{"dataset":{"id":"78","dataset_id":"nm000108","name":"hyser_bids","description":null,"owner_user_id":2,"status":"active","github_repo":"nemarDatasets/nm000108","concept_doi":"10.82901/nemar.nm000108","latest_version_doi":"10.82901/nemar.nm000108.v1.0.1","created_at":"2026-02-14 06:12:01","updated_at":"2026-07-10 21:43:16","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"HySER: High-Density Surface Electromyogram Recordings\",\n  \"description\": \"HySER is a high-density surface electromyography (HD-sEMG) dataset comprising 256-channel recordings from 20 right-handed subjects performing five task types across two sessions. The dataset includes simultaneous 5-finger force measurements and covers discrete hand gesture recognition (34 gestures), maximum voluntary contractions, single-finger and multi-finger force tracking, and random contractions. This resource supports research in motor control, gesture recognition, and myoelectric signal processing.\",\n  \"methods_description\": \"EMG data were acquired using a Quattrocento system (OT Bioelettronica) at 2048 Hz sampling rate with 16-bit resolution. Four 8×8 gelled electrode arrays (5 mm × 2.8 mm, 10 mm inter-electrode spacing) were placed on the right forearm extensors and flexors. Hardware filtering included 10 Hz high-pass (2nd order) and 500 Hz low-pass. Simultaneous 5-finger force recordings were obtained at 100 Hz using force sensors. Two sessions per subject were separated by 3-25 days.\",\n  \"license\": \"ODC-By-1.0\",\n  \"dataset_type\": \"raw\",\n  \"authors\": {\n    \"Xinyu Jiang\": {\n      \"orcid\": \"0000-0002-8518-1415\",\n      \"affiliations\": [\n        {\n          \"name\": \"Center for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai 200433, China\"\n        }\n      ]\n    },\n    \"Chenyun Dai\": {\n      \"orcid\": \"0000-0002-3056-4339\",\n      \"affiliations\": [\n        {\n          \"name\": \"Center for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai 200433, China\"\n        }\n      ]\n    },\n    \"Jiahao Fan\": {\n      \"orcid\": \"0000-0003-0514-1323\",\n      \"affiliations\": [\n        {\n          \"name\": \"Center for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai 200433, China\"\n        }\n      ]\n    },\n    \"Xiangyu Liu\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"Electromyography\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D004576\"\n    },\n    {\n      \"term\": \"hand gestures\"\n    },\n    {\n      \"term\": \"motor control\"\n    },\n    {\n      \"term\": \"force tracking\"\n    },\n    {\n      \"term\": \"muscle activity\"\n    },\n    {\n      \"term\": \"signal processing\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.1109/TNSRE.2021.3082551\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.13026/ym7v-bh53\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000108\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataexplorer/detail?dataset_id=nm000108\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"Shanghai Municipal Science and Technology Major Project\",\n      \"award_number\": \"2017SHZDZX01\"\n    },\n    {\n      \"funder_name\": \"Shanghai Pujiang Program\",\n      \"award_number\": \"19PJ1401100\"\n    },\n    {\n      \"funder_name\": \"Natural Science Foundation of Shanghai\",\n      \"award_number\": \"20ZR1403400\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"EMG Dataset\",\n  \"modalities\": [\n    \"emg\"\n  ]\n}","last_activity_at":null,"source":null,"source_id":null,"subject_count":20,"modalities":"emg","age_min":21,"age_max":34,"file_size":116161750237,"total_files":6504,"tasks":"gesture01,gesture02,gesture03,gesture04,gesture05,gesture06,gesture07,gesture08,gesture09,gesture10,gesture11,gesture12,gesture13,gesture14,gesture15,gesture16,gesture17,gesture18,gesture19,gesture20,gesture21,gesture22,gesture23,gesture24,gesture25,gesture26,gesture27,gesture28,gesture29,gesture30,gesture31,gesture32,gesture33,gesture34,multifinger,mvc,random,singlefinger","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Xinyu Jiang, Chenyun Dai, Jiahao Fan, Xiangyu Liu","license":"ODC-By-1.