Healthy (selected): Supported by absence of any clinical recruitment/diagnosis language and presence of generic participant listings: \"8 participants (P01-P08)\" and \"16 participants (S01-S16)\".\n2) Unknown (runner-up): Because the metadata never explicitly states \"healthy\" or \"control\".\nSelection rationale: Healthy is more consistent with the typical volunteer framing and lack of any pathology terms; no explicit clinical fact pushes to another label.\nConfidence basis: contextual inference only (no explicit 'healthy' statement) => moderate confidence.\n\nModality candidates:\n1) Auditory (selected): \"Participants listened to speech sounds\"; stimuli are phonemes/words/pseudowords.\n2) Other (runner-up): because TMS is present, but TMS is not a sensory stimulus modality here; the dominant presented stimulus is auditory.\nSelection rationale: auditory speech stimuli are clearly the primary input channel.\nConfidence basis: 2 explicit stimulus quotes.\n\nType candidates:\n1) Perception (selected): Explicit \"phoneme discrimination task\" and \"identified stimuli\" indicates sensory categorization/discrimination.\n2) Clinical/Intervention (runner-up): TMS is an intervention technique, but the study is not framed as a clinical cohort/intervention trial; the construct studied is speech-sound discrimination.\nSelection rationale: per guidelines, discrimination/detection tasks map to Perception; TMS is methodological rather than the primary research purpose category here.\nConfidence basis: 2 explicit task-purpose quotes (phoneme discrimination; identify stimuli)."}},"computed_title":"EEG dataset for speech decoding","nchans_counts":[{"val":61,"count":53},{"val":83,"count":3}],"sfreq_counts":[{"val":2000.0,"count":56}],"stats_computed_at":"2026-05-31T19:34:32.602220+00:00","total_duration_s":182725.0,"author_year":"Moreira2025","canonical_name":null,"bad_channels_info":null,"acknowledgements":"This research was funded by the U.S. Russia Foundation through award No. 20-AUG-19-UCLA. The article is an output of a research project implemented as part of the Basic Research Program at the National Research University Higher School of Economics (HSE University). We would like to thank Tyler Wishard, Bela Syed, Sophia Mourad, and Panagiota Loizidou for their assistance in collecting the data. Figure 3a is adapted with permission from Wolters Kluwer Health, Inc.: Digeser FM, Wohlberedt T, Hoppe U. Contribution of spectrotemporal features on auditory event-related potentials elicited by consonant vowel syllables. Ear and Hearing. 2009 Dec 1;30(6):704-12. https://doi.org/10.1097/AUD.0b013e3181b1d42d. The Creative Commons license does not apply to this content. Use of the material in any format is prohibited without written permission from the publisher, Wolters Kluwer Health, Inc. Please contact permissions@lww.com for further information. Figure 3b is adapted from Khalighinejad B, da Silva GC, Mesgarani N. Dynamic encoding of acoustic features in neural responses to continuous speech. Journal of Neuroscience. 2017 Feb 22;37(8):2176-85. https://doi.org/10.1523/JNEUROSCI.2383-16.2017. Figure 3c is adapted with permission from John Wiley // Sons, Inc.: Rogasch NC, Fitzgerald PB. Assessing cortical network properties using TMS–EEG. Human brain mapping. 2013 Jul;34(7):1652-69. https://doi.org/10.1002/hbm.22016.","generated_by":[{"Name":"MATLAB","Version":"9.14.0.2206163 (R2023a)"}],"how_to_acknowledge":"Please cite: Moreira et al. \"An open-access EEG dataset for speech decoding: Exploring the role of articulation and coarticulation\"","associated_paper_meta":{"channel":"crossref-biblio","confidence":"high","author_overlap":7,"is_oa":true,"oa_status":"gold","source":"paper_resolver"}}}