{"dataset":{"id":"205","dataset_id":"nm000173","name":"Motor Imagery ataset from Ofner et al 2017","description":"This dataset comprises EEG recordings from 15 healthy subjects performing six different upper limb movements (elbow flexion/extension, forearm supination/pronation, hand open/close) and rest conditions in both movement execution and motor imagery modalities. The study investigates neural encoding of individual upper limb movements using low-frequency EEG signals (0.3-3 Hz) and achieves classification accuracies of 55-87% for executed movements and 27-73% for imagined movements. Source localization analysis identifies discriminative movement information in premotor areas, primary motor cortex, somatosensory cortex, and posterior parietal cortex, with applications toward non-invasive control of motor neuroprostheses and robotic arms.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000173","concept_doi":"10.82901/nemar.nm000173","latest_version_doi":"10.82901/nemar.nm000173.v1.0.3","created_at":"2026-03-23 14:45:56","updated_at":"2026-08-18 18:15:17","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"enriched\",\n  \"title\": \"Motor Imagery ataset from Ofner et al 2017\",\n  \"description\": \"This dataset comprises EEG recordings from 15 healthy subjects performing six different upper limb movements (elbow flexion/extension, forearm supination/pronation, hand open/close) and rest conditions in both movement execution and motor imagery modalities. The study investigates neural encoding of individual upper limb movements using low-frequency EEG signals (0.3-3 Hz) and achieves classification accuracies of 55-87% for executed movements and 27-73% for imagined movements. Source localization analysis identifies discriminative movement information in premotor areas, primary motor cortex, somatosensory cortex, and posterior parietal cortex, with applications toward non-invasive control of motor neuroprostheses and robotic arms.\",\n  \"methods_description\": \"EEG was recorded from 61 channels at 512 Hz sampling rate using g.tec medical engineering hardware with active sensors. Signals were filtered with 0.01-200 Hz bandpass (8th order Chebyshev) and 50 Hz notch filtering. Reference electrode was placed at right mastoid with ground at AFz. Subjects performed trial-based paradigms with visual cue presentation at 2s, executing or imagining sustained movements for 3 seconds. Classification employed shrinkage LDA with discriminative spatial patterns (DSP) features extracted from time-domain signals. Source localization used sLORETA analysis. Cross-validation employed 10x10-fold within-session evaluation.\",\n  \"license\": \"CC-BY-4.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Patrick Ofner\": {\n      \"orcid\": \"0000-0001-7169-4300\"\n    },\n    \"Andreas Schwarz\": {},\n    \"Joana Pereira\": {},\n    \"Gernot R. Müller-Putz\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"motor imagery\"\n    },\n    {\n      \"term\": \"movement execution\"\n    },\n    {\n      \"term\": \"upper limb movements\"\n    },\n    {\n      \"term\": \"brain-computer interfaces\"\n    },\n    {\n      \"term\": \"motor cortex\"\n    },\n    {\n      \"term\": \"neuroprosthesis\"\n    },\n    {\n      \"term\": \"low-frequency\"\n    },\n    {\n      \"term\": \"time-domain\"\n    },\n    {\n      \"term\": \"BCI\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.1371/journal.pone.0182578\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000173\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.5281/zenodo.834976\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsVersionOf\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/nm000173\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"European Commission\",\n      \"award_number\": \"H2020-643955\",\n      \"award_title\": \"MoreGrasp\"\n    },\n    {\n      \"funder_name\": \"European Research Council\",\n      \"award_number\": \"ERC-681231\",\n      \"award_title\": \"Feel Your Reach\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"36.5 GB (603 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".gdf\",\n    \".html\",\n    \".json\",\n    \".md\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"3e1ea7f08daf56ee1c73f30eea70f4113bea1a1cc71ef4dcb878e0a7a8dd0ae4\"\n}","last_activity_at":"2026-08-16 14:10:25","source":null,"source_id":null,"subject_count":15,"modalities":"eeg","age_min":2014,"age_max":2014,"file_size":36508043321,"total_files":2263,"tasks":"imagery","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Patrick Ofner, Andreas Schwarz, Joana Pereira, Gernot R. Müller-Putz","license":"CC-BY-4.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000173-blue)](https://doi.org/10.82901/nemar.nm000173)\n\n# Motor Imagery ataset from Ofner et al 2017\n\nMotor Imagery ataset from Ofner et al 2017.