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000108-blue)](https://doi.org/10.82901/nemar.nm000108)\n\n[![Paper DOI](https://img.shields.io/badge/Paper-10.1109/TNSRE.2021.3082551-blue)](https://doi.org/10.1109/TNSRE.2021.3082551)\n[![PhysioNet](https://img.shields.io/badge/PhysioNet-hd--semg%2F1.0.0-green)](https://physionet.org/content/hd-semg/1.0.0/)\n[![License](https://img.shields.io/badge/License-ODC--By--1.0-lightgrey)](https://opendatacommons.org/licenses/by/1-0/)\n\n# HySER: High-Density Surface Electromyogram Recordings\n\nBIDS-formatted version of the Hyser Surface EMG for Hand Gesture Recognition dataset (Jiang et al., 2021). 20 subjects performed 5 task types across 2 sessions using 256-channel high-density surface EMG (HD-sEMG) with simultaneous 5-finger force recordings.\n\n## Subjects\n\n20 right-handed participants (12M, 8F; age 21-34). Two sessions per subject separated by 3-25 days. Demographics in `participants.tsv`.\n\n## Tasks\n\n| Task | BIDS label | Description | Trials |\n|------|-----------|-------------|--------|\n| Pattern Recognition | `gesture01`-`gesture34` | 34 discrete hand gestures (Table I in paper) | 2 per gesture, each with 3 dynamic + 1 maintenance |\n| Maximum Voluntary Contraction | `mvc` | MVC flexion/extension per finger | 2 per finger, 10s each |\n| Single Finger (1-DOF) | `singlefinger` | Triangle force trajectory, individual fingers | 3 per finger, 25s each |\n| Multi-Finger (N-DOF) | `multifinger` | Simultaneous multi-finger force tracking | 2 per combination, 25s each |\n| Random | `random` | Free finger contractions at any force | 5 trials, 25s each |\n\n## Equipment\n\n- **EMG system:** Quattrocento (OT Bioelettronica, Torino, Italy), 2048 Hz, gain 150, 16-bit ADC\n- **Electrodes:** Four 8x8 gelled elliptical arrays (5mm x 2.8mm), 10mm inter-electrode distance\n- **Placement:** Two arrays on extensors (distal/proximal), two on flexors (distal/proximal) of right forearm\n- **Reference:** Olecranon (elbow); Ground: head of ulna (right leg drive)\n- **Hardware filters:** HP 10 Hz (2nd order), LP 500 Hz\n- **Force sensors:** SAS + HSGA (Huatran, Shenzhen, China), 100 Hz, 5 fingers\n\n## File Organization\n\n- `*_emg.bdf` - 256-channel EMG data (BDF format)\n- `*_physio.tsv.gz` - 5-finger force data (non-PR tasks only)\n- `*_channels.tsv` - Channel metadata with electrode mapping (`signal_electrode` column)\n- `*_electrodes.tsv` - Electrode positions in local grid coordinates (mm)\n- `*_events.tsv` - Event markers (gesture trials or segment boundaries)\n\nNon-PR tasks (MVC, singlefinger, multifinger, random) are merged from multiple original recordings into single files per session, with boundary events marking segment junctions.\n\n## Coordinate Systems\n\nFour local grid systems (`space-ed`, `space-ep`, `space-fd`, `space-fp`) in mm, anchored to a parent forearm system (`space-forearm`) in anatomical percent coordinates. See `space-*_coordsystem.json` files.\n\n## Missing Data\n\n6 gesture recordings absent in source dataset (not conversion failures):\nsub-01/ses-2/gesture25, sub-03/ses-1/gesture04, sub-03/ses-2/gesture04, sub-05/ses-1/gesture34, sub-11/ses-1/gesture08, sub-19/ses-2/gesture11.\n\n## Conversion\n\nConverted using EMG-2-BIDS (EEGLAB + bids-matlab-tools). Data integrity verified: mean Pearson correlation >0.9999 between original WFDB and converted BDF across all 1514 recordings. 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