{"success":true,"database":"eegdash","data":{"_id":"6953f4239276ef1ee07a32f0","dataset_id":"ds003774","associated_paper_doi":null,"authors":["Krishna Prasad Miyapuram","Pankaj Pandey","Nashra Ahmad","Bharatesh R Shiraguppi","Esha Sharma","Prashant Lawhatre","Dhananjay Sonawane","Derek Lomas"],"bids_version":"1.1.1","contact_info":["Krishna Miyapuram"],"contributing_labs":null,"data_processed":false,"dataset_doi":"10.18112/openneuro.ds003774.v1.0.0","datatypes":["eeg"],"demographics":{"subjects_count":20,"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/ds003774","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"53f3f0b0d15c89d561b8cdc30b9905823c3a20d79782ea111b4c9ec894ac7646","license":"CC0","n_contributing_labs":null,"name":"Music Listening- Genre EEG dataset (MUSIN-G)","readme":"The dataset contains Electroencephalography (EEG) responses from 20 Indian participants, on 12 songs of different genres (from Indian Classical to Goth Rock). Each session indicates a song by its number.\nFor the experiment,  the participants were indicated to close their eyes indicated by a single beep, and the song was presented to them on speakers. After listening to each song, a double beep was presented, asking them to open their eyes and rate their familiarity and enjoyment to the song.  The responses were taken on a scale of 1 to 5, where 1 meant most familiar or most enjoyable, and 5 meant least familiar or least enjoyable.","recording_modality":["eeg"],"senior_author":"Derek Lomas","sessions":["01","02","03","04","05","06","07","08","09","10","11","12"],"size_bytes":10863711359,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["MusicListening"],"timestamps":{"digested_at":"2026-04-22T12:25:41.294682+00:00","dataset_created_at":"2021-08-23T10:09:25.546Z","dataset_modified_at":"2022-08-25T13:43:21.000Z"},"total_files":240,"storage":{"backend":"s3","base":"s3://openneuro.org/ds003774","raw_key":"dataset_description.json","dep_keys":["CHANGES","README"]},"tagger_meta":{"config_hash":"4a051be509a0e3d0","metadata_hash":"2ef48911732aa05d","model":"openai/gpt-5.2","tagged_at":"2026-01-20T10:19:49.814566+00:00"},"tags":{"pathology":["Healthy"],"modality":["Auditory"],"type":["Affect"],"confidence":{"pathology":0.7,"modality":0.8,"type":0.7},"reasoning":{"few_shot_analysis":"Most similar few-shot examples by stimulus domain/modality are: (1) \"Subcortical responses to music and speech...\" which is labeled Auditory/Perception and involves listening to music/speech clips; this guides assigning Modality=Auditory for music listening paradigms. (2) \"EEG: Three armed bandit gambling task\" is labeled Type=Affect because it targets reward/valence-related processing; this guides mapping explicit subjective enjoyment/preference ratings to Type=Affect rather than generic Perception. Few-shot examples do not indicate any pathology inference here; absent explicit diagnosis, convention is Pathology=Healthy.","metadata_analysis":"Key quoted facts from the provided README: (1) Population: \"EEG responses from 20 Indian participants\" (no diagnosis/clinical recruitment mentioned). (2) Auditory stimulus: \"on 12 songs of different genres\" and \"the song was presented to them on speakers.\" (3) Affective/preference report: \"rate their familiarity and enjoyment to the song\" on a 1–5 scale. (4) Task context: \"participants were indicated to close their eyes... and the song was presented\" then \"open their eyes and rate\".","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology: Metadata SAYS \"20 Indian participants\" with no disorder/clinical terms. Few-shot pattern SUGGESTS using Healthy when no clinical recruitment is stated (e.g., multiple healthy-only datasets). ALIGN.\nModality: Metadata SAYS \"song was presented to them on speakers\" and \"12 songs\". Few-shot pattern SUGGESTS Auditory for listening tasks with sound stimuli (e.g., music/speech example). ALIGN.\nType: Metadata SAYS participants \"rate their familiarity and enjoyment\" after each song, which is explicitly hedonic/liking-related. Few-shot pattern SUGGESTS mapping reward/liking/valence-focused paradigms to Affect (e.g., bandit gambling labeled Affect). ALIGN (stronger than Perception because subjective enjoyment is central).","decision_summary":"Pathology top-2: (A) Healthy — supported by \"20 Indian participants\" with no diagnosis/clinical recruitment stated; matches convention of labeling non-clinical cohorts as Healthy. (B) Unknown — possible if demographics/health screening absent, but less supported because lack of pathology mention typically maps to Healthy. Final: Healthy. Confidence 0.7 (1 explicit non-clinical participant description, no contrary evidence).\nModality top-2: (A) Auditory — supported by \"12 songs\" and \"presented... on speakers\" and beep cues; strong match to auditory-listening few-shot. (B) Resting State — eyes closed periods exist, but stimuli are songs and beeps, so not resting. Final: Auditory. Confidence 0.8 (2+ explicit auditory-stimulus quotes + clear few-shot analog).\nType top-2: (A) Affect — supported by \"rate their... enjoyment\" (hedonic evaluation) and also familiarity rating tied to preference/affective appraisal of music. (B) Perception — could be framed as auditory/music perception, but the primary measured construct described is enjoyment/familiarity ratings rather than discrimination/detection. Final: Affect. Confidence 0.7 (1 explicit enjoyment-rating quote; no abstract to further confirm primary aim)."}},"nemar_citation_count":8,"computed_title":"Music Listening- Genre EEG dataset (MUSIN-G)","nchans_counts":[{"val":129,"count":240}],"sfreq_counts":[{"val":1000.0,"count":132},{"val":250.0,"count":108}],"stats_computed_at":"2026-04-22T23:16:00.222497+00:00","total_duration_s":31103.068,"canonical_name":null,"name_confidence":0.72,"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":"Miyapuram2021"}}