{"dataset":{"id":"193","dataset_id":"nm000162","name":"BNCI 2025-001 Motor Kinematics Reaching dataset","description":"This dataset comprises preprocessed electroencephalography (EEG) recordings from 20 healthy participants performing a motor imagery task involving discrete reaching movements in four directions (up, down, left, right) with varying speeds and distances. Participants executed 960 trials across 10 blocks while viewing visual cues, with concurrent eye-tracking and motion capture data. The dataset includes extensive preprocessing with artifact correction, source localization, and classification features, making it suitable for brain-computer interface research and motor imagery decoding studies.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000162","concept_doi":"10.82901/nemar.nm000162","latest_version_doi":"10.82901/nemar.nm000162.v1.0.2","created_at":"2026-03-23 03:32:39","updated_at":"2026-08-18 18:13:54","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"BNCI 2025-001 Motor Kinematics Reaching dataset\",\n  \"description\": \"This dataset comprises preprocessed electroencephalography (EEG) recordings from 20 healthy participants performing a motor imagery task involving discrete reaching movements in four directions (up, down, left, right) with varying speeds and distances. Participants executed 960 trials across 10 blocks while viewing visual cues, with concurrent eye-tracking and motion capture data. The dataset includes extensive preprocessing with artifact correction, source localization, and classification features, making it suitable for brain-computer interface research and motor imagery decoding studies.\",\n  \"methods_description\": \"EEG data were acquired at 500 Hz using a 67-channel BrainAmp system (Zebris ELPOS cap) with 4 EOG channels. Participants performed a cue-paced center-out reaching task with visual feedback. Preprocessing included low-pass filtering (100 Hz), notch filtering (50 Hz), downsampling (200 Hz), bad channel rejection and interpolation, bandpass filtering (0.3-80 Hz), eye artifact correction via SGEYESUB, ICA with FastICA, IC artifact removal, and common average reference. Final analysis used 55 channels after removing frontal and EOG channels. Bad trials were rejected based on amplitude thresholds (>200 µV) or movement criteria (incorrect direction, no movement, duration <0.2s or >4s, movement onset <0.5s after cue).\",\n  \"license\": \"CC-BY-4.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Nitikorn Srisrisawang\": {\n      \"orcid\": \"0000-0001-9541-2777\"\n    },\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\": \"reaching movements\"\n    },\n    {\n      \"term\": \"source localization\"\n    },\n    {\n      \"term\": \"eye artifact correction\"\n    },\n    {\n      \"term\": \"independent component analysis\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.1088/1741-2552/ada0ea\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000162\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/nm000162\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"Royal Thai Government (scholar funding for N.S.)\"\n    },\n    {\n      \"funder_name\": \"BioTechMed Graz\"\n    },\n    {\n      \"funder_name\": \"Royal Thai Government\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"16.5 GB (43 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".html\",\n    \".json\",\n    \".md\",\n    \".set\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"be6ebf84711b9f0b9e525f6d537f3488e6d741a073bd778e0a6ee17d516c515b\"\n}","last_activity_at":"2026-08-16 13:27:57","source":null,"source_id":null,"subject_count":20,"modalities":"eeg","age_min":26.1,"age_max":26.1,"file_size":16513967243,"total_files":253,"tasks":"imagery","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Nitikorn Srisrisawang, Gernot R Müller-Putz","license":"CC-BY-4.