\n\n## Dataset Overview\n\n- **Code**: Ofner2017\n- **Paradigm**: imagery\n- **DOI**: 10.1371/journal.pone.0182578\n- **Subjects**: 15\n- **Sessions per subject**: 2\n- **Events**: right_elbow_flexion=1536, right_elbow_extension=1537, right_supination=1538, right_pronation=1539, right_hand_close=1540, right_hand_open=1541, rest=1542\n- **Trial interval**: [0, 3] s\n- **Runs per session**: 10\n- **Session IDs**: movement_execution, motor_imagery\n- **File format**: gdf\n\n## Acquisition\n\n- **Sampling rate**: 512.0 Hz\n- **Number of channels**: 61\n- **Channel types**: eeg=61, eog=3, misc=32\n- **Channel names**: C1, C2, C3, C4, C5, C6, CCP1h, CCP2h, CCP3h, CCP4h, CCP5h, CCP6h, CP1, CP2, CP3, CP4, CP5, CP6, CPP1h, CPP2h, CPP3h, CPP4h, CPP5h, CPP6h, CPz, Cz, F1, F2, F3, F4, FC1, FC2, FC3, FC4, FC5, FC6, FCC1h, FCC2h, FCC3h, FCC4h, FCC5h, FCC6h, FCz, FFC1h, FFC2h, FFC3h, FFC4h, FFC5h, FFC6h, FTT7h, FTT8h, Fz, P1, P2, P3, P4, PPO1h, PPO2h, Pz, TTP7h, TTP8h, armeodummy-0, armeodummy-1, armeodummy-10, armeodummy-11, armeodummy-12, armeodummy-2, armeodummy-3, armeodummy-4, armeodummy-5, armeodummy-6, armeodummy-7, armeodummy-8, armeodummy-9, eog-l, eog-m, eog-r, gesture, index_far, index_middle, index_near, litte_far, litte_near, middle_far, middle_near, middle_ring, pitch, ring_far, ring_little, ring_near, roll, thumb_far, thumb_index, thumb_near, thumb_palm, wrist_bend\n- **Montage**: standard_1005\n- **Hardware**: g.tec medical engineering GmbH\n- **Reference**: right mastoid\n- **Ground**: AFz\n- **Sensor type**: active\n- **Line frequency**: 50.0 Hz\n- **Online filters**: 0.01-200 Hz bandpass (8th order Chebyshev), 50 Hz notch\n\n## Participants\n\n- **Number of subjects**: 15\n- **Health status**: healthy\n- **Age**: mean=27.0, std=5.0, min=22.0, max=40.0\n- **Gender distribution**: female=9, male=6\n- **Handedness**: {'right': 14, 'left': 1}\n- **Species**: human\n\n## Experimental Protocol\n\n- **Paradigm**: imagery\n- **Number of classes**: 7\n- **Class labels**: right_elbow_flexion, right_elbow_extension, right_supination, right_pronation, right_hand_close, right_hand_open, rest\n- **Study design**: Trial-based paradigm with sustained movements/motor imagery. Each trial: fixation cross at 0s, cue presentation at 2s, sustained movement/MI execution. Subjects performed both movement execution (ME) and motor imagery (MI) in separate sessions.\n- **Feedback type**: none\n- **Stimulus type**: visual cue\n- **Synchronicity**: synchronous\n- **Mode**: offline\n- **Training/test split**: False\n- **Instructions**: Subjects were instructed to execute sustained movements in ME session and perform kinesthetic motor imagery in MI session. For rest class, subjects were instructed to avoid any movement and to stay in the starting position.\n\n## HED Event Annotations\n\nSchema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n\n```\n  right_elbow_flexion\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Imagine\n          ├─ Flex\n          └─ Right, Elbow\n\n  right_elbow_extension\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Imagine\n          ├─ Stretch\n          └─ Right, Elbow\n\n  right_supination\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Imagine\n          ├─ Turn\n          ├─ Right, Forearm\n          └─ Label/supination\n\n  right_pronation\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Imagine\n          ├─ Turn\n          ├─ Right, Forearm\n          └─ Label/pronation\n\n  right_hand_close\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Imagine\n          ├─ Close\n          └─ Right, Hand\n\n  right_hand_open\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Imagine\n          ├─ Open\n          └─ Right, Hand\n\n  rest\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Rest\n\n```\n## Paradigm-Specific Parameters\n\n- **Detected paradigm**: motor_imagery\n- **Imagery tasks**: elbow_flexion, elbow_extension, forearm_supination, forearm_pronation, hand_open, hand_close\n\n## Data Structure\n\n- **Trials**: 420\n- **Trials per class**: elbow_flexion=60, elbow_extension=60, forearm_supination=60, forearm_pronation=60, hand_open=60, hand_close=60, rest=60\n- **Trials context**: per_session\n\n## Preprocessing\n\n- **Preprocessing applied**: False\n\n## Signal Processing\n\n- **Classifiers**: sLDA\n- **Feature extraction**: time-domain signals, discriminative spatial patterns (DSP)\n- **Frequency bands**: analyzed=[0.3, 3.0] Hz\n- **Spatial filters**: sLORETA source localization\n\n## Cross-Validation\n\n- **Method**: 10x10-fold cross-validation\n- **Folds**: 10\n- **Evaluation type**: within-session\n\n## Performance (Original Study)\n\n- **Mov Vs Mov Me**: 55.0\n- **Mov Vs Rest Me**: 87.0\n- **Mov Vs Mov Mi**: 27.0\n- **Mov Vs Rest Mi**: 73.0\n\n## BCI Application\n\n- **Applications**: neuroprosthesis, robotic_arm\n- **Environment**: