{"dataset":{"id":"127","dataset_id":"nm000124","name":"Han2024 – SSVEP fatigue dataset with two frequency paradigms","description":"This dataset contains 64-channel EEG recordings from 24 healthy subjects performing two SSVEP-BCI paradigms (low-frequency 8-15.5 Hz and high-frequency 25.5-33 Hz, 16 targets each) using JFPM visual flicker stimulation. Each subject completed training and fatigue-