{"dataset":{"id":"47104","dataset_id":"nm000178","name":"BNCI 2020-001 Reach-and-Grasp Electrode Comparison EEG dataset","description":"This dataset comprises EEG recordings from 15 healthy participants performing reach-and-grasp motor imagery tasks using three different electrode systems: gel-based laboratory equipment, water-based mobile EEG, and dry-electrode mobile EEG. Data were acquired at 256 Hz from 58 EEG channels plus 6 EOG channels across three sessions with 7200 total trials. Participants executed palmar grasp (toward glass) and lateral grasp (toward spoon) actions. The study investigates the feasibility of decoding natural reach-and-grasp neural correlates across different EEG acquisition modalities for brain-computer interface applications.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000178","concept_doi":"10.82901/nemar.nm000178","latest_version_doi":"10.82901/nemar.nm000178.v1.0.3","created_at":"2026-06-19 18:05:21","updated_at":"2026-08-18 18:17:35","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"enriched\",\n  \"title\": \"BNCI 2020-001 Reach-and-Grasp Electrode Comparison EEG dataset\",\n  \"description\": \"This dataset comprises EEG recordings from 15 healthy participants performing reach-and-grasp motor imagery tasks using three different electrode systems: gel-based laboratory equipment, water-based mobile EEG, and dry-electrode mobile EEG. Data were acquired at 256 Hz from 58 EEG channels plus 6 EOG channels across three sessions with 7200 total trials. Participants executed palmar grasp (toward glass) and lateral grasp (toward spoon) actions. The study investigates the feasibility of decoding natural reach-and-grasp neural correlates across different EEG acquisition modalities for brain-computer interface applications.\",\n  \"methods_description\": \"EEG data were acquired at 256 Hz sampling rate using 58 EEG channels and 6 EOG channels (64 total) with a 5% grid system montage. Three recording systems were employed: gel-based active electrodes (g.tec USBamp/g.Ladybird, reference: right earlobe, ground: AFz), water-based mobile EEG (EEG-Versatile), and dry-electrode mobile EEG (EEG-Hero). Online filtering included 8th order Chebyshev filter (0.01-100 Hz) and 50 Hz notch filter. Participants performed 80 self-initiated reach-and-grasp trials each for palmar grasp (toward glass) and lateral grasp (toward spoon) with 2-second fixation period, 1-2 second hold, and 4-second inter-trial interval. Offline preprocessing included 4th order Butterworth bandpass filtering (0.3-60 Hz), extended infomax ICA for artifact removal (applied to gel-based and water-based systems; not applied to dry-electrode recordings due to unfavorable channel count), amplitude thresholding (>125 µV), and abnormal joint probability/kurtosis rejection (4 SD threshold).\",\n  \"license\": \"CC-BY-4.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Andreas Schwarz\": {},\n    \"Carlos Escolano\": {},\n    \"Luis Montesano\": {},\n    \"Gernot R. Müller-Putz\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"Electroencephalography\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D004569\"\n    },\n    {\n      \"term\": \"Brain-Computer Interfaces\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D062207\"\n    },\n    {\n      \"term\": \"motor imagery\"\n    },\n    {\n      \"term\": \"reach-and-grasp\"\n    },\n    {\n      \"term\": \"movement-related cortical potential\"\n    },\n    {\n      \"term\": \"mobile EEG\"\n    },\n    {\n      \"term\": \"dry electrodes\"\n    },\n    {\n      \"term\": \"electrode comparison\"\n    },\n    {\n      \"term\": \"electrode systems\"\n    },\n    {\n      \"term\": \"MOABB\"\n    },\n    {\n      \"term\": \"benchmark\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.3389/fnins.2020.00849\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000178\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.3389/fnhum.2022.898300\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/nm000178\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"European Commission\",\n      \"award_number\": \"MoreGrasp\",\n      \"award_title\": \"Horizon 2020 Project MoreGrasp\"\n    },\n    {\n      \"funder_name\": \"European Commission\",\n      \"award_number\": \"GA-644402\",\n      \"award_title\": \"Horizon 2020 Project MoreGrasp\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"9.3 GB (91 files)\"\n  ],\n  \"formats\": [\n    \".edf\",\n    \".json\",\n    \".mat\",\n    \".md\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"df620ceaf8b0c37dbe1ee4f07bb7aecc580508f9a8871159053b9a6c7d78d78b\"\n}","last_activity_at":"2026-08-16 13:28:12","source":null,"source_id":null,"subject_count":45,"modalities":"eeg","age_min":null,"age_max":null,"file_size":9304493351,"total_files":551,"tasks":"imagery","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Andreas Schwarz, Carlos Escolano, Luis Montesano, Gernot R. Müller-Putz","license":"CC-BY-4.