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000162-blue)](https://doi.org/10.82901/nemar.nm000162)\n\n# BNCI 2025-001 Motor Kinematics Reaching dataset\n\nBNCI 2025-001 Motor Kinematics Reaching dataset.\n\n## Dataset Overview\n\n- **Code**: BNCI2025-001\n- **Paradigm**: imagery\n- **DOI**: 10.1088/1741-2552/ada0ea\n- **Subjects**: 20\n- **Sessions per subject**: 1\n- **Events**: up_slow_near=1, up_slow_far=2, up_fast_near=3, up_fast_far=4, down_slow_near=5, down_slow_far=6, down_fast_near=7, down_fast_far=8, left_slow_near=9, left_slow_far=10, left_fast_near=11, left_fast_far=12, right_slow_near=13, right_slow_far=14, right_fast_near=15, right_fast_far=16\n- **Trial interval**: [0, 4] s\n- **File format**: EEG (BrainAmp)\n- **Data preprocessed**: True\n\n## Acquisition\n\n- **Sampling rate**: 500.0 Hz\n- **Number of channels**: 67\n- **Channel types**: eeg=67, eog=4\n- **Channel names**: AF3, AF4, AF7, AF8, AFz, C1, C2, C3, C4, C5, C6, CP1, CP2, CP3, CP4, CP5, CP6, CPz, Cz, EOGL1, EOGL2, EOGL3, EOGR1, F1, F2, F3, F4, F5, F6, F7, F8, FC1, FC2, FC3, FC4, FC5, FC6, FCz, FT7, FT8, Fz, O1, O2, Oz, P1, P2, P3, P4, P5, P6, P7, P8, PO3, PO4, PO7, PO8, POz, PPO1h, PPO2h, Pz, T7, T8, TP7, TP8, targetPosX, targetPoxY, validity, vx, vy, x, y\n- **Montage**: af7 af3 afz af4 af8 f7 f5 f3 f1 fz f2 f4 f6 f8 ft7 fc5 fc3 fc1 fcz fc2 fc4 fc6 ft8 t7 c5 c3 c1 cz c2 c4 c6 t8 tp7 cp5 cp3 cp1 cpz cp2 cp4 cp6 tp8 p7 p5 p3 p1 pz p2 p4 p6 p8 ppo1h ppo2h po7 po3 poz po4 po8 o1 oz o2\n- **Hardware**: BrainAmp\n- **Software**: EEGLAB\n- **Reference**: common average\n- **Sensor type**: EEG\n- **Line frequency**: 50.0 Hz\n- **Online filters**: 50 Hz notch\n- **Cap manufacturer**: Zebris Medical GmbH\n- **Cap model**: ELPOS\n- **Auxiliary channels**: EOG (4 ch, horizontal, vertical)\n\n## Participants\n\n- **Number of subjects**: 20\n- **Health status**: patients\n- **Clinical population**: Healthy\n- **Age**: mean=26.1, std=4.1\n- **Gender distribution**: male=12, female=8\n- **Handedness**: {'right': 17, 'left': 3}\n- **Species**: human\n\n## Experimental Protocol\n\n- **Paradigm**: imagery\n- **Task type**: discrete reaching\n- **Number of classes**: 16\n- **Class labels**: up_slow_near, up_slow_far, up_fast_near, up_fast_far, down_slow_near, down_slow_far, down_fast_near, down_fast_far, left_slow_near, left_slow_far, left_fast_near, left_fast_far, right_slow_near, right_slow_far, right_fast_near, right_fast_far\n- **Tasks**: discrete reaching\n- **Study design**: Four-direction center-out reaching task with varying speeds (quick/slow) and distances (near/far) following visual cue, self-paced execution with eye fixation on cue\n- **Feedback type**: visual (cue color: green for correct, red for incorrect direction)\n- **Stimulus type**: visual cue\n- **Stimulus modalities**: visual\n- **Primary modality**: visual\n- **Synchronicity**: cue-paced\n- **Mode**: both\n- **Instructions**: Follow cue with eyes, wait at least 1s after cue stops, mimic movement while fixating eyes on cue, move smoothly with whole arm avoiding wrist rotation\n\n## HED Event Annotations\n\nSchema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n\n```\n  up_slow_near\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Reach\n          ├─ Upward\n          ├─ Label/slow\n          └─ Label/near\n\n  up_slow_far\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Reach\n          ├─ Upward\n          ├─ Label/slow\n          └─ Label/far\n\n  up_fast_near\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Reach\n          ├─ Upward\n          ├─ Label/fast\n          └─ Label/near\n\n  up_fast_far\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Reach\n          ├─ Upward\n          ├─ Label/fast\n          └─ Label/far\n\n  down_slow_near\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Reach\n          ├─ Downward\n          ├─ Label/slow\n          └─ Label/near\n\n  down_slow_far\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Reach\n          ├─ Downward\n          ├─ Label/slow\n          └─ Label/far\n\n  down_fast_near\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Reach\n          ├─ Downward\n          ├─ Label/fast\n          └─ Label/near\n\n  down_fast_far\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Reach\n          ├─ Downward\n          ├─ Label/fast\n          └─ Label/far\n\n  left_slow_near\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Reach\n          ├─ Left\n          ├─ Label/slow\n          └─ Label/near\n\n  left_slow_far\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Reach\n          ├─ Left\n          ├─ Label/slow\n          └─ Label/far\n\n  left_fast_near\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Reach\n          ├─ Left\n          ├─ Label/fast\n          └─ Label/near\n\n  left_fast_far\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Reach\n          ├─ Left\n          ├─ Label/fast\n          └─ Label/far\n\n  right_slow_near\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Reach\n          ├─ Right\n          ├─ Label/slow\n          └─ Label/near\n\n  right_slow_far\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Reach\n          ├─ Right\n          ├─ Label/slow\n          └─ Label/far\n\n  right_fast_near\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Reach\n          ├─ Right\n          ├─ Label/fast\n          └─ Label/near\n\n  right_fast_far\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Reach\n          ├─ Right\n          ├─ Label/fast\n          └─ Label/far\n\n```\n## Paradigm-Specific Parameters\n\n- **Detected paradigm**: motor_imagery\n- **Number of targets**: 4\n- **Imagery tasks**: right_hand_reaching\n\n## Data Structure\n\n- **Trials**: 960\n- **Trials per class**: up=240, down=240, left=240, right=240\n- **Blocks per session**: 10\n- **Block duration**: 1200.0 s\n- **Trials context**: per_participant (before rejection)\n\n## Preprocessing\n\n- **Data state**: preprocessed with eye artifact correction\n- **Preprocessing applied**: True\n- **Steps**: low-pass filter at 100 Hz, notch filter at 50 Hz, downsampling to 200 Hz, bad channel rejection and interpolation, bandpass filter 0.3-80 Hz, eye artifact correction via SGEYESUB, ICA with FastICA algorithm, IC artifact removal, low-pass filter at 3 Hz, downsampling to 10 Hz, bad trial rejection, common average reference\n- **Highpass filter**: 0.3 Hz\n- **Lowpass filter**: 100.0 Hz\n- **Bandpass filter**: {'low_cutoff_hz': 0.3, 'high_cutoff_hz': 80.0}\n- **Notch filter**: [50] Hz\n- **Filter type**: Butterworth\n- **Filter order**: 2\n- **Artifact methods**: ICA, SGEYESUB (Sparse Generalized Eye Artifact Subspace Subtraction), IClabel plugin\n- **Re-reference**: common average\n- **Downsampled to**: 200.0 Hz\n- **Epoch window**: [-3.0, 4.0]\n- **Notes**: Frontal channels (AF7, AF3, AFz, AF4, AF8) and EOG removed prior to CAR to reduce residual eye artifacts. Final analysis used 55 channels. Eye blocks recorded separately for SGEYESUB model training. Bad trials rejected based on amplitude >200 µV or standard deviation >5SD. Movement-related bad trials rejected for incorrect direction, no movement, duration <0.2s or >4s, or movement initiated <0.5s after cue stop.