{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a33db","dataset_id":"ds005383","associated_paper_doi":null,"authors":["Yanru Bai","Qi Tang","Ran Zhao","Hongxing Liu","Mingkun Guo","Shuming Zhang","Minghan Guo","Junjie Wang","Changjian Wang","Mu Xing","Guangjian Ni","Dong Ming"],"bids_version":"1.7.0","contact_info":[],"contributing_labs":null,"data_processed":true,"dataset_doi":"doi:10.18112/openneuro.ds005383.v1.0.0","datatypes":["eeg"],"demographics":{"subjects_count":30,"ages":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30],"age_min":1,"age_max":30,"age_mean":15.5,"species":null,"sex_distribution":{"o":30},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds005383","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"483ee1271ef8641a1cca6f34044c41a48f27d60dc1d3264eebd40b5dddb7cd8e","license":"CC0","n_contributing_labs":null,"name":"TMNRED, A Chinese Language EEG Dataset for Fuzzy Semantic Target Identification in Natural Reading Environments","readme":"# TMNRED Dataset - Chinese Natural Reading EEG for Fuzzy Semantic Target Identification\n## Overview\nThis dataset, named TMNRED, consists of electroencephalogram (EEG) recordings obtained from 30 participants engaged in natural reading tasks. The aim is to investigate the mechanisms of semantic processing in the Chinese language within a natural reading environment.\n## Data Collection\n- Participants: 30 healthy, right-handed individuals (average age: 22.07 years, standard deviation: 2.7 years; 18 females, 12 males) who are native Chinese speakers.\n- Materials: Text ranging from 15 to 20 characters, presented as news headlines or short sentences. Materials include target semantic items and non-target semantic items.\n- Procedure: Participants read sentences displayed on a screen at their own pace. Each participant completed 8 blocks of 400 trials in total, with each trial lasting approximately 2.2 seconds, including a fixation cross and inter-stimulus intervals.\n## Data Structure\nThe dataset is organized according to the BIDS standard:\n- Main Folder:\n  - `dataset_description.json`: Description of the dataset.\n  - `participants.tsv`: Participant information.\n  - `participants.json`: Details of columns in `participants.tsv`.\n  - `README`: General information about the dataset.\n  - `data_all.mat`: Labeled EEG data of all subjects in MAT format.\n- Derivative Data:\n  - `final_bids/`: EEG data stored in JSON, TSV, and EDF formats.\n  - `preproc/`: Preprocessed data, including subfolders for each subject (`sub-01`, etc.), with data in various formats (BDF, SET, FDT, ERP, MAT).\n## Technical Validation\nSensor-level EEG analyses were performed, showing distinct responses to target and non-target words at different time points, with notable changes in potential distribution across the scalp.\n## Distribution\nThe raw and preprocessed EEG data are openly available online at https://github.com/tym5049/TMNRED_Dataset under the Creative Commons Attribution 4.0 International Public License (https://creativecommons.org/licenses/by/4.0/).\n## Usage Notes\n- Researchers should cite the dataset appropriately when using it.\n- For any questions or issues, please refer to the `README` file or contact the corresponding authors: Yanru Bai (yr56 bai@tju.edu.cn), Guangjian Ni (niguangjian@tju.edu.cn).\n## Acknowledgments\nThis work was mainly supported by the National Key R&D Program of China (2023YFF1203503) and the National Natural Science Foundation of China (82202290). We also thank all research assistants who provided general support in participant recruiting and data collection.","recording_modality":["eeg"],"senior_author":"Dong Ming","sessions":["1","2","3","4","5","6","7","8"],"size_bytes":375577954,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["fuzzysemanticrecognition"],"timestamps":{"digested_at":"2026-04-22T12:27:42.735745+00:00","dataset_created_at":"2024-07-28T15:44:23.976Z","dataset_modified_at":"2024-07-28T17:18:44.000Z"},"total_files":240,"storage":{"backend":"s3","base":"s3://openneuro.org/ds005383","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv"]},"tagger_meta":{"config_hash":"4a051be509a0e3d0","metadata_hash":"f4179fa07d2a6b89","model":"openai/gpt-5.2","tagged_at":"2026-01-20T17:50:09.876041+00:00"},"tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Perception"],"confidence":{"pathology":0.85,"modality":0.85,"type":0.7},"reasoning":{"few_shot_analysis":"Most similar few-shot by stimulus/input channel and goal is the visual discrimination task in the schizophrenia dataset (Meta-rdk), labeled as Modality=Visual and Type=Perception. That example establishes the convention that tasks centered on processing visually presented stimuli (even with target/choice aspects) map to Perception when the primary scientific aim is stimulus/semantic processing rather than motor control or value-based choice. The Braille letters example also supports mapping letter/word stimulus processing to a sensory/perceptual framing (there tactile+learning), suggesting that language-like stimuli are treated under general perceptual/cognitive categories available here.","metadata_analysis":"Pathology/population facts: the README explicitly states a non-clinical cohort: \"Participants: 30 healthy, right-handed individuals\" and also \"native Chinese speakers.\" \nTask/stimulus facts supporting visual modality and semantic reading: stimuli are visually presented text: \"Text ranging from 15 to 20 characters, presented as news headlines or short sentences\" and \"Participants read sentences displayed on a screen at their own pace.\" \nCognitive aim/type facts: the goal is semantic processing: \"The aim is to investigate the mechanisms of semantic processing in the Chinese language\" and materials include \"target semantic items and non-target semantic items.\"","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology: Metadata says \"30 healthy\" participants. Few-shot conventions: when no disorder recruitment is stated, label Healthy. ALIGN.\nModality: Metadata says sentences are \"displayed on a screen\" (visual text). Few-shot conventions: screen-presented dots/arrows/words => Visual. ALIGN.\nType: Metadata says the scientific aim is \"semantic processing\" during natural reading, with target vs non-target semantic items. Few-shot conventions: stimulus-processing/discrimination paradigms are typically labeled Perception (e.g., visual discrimination). This largely ALIGNs, though an alternative could be Attention due to target vs non-target; however the explicit aim emphasizes semantic processing rather than attentional control.","decision_summary":"Top-2 candidates — Pathology: (1) Healthy: supported by \"30 healthy\"; (2) Unknown: would apply only if population unspecified (not the case). Final: Healthy. \nTop-2 candidates — Modality: (1) Visual: supported by \"displayed on a screen\" and text stimuli; (2) Other: if modality were unclear (not the case). Final: Visual. \nTop-2 candidates — Type: (1) Perception: supported by \"mechanisms of semantic processing\" during reading and target vs non-target semantic item processing; consistent with few-shot visual discrimination=>Perception convention. (2) Attention: plausible because of \"target semantic items and non-target semantic items\" (target identification). Final: Perception because the dataset description foregrounds semantic processing in natural reading rather than attentional control per se. \nConfidence justification: Pathology and Modality have multiple explicit quotes; Type has explicit semantic-processing aim but category mapping (semantic/language) is approximate within allowed labels, reducing confidence."}},"nemar_citation_count":0,"computed_title":"TMNRED, A Chinese Language EEG Dataset for Fuzzy Semantic Target Identification in Natural Reading Environments","nchans_counts":[{"val":31,"count":240}],"sfreq_counts":[{"val":200.0,"count":240}],"stats_computed_at":"2026-04-22T23:16:00.309433+00:00","source_url":"https://openneuro.org/datasets/ds005383","total_duration_s":29976.800000000003,"canonical_name":null,"name_confidence":0.97,"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":"Bai2024"}}