{"dataset":{"id":"47568","dataset_id":"nm000257","name":"Imagined speech EEG dataset — short words condition (Nguyen et al. 2017)","description":"This dataset contains preprocessed EEG recordings from 6 healthy participants performing imagined speech tasks with three short word conditions (out, in, up). Data were acquired at 256 Hz using 64 channels and analyzed using Riemannian manifold methods and relevance vector machines for brain-computer interface applications. The motor imagery paradigm employed auditory and visual cueing, yielding 5,400 trials suitable for BCI research and benchmarking.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000257","concept_doi":"10.82901/nemar.nm000257","latest_version_doi":"10.82901/nemar.nm000257.v1.0.3","created_at":"2026-06-19 23:06:16","updated_at":"2026-08-18 18:19:50","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"enriched\",\n  \"title\": \"Imagined speech EEG dataset — short words condition (Nguyen et al. 2017)\",\n  \"description\": \"This dataset contains preprocessed EEG recordings from 6 healthy participants performing imagined speech tasks with three short word conditions (out, in, up). Data were acquired at 256 Hz using 64 channels and analyzed using Riemannian manifold methods and relevance vector machines for brain-computer interface applications. The motor imagery paradigm employed auditory and visual cueing, yielding 5,400 trials suitable for BCI research and benchmarking.\",\n  \"methods_description\": \"EEG data were acquired at 256 Hz using 64 channels (60 EEG, 4 EOG) with a BrainProducts ActiCHamp system. Preprocessing included bandpass filtering (8-70 Hz, 5th order Butterworth), 60 Hz notch filtering, and EOG artifact removal via adaptive filtering. Trials of 2 seconds duration were extracted from a 5-second post-stimulus interval following an auditory beep sequence. Riemannian manifold methods and relevance vector machines were applied for classification.\",\n  \"license\": \"other-open\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Chuong H. Nguyen\": {},\n    \"George K. Karavas\": {},\n    \"Panagiotis Artemiadis\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"imagined speech\"\n    },\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"motor imagery\"\n    },\n    {\n      \"term\": \"brain-computer interface\"\n    },\n    {\n      \"term\": \"Riemannian manifold\"\n    },\n    {\n      \"term\": \"covariance matrix\"\n    },\n    {\n      \"term\": \"relevance vector machines\"\n    },\n    {\n      \"term\": \"short words\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.1088/1741-2552/aa8235\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000257\",\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\": \"https://nemar.org/dataset/nm000257\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"2.0 GB (13 files)\"\n  ],\n  \"formats\": [\n    \".edf\",\n    \".json\",\n    \".mat\",\n    \".md\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"f921ae2efaef99b6e877ca825f3261c200955e52478ffd53def8a79140a0ea0a\"\n}","last_activity_at":"2026-08-16 13:35:42","source":null,"source_id":null,"subject_count":6,"modalities":"eeg","age_min":null,"age_max":null,"file_size":2042956079,"total_files":83,"tasks":"imagery","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Chuong H. Nguyen, George K. Karavas, Panagiotis Artemiadis","license":"other-open","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000257-blue)](https://doi.org/10.82901/nemar.nm000257)\n\n# Imagined speech EEG dataset — short words condition (Nguyen et al. 2017)\n\n## Overview\n\nThis dataset comprises preprocessed EEG recordings from 6 healthy participants performing imagined speech tasks with three short word conditions (out, in, up). The study employed a motor imagery paradigm with auditory and visual cueing, yielding 5,400 trials analyzed using Riemannian manifold methods and relevance vector machines for brain-computer interface applications.\n\n## Dataset Summary\n\n| Property | Value |\n|---|---|\n| Subjects | 6 |\n| Channels | 64 |\n| Classes | 3 |\n| Trial length | 5 s |\n| Sampling frequency | 256 Hz |\n| Sessions | 1 |\n| Total trials | 1800 |\n| Paradigm | MotorImagery |\n\n## Data Collection Methods\n\nEEG data were acquired at 256 Hz using 64 channels (60 EEG, 4 EOG) with a BrainProducts ActiCHamp system. Preprocessing included bandpass filtering (8-70 Hz, 5th order Butterworth), 60 Hz notch filtering, EOG artifact removal via adaptive filtering. Trials were 2 seconds in duration, extracted from a 5-second post-stimulus interval following an auditory beep sequence.\n\n## How to Access via MOABB\n\nInstall MOABB and load this dataset directly:\n\n```python\nfrom moabb.datasets import Nguyen2017_S\nfrom moabb.paradigms import MotorImagery\nparadigm = MotorImagery()\n\ndataset = Nguyen2017_S()\nX, y, metadata = paradigm.get_data(dataset)\n```\n\nFor more details see the [MOABB documentation](https://moabb.neurotechx.com/) and the\n[MOABB dataset page](https://moabb.neurotechx.com/docs/generated/moabb.datasets.Nguyen2017_S.html).\n\n## Citation\n\nIf you use this dataset please cite the primary publication:\n\n> DOI: [10.1088/1741-2552/aa8235](https://doi.org/10.1088/1741-2552/aa8235)\n\n## NEMAR / MOABB Benchmark Collection\n\nThis BIDS-formatted dataset was converted from the original data using the\n[MOABB](https://moabb.neurotechx.com/) pipeline and re-hosted on\n[NEMAR](https://nemar.org/) as part of the MOABB benchmark collection.\nThe original data and license terms apply — see `dataset_description.json` for details.\n","bids_version":"1.9.0","sessions_count":1,"publish_date":null,"embedding_dirty":0,"license_tier":"unknown","zarr_status":"ready","zarr_converted_at":"2026-08-22 07:39:54","zarr_store_count":6,"zarr_index_etag":"f2bc1668d925c4fd75db685a0f1d84c7","zarr_source_commit":"70b419c921f86a402a10c2e0d7e73d0f86c132bb","archive_status":"ready","archive_size":1910866168,"archive_retry_count":0,"records_status":"ready","archive_skip_reason":null,"zarr_errors":0,"zarr_failure_count":0,"zarr_deterministic":0,"zarr_failed_at":null,"num_dataset_citations":0,"num_datapaper_citations":98,"n_channels":60,"electrode_system":"10-10","has_hed":1,"hed_version":"8.4.0","is_exemplar":0,"bytes_present":2042724759,"data_complete":1,"withdrawn_at":null,"withdrawn_reason":null,"archive_complete":1,"archive_absent_files":0,"archive_declared_files":83,"zarr_pool_breaks":null,"total_recording_duration":12906,"recording_duration_min":2151,"recording_duration_max":2151,"recording_count":6,"recordings_unavailable":0,"recordings_measured":6,"channel_count_min":60,"channel_count_max":60,"sampling_frequency":256,"power_line_frequency":60,"eeg_reference":null,"placement_scheme":"10-20 system","sweep_stamps":"{\"enrichment_updated_at\":\"2026-08-18 18:16:33\",\"metadata_updated_at\":\"2026-08-18 18:19:49\",\"archive_checked_at\":\"2026-08-18 18:25:02\",\"zarr_checked_at\":null,\"records_checked_at\":\"2026-08-18 18:24:04\",\"citations_updated_at\":\"2026-09-10 03:00:14\",\"channel_montage_checked_at\":\"2026-06-28 23:01:13\",\"hed_checked_at\":\"2026-06-30 04:31:22\",\"data_checked_at\":null,\"availability_report_at\":\"2026-08-22 03:01:07\",\"recording_stats_at\":\"2026-09-02 11:32:04\",\"signal_defaults_at\":\"2026-09-02 11:49:26\"}","participants":6,"num_citations":98,"latest_version":"v1.0.3","zarr_verify_status":null,"zarr_verified_at":null,"owner_username":"bruaristimunha","owner_github":"bruAristimunha","file_size_formatted":"1.90 GB","zarr_data_failures":null,"zarr_index_url":"https://zarr.nemar.org/nm000257/zarr/index.json","attestation_deposit_type":null,"attestation_key_status":null,"attestation_deidentified":null,"attestation_no_duplicate":null,"attestation_upstream_source":null,"attestation_accepted_at":null}}