{"dataset":{"id":"61285","dataset_id":"on008465","name":"NeuralEcho MACS high-density communication-related EEG dataset","description":"This dataset comprises high-density EEG recordings from 30 healthy, right-handed, native Chinese-speaking adults performing imagined and executed vocalization and writing tasks involving four Chinese strokes and four English letters. The factorial design crossed task mode, action, script and token identity, yielding 32 event-coded conditions across 15,360 task events. The dataset supports research into motor imagery, speech and handwriting-related neural communication signals.","owner_user_id":15,"status":"active","github_repo":"nemarDatasets/on008465","concept_doi":"10.82901/nemar.on008465","latest_version_doi":"10.82901/nemar.on008465.v1.0.0","created_at":"2026-09-08 19:01:04","updated_at":"2026-09-08 19:29:36","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"NeuralEcho MACS high-density communication-related EEG dataset\",\n  \"description\": \"This dataset comprises high-density EEG recordings from 30 healthy, right-handed, native Chinese-speaking adults performing imagined and executed vocalization and writing tasks involving four Chinese strokes and four English letters. The factorial design crossed task mode, action, script and token identity, yielding 32 event-coded conditions across 15,360 task events. The dataset supports research into motor imagery, speech and handwriting-related neural communication signals.\",\n  \"methods_description\": \"Each participant completed eight runs of 64 trials in a factorial design crossing task mode (motor imagery or motor execution), action (read/speak or write), script (Chinese or English) and token identity. EEG was recorded using a 127-channel cap with a shared template layout referenced to bilateral mastoid electrodes M1 and M2. Proprietary Neuroscan source recordings were converted to BrainVision format organized per BIDS, with EEGLAB used for preprocessing derivatives.\",\n  \"license\": \"CC0\",\n  \"dataset_type\": \"raw\",\n  \"authors\": {\n    \"Huang Jinfeng\": {},\n    \"Kangqiao Liu\": {},\n    \"Li Zhongjie\": {},\n    \"Jin Yongdong\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"motor imagery\"\n    },\n    {\n      \"term\": \"handwriting\"\n    },\n    {\n      \"term\": \"speech production\"\n    },\n    {\n      \"term\": \"high-density EEG\"\n    },\n    {\n      \"term\": \"brain-computer interface\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/on008465\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/on008465\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.21105/joss.01896\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.1038/s41597-019-0104-8\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds008465.v1.0.0\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"177 GB (3830 files)\"\n  ],\n  \"formats\": [\n    \".cnt\",\n    \".csv\",\n    \".dap\",\n    \".eeg\",\n    \".json\",\n    \".md\",\n    \".npy\",\n    \".npz\",\n    \".rs3\",\n    \".set\",\n    \".study\",\n    \".tsv\",\n    \".txt\",\n    \".vhdr\",\n    \".vmrk\",\n    \".yml\"\n  ],\n  \"source_hash\": \"eb4d10eb3c9daa1a7d9974f60661b5ecdf506afe49696693db092645c8de9586\"\n}","last_activity_at":"2026-09-08 19:01:04","source":"openneuro","source_id":"ds008465","subject_count":30,"modalities":"eeg","age_min":19,"age_max":30,"file_size":194556468057,"total_files":5345,"tasks":"comm","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Huang Jinfeng, Kangqiao Liu, Li Zhongjie, Jin Yongdong","license":"CC0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on008465-blue)](https://doi.org/10.82901/nemar.on008465)\n\nNeuralEcho MACS high-density communication-related EEG dataset\n================================================================\n\nOverview\n--------\nThis dataset contains high-density EEG recordings from 30 healthy,\nright-handed, native Chinese-speaking adults. Participants performed imagined\nand executed vocalization and writing tasks involving four Chinese strokes\n(heng, shu, pie and na) and four English letters (a, b, c and d).\n\nExperimental design\n-------------------\nEach participant completed eight runs of 64 trials. The factorial design\ncrossed task mode (motor imagery or motor execution), action (read/speak or\nwrite), script (Chinese or English) and token identity, yielding 32 event-coded\nconditions and 15,360 task events.\n\nData organization\n-----------------\nThe BIDS root contains BrainVision EEG data and metadata. Proprietary Neuroscan\nsource recordings are under sourcedata/. EEGLAB preprocessing derivatives and\nsubject-level machine-learning exports are under derivatives/. Those two\ndirectories are intentionally listed in .bidsignore because they are shared\nfor reuse but are not part of raw-BIDS validation.\n\nElectrode coordinates and reference\n-----------------------------------\nThe electrodes.tsv files contain a shared 127-channel cap-layout template\nderived from Code/127cn.csv. Coordinates are expressed in millimetres using\nthe EEGLAB ALS convention: positive x points anteriorly, positive y points to\nthe participant's left and positive z points superiorly. These are template\ncoordinates repeated across participants, not participant-specific digitized\npositions. Trigger is an acquisition channel and is therefore excluded from\nelectrodes.tsv. The continuous recordings used the bilateral mastoid\nelectrodes M1 and M2 as the EEG reference.\n\nAuthors and contributors\n------------------------\nHuang Jinfeng; Kangqiao Liu; Li Zhongjie; Jin Yongdong.\nData were collected and curated collaboratively by Tianjin University,\nShenzhen University and NeuralEcho Technology Co., Ltd.\n\nReferences\n----------\nAppelhoff, S. et al. MNE-BIDS: Organizing electrophysiological data into the\nBIDS format and facilitating their analysis. Journal of Open Source Software\n4, 1896 (2019). https://doi.org/10.21105/joss.01896\n\nPernet, C. R. et al. EEG-BIDS, an extension to the brain imaging data structure\nfor electroencephalography. Scientific Data 6, 103 (2019).\nhttps://doi.org/10.1038/s41597-019-0104-8\n","bids_version":"1.7.0","sessions_count":1,"publish_date":null,"embedding_dirty":0,"license_tier":"public","zarr_status":null,"zarr_converted_at":null,"zarr_store_count":null,"zarr_index_etag":null,"zarr_source_commit":null,"archive_status":null,"archive_size":null,"archive_retry_count":0,"records_status":"ready","archive_skip_reason":"dataset 181.2 GB exceeds 100.0 GB archive limit; use direct download","zarr_errors":null,"zarr_failure_count":null,"zarr_deterministic":null,"zarr_failed_at":null,"num_dataset_citations":0,"num_datapaper_citations":0,"n_channels":124,"electrode_system":"10-05","has_hed":0,"hed_version":null,"is_exemplar":0,"bytes_present":190485702143,"data_complete":0,"withdrawn_at":null,"withdrawn_reason":null,"archive_complete":null,"archive_absent_files":null,"archive_declared_files":null,"zarr_pool_breaks":null,"total_recording_duration":null,"recording_duration_min":null,"recording_duration_max":null,"recording_count":null,"recordings_unavailable":null,"recordings_measured":null,"channel_count_min":null,"channel_count_max":null,"sampling_frequency":1000,"power_line_frequency":50,"eeg_reference":"M1 and M2 bilateral mastoid electrodes","placement_scheme":"Shared 127-channel template montage derived from 127cn.csv; coordinates were not digitized separately for each participant","sweep_stamps":"{\"enrichment_updated_at\":\"2026-09-08 19:29:05\",\"metadata_updated_at\":\"2026-09-08 19:29:34\",\"archive_checked_at\":\"2026-09-08 19:26:49\",\"records_checked_at\":\"2026-09-08 19:27:37\"}","participants":30,"num_citations":0,"latest_version":"v1.0.0","zarr_verify_status":null,"zarr_verified_at":null,"owner_username":"nemarAdmin","owner_github":"nemarAdmin","file_size_formatted":"181 GB","zarr_data_failures":null,"zarr_index_url":null,"attestation_deposit_type":null,"attestation_key_status":null,"attestation_deidentified":null,"attestation_no_duplicate":null,"attestation_upstream_source":null,"attestation_accepted_at":null}}