laboratory\n- **Online feedback**: False\n\n## Tags\n\n- **Pathology**: Healthy\n- **Modality**: Motor\n- **Type**: Motor Imagery, Motor Execution\n\n## Documentation\n\n- **DOI**: 10.1371/journal.pone.0182578\n- **Associated paper DOI**: 10.1371/journal.pone.0182578\n- **License**: CC-BY-4.0\n- **Investigators**: Patrick Ofner, Andreas Schwarz, Joana Pereira, Gernot R. Müller-Putz\n- **Senior author**: Gernot R. Müller-Putz\n- **Contact**: gernot.mueller@tugraz.at\n- **Institution**: Graz University of Technology\n- **Department**: Institute of Neural Engineering, BCI-Lab\n- **Country**: AT\n- **Repository**: BNCI Horizon 2020\n- **Data URL**: https://bnci-horizon-2020.eu/database/data-sets\n- **Publication year**: 2017\n- **Funding**: H2020-643955 MoreGrasp; ERC Consolidator Grant ERC-681231 Feel Your Reach\n- **Ethics approval**: Medical University of Graz, approval number 28-108 ex 15/16\n- **Acknowledgements**: Data are available from the BNCI Horizon 2020 database at http://bnci-horizon-2020.eu/database/data-sets (accession number 001-2017) and from Zenodo at DOI 10.5281/zenodo.834976\n- **Keywords**: upper limb movements, EEG, motor imagery, movement execution, low-frequency, time-domain, BCI, neuroprosthesis\n\n## Abstract\n\nHow neural correlates of movements are represented in the human brain is of ongoing interest and has been researched with invasive and non-invasive methods. In this study, we analyzed the encoding of single upper limb movements in the time-domain of low-frequency electroencephalography (EEG) signals. Fifteen healthy subjects executed and imagined six different sustained upper limb movements. We classified these six movements and a rest class and obtained significant average classification accuracies of 55% (movement vs movement) and 87% (movement vs rest) for executed movements, and 27% and 73%, respectively, for imagined movements. Furthermore, we analyzed the classifier patterns in the source space and located the brain areas conveying discriminative movement information. The classifier patterns indicate that mainly premotor areas, primary motor cortex, somatosensory cortex and posterior parietal cortex convey discriminative movement information. The decoding of single upper limb movements is specially interesting in the context of a more natural non-invasive control of e.g., a motor neuroprosthesis or a robotic arm in highly motor disabled persons.\n\n## Methodology\n\nSubjects performed 6 sustained upper limb movements (elbow flexion/extension, forearm supination/pronation, hand open/close) plus rest in two separate sessions (movement execution and motor imagery). EEG was recorded from 61 channels, filtered to 0.3-3 Hz, and classified using shrinkage LDA with discriminative spatial patterns. Source localization was performed using sLORETA. Classification employed both single time-point and time-window approaches with 10x10-fold cross-validation.\n\n## References\n\nOfner, P., Schwarz, A., Pereira, ","bids_version":"1.9.0","sessions_count":2,"publish_date":"2026-05-11 18:04:36","embedding_dirty":0,"license_tier":"attribution","zarr_status":"failed","zarr_converted_at":null,"zarr_store_count":null,"zarr_index_etag":null,"zarr_source_commit":null,"archive_status":"ready","archive_size":20847000463,"archive_retry_count":0,"records_status":"ready","archive_skip_reason":null,"zarr_errors":300,"zarr_failure_count":300,"zarr_deterministic":1,"zarr_failed_at":"2026-09-02 09:51:03","num_dataset_citations":0,"num_datapaper_citations":94,"n_channels":61,"electrode_system":"10-05","has_hed":1,"hed_version":"8.4.0","is_exemplar":0,"bytes_present":36505046001,"data_complete":1,"withdrawn_at":null,"withdrawn_reason":null,"archive_complete":1,"archive_absent_files":0,"archive_declared_files":2263,"zarr_pool_breaks":0,"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":512,"power_line_frequency":50,"eeg_reference":"right mastoid","placement_scheme":"10-05 system","sweep_stamps":"{\"enrichment_updated_at\":\"2026-08-18 18:12:37\",\"metadata_updated_at\":\"2026-08-18 18:15:16\",\"archive_checked_at\":\"2026-08-18 18:40:47\",\"zarr_checked_at\":\"2026-06-07 17:58:25\",\"records_checked_at\":\"2026-08-18 18:22:14\",\"citations_updated_at\":\"2026-09-08 03:00:46\",\"channel_montage_checked_at\":\"2026-06-28 22:55:13\",\"hed_checked_at\":\"2026-06-30 04:15:05\",\"data_checked_at\":null,\"availability_report_at\":\"2026-08-20 03:00:32\",\"recording_stats_at\":null,\"signal_defaults_at\":\"2026-09-02 11:41:25\"}","participants":15,"num_citations":94,"latest_version":"v1.0.3","zarr_verify_status":null,"zarr_verified_at":null,"owner_username":"bruaristimunha","owner_github":"bruAristimunha","file_size_formatted":"34.00 GB","zarr_data_failures":{"count":300,"detail_ref":"zarr/index.json","compacted_by":"migration_0074"},"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}}