\n\n## Signal Processing\n\n- **Classifiers**: sLDA (shrinkage Linear Discriminant Analysis)\n- **Feature extraction**: Low-frequency EEG (0.3-3 Hz), Source localization (sLORETA), ICA, ROI-based features\n- **Frequency bands**: delta=[0.3, 3.0] Hz; analyzed=[0.3, 100.0] Hz\n- **Spatial filters**: Common Average Reference, Source-space projection\n\n## Cross-Validation\n\n- **Method**: stratified k-fold\n- **Folds**: 10\n- **Evaluation type**: within_sessio","bids_version":"1.9.0","sessions_count":1,"publish_date":"2026-05-11 15:43:22","embedding_dirty":0,"license_tier":"attribution","zarr_status":"ready","zarr_converted_at":"2026-09-04 12:21:18","zarr_store_count":20,"zarr_index_etag":"8a65339620650ac72dbecba307dc9219","zarr_source_commit":"59816ef54de934cf5c0e881cfa0ecb795d9f5908","archive_status":"ready","archive_size":9721867908,"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":3,"n_channels":67,"electrode_system":"10-05","has_hed":1,"hed_version":"8.4.0","is_exemplar":0,"bytes_present":16512912127,"data_complete":1,"withdrawn_at":null,"withdrawn_reason":null,"archive_complete":1,"archive_absent_files":0,"archive_declared_files":253,"zarr_pool_breaks":0,"total_recording_duration":160025,"recording_duration_min":7092,"recording_duration_max":8814,"recording_count":20,"recordings_unavailable":0,"recordings_measured":20,"channel_count_min":67,"channel_count_max":67,"sampling_frequency":500,"power_line_frequency":50,"eeg_reference":"common average","placement_scheme":"af7 af3 afz af4 af8 f7 f5 f3 f1 fz f2 f4 f6 f8 ft7 fc5 fc3 fc1 fcz fc2 fc4 fc6 ft8 t7 c5 c3 c1 cz c2 c4 c6 t8 tp7 cp5 cp3 cp1 cpz cp2 cp4 cp6 tp8 p7 p5 p3 p1 pz p2 p4 p6 p8 ppo1h ppo2h po7 po3 poz po4 po8 o1 oz o2","sweep_stamps":"{\"enrichment_updated_at\":\"2026-08-18 18:11:45\",\"metadata_updated_at\":\"2026-08-18 18:13:52\",\"archive_checked_at\":\"2026-08-18 18:26:21\",\"zarr_checked_at\":\"2026-06-07 17:58:24\",\"records_checked_at\":\"2026-08-18 18:20:10\",\"citations_updated_at\":\"2026-09-09 03:00:14\",\"channel_montage_checked_at\":\"2026-06-28 22:54:08\",\"hed_checked_at\":\"2026-06-30 04:13:57\",\"data_checked_at\":null,\"availability_report_at\":\"2026-08-20 03:00:11\",\"signal_defaults_at\":\"2026-09-02 11:40:24\",\"recording_stats_at\":\"2026-09-05 03:00:51\",\"zarr_verify_attempted_at\":\"2026-09-09 03:02:02\",\"zarr_verified_at\":\"2026-09-09 03:02:02\",\"zarr_verified_commit\":\"59816ef54de934cf5c0e881cfa0ecb795d9f5908\",\"zarr_verify_status\":\"verified\",\"zarr_verify_examples\":[],\"zarr_verify_sampled\":20.0,\"zarr_verify_checked\":20.0,\"zarr_verify_checked_channels\":20.0,\"zarr_verify_checked_duration\":20.0,\"zarr_verify_checked_rate\":20.0,\"zarr_verify_unchecked\":0.0,\"zarr_verify_mismatch_count\":0.0,\"zarr_verify_examples_truncated\":0.0}","participants":20,"num_citations":3,"latest_version":"v1.0.2","zarr_verify_status":"verified","zarr_verified_at":"2026-09-09 03:02:02","owner_username":"bruaristimunha","owner_github":"bruAristimunha","file_size_formatted":"15.38 GB","zarr_data_failures":null,"zarr_index_url":"https://zarr.nemar.org/nm000162/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}}