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000178-blue)](https://doi.org/10.82901/nemar.nm000178)\n\n# BNCI 2020-001 Reach-and-Grasp Electrode Comparison EEG dataset\n\n## Overview\n\nThis dataset comprises EEG recordings from 45 healthy participants performing self-initiated reach-and-grasp motor imagery tasks using three different recording systems: gel-based laboratory equipment (g.tec USBamp/g.Ladybird), water-based mobile EEG (EEG-Versatile), and dry-electrode mobile EEG (EEG-Hero). Participants executed palmar and lateral grasp actions toward objects while EEG signals were recorded at 256 Hz from 58 EEG channels plus 6 EOG channels (64 total channels). The study investigates the feasibility of decoding natural reach-and-grasp neural correlates across different EEG acquisition modalities for brain-computer interface applications.\n\n## Dataset Summary\n\n| Property | Value |\n|---|---|\n| Subjects | 15 |\n| Channels | 11–64 |\n| Classes | 3 |\n| Trial length | 5 s |\n| Sampling frequency | 256 Hz |\n| Sessions | 3 |\n| Total trials | 7200 |\n| Paradigm | MotorImagery |\n\n## Data Collection Methods\n\nEEG data were acquired at 256 Hz sampling rate using 58 EEG channels and 6 EOG channels (64 total) with a 5% grid system montage. Three recording systems were employed: gel-based active electrodes (g.tec USBamp/g.Ladybird, reference: right earlobe, ground: AFz), water-based mobile EEG (EEG-Versatile), and dry-electrode mobile EEG (EEG-Hero). Online filtering included 8th order Chebyshev filter (0.01-100 Hz) and 50 Hz notch filter. Participants performed 80 self-initiated reach-and-grasp trials each for palmar grasp (toward glass) and lateral grasp (toward spoon) with 2-second fixation period, 1-2 second hold, and 4-second inter-trial interval. Offline preprocessing included 4th order Butterworth bandpass filtering (0.3-60 Hz), extended infomax ICA for artifact removal (applied to gel-based and water-based systems; not applied to dry-electrode recordings due to unfavorable channel count), amplitude thresholding (>125 µV), and abnormal joint probability/kurtosis rejection (4 SD threshold).\n\n## How to Access via MOABB\n\nInstall MOABB and load this dataset directly:\n\n```python\nfrom moabb.datasets import BNCI2020_001\nfrom moabb.paradigms import MotorImagery\nparadigm = MotorImagery()\n\ndataset = BNCI2020_001()\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.BNCI2020_001.html).\n\n## Citation\n\nIf you use this dataset please cite the primary publication:\n\n> DOI: [10.3389/fnhum.2022.898300](https://doi.org/10.3389/fnhum.2022.898300)\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":"attribution","zarr_status":"ready","zarr_converted_at":"2026-08-22 07:46:58","zarr_store_count":45,"zarr_index_etag":"2d58c421739a2f2b4878e579ad1b81d0","zarr_source_commit":"db46b4dcd05fdf5e6fbe666514f1c8bf6dffc7d1","archive_status":"ready","archive_size":8879011965,"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":51,"n_channels":58,"electrode_system":"10-05","has_hed":1,"hed_version":"8.4.0","is_exemplar":0,"bytes_present":9303683875,"data_complete":1,"withdrawn_at":null,"withdrawn_reason":null,"archive_complete":1,"archive_absent_files":0,"archive_declared_files":551,"zarr_pool_breaks":null,"total_recording_duration":133113,"recording_duration_min":2074,"recording_duration_max":3825,"recording_count":45,"recordings_unavailable":0,"recordings_measured":45,"channel_count_min":12,"channel_count_max":58,"sampling_frequency":256,"power_line_frequency":50,"eeg_reference":"right earlobe","placement_scheme":"5% grid system","sweep_stamps":"{\"enrichment_updated_at\":\"2026-08-18 18:15:05\",\"metadata_updated_at\":\"2026-08-18 18:17:34\",\"archive_checked_at\":\"2026-08-18 18:30:40\",\"zarr_checked_at\":null,\"records_checked_at\":\"2026-08-18 18:23:13\",\"citations_updated_at\":\"2026-09-08 03:00:47\",\"channel_montage_checked_at\":\"2026-06-28 22:55:37\",\"hed_checked_at\":\"2026-06-30 04:15:36\",\"data_checked_at\":null,\"availability_report_at\":\"2026-08-20 03:00:44\",\"recording_stats_at\":\"2026-09-02 11:31:46\",\"signal_defaults_at\":\"2026-09-02 11:41:45\"}","participants":45,"num_citations":51,"latest_version":"v1.0.3","zarr_verify_status":null,"zarr_verified_at":null,"owner_username":"bruaristimunha","owner_github":"bruAristimunha","file_size_formatted":"8.67 GB","zarr_data_failures":null,"zarr_index_url":"https://zarr.nemar.org/nm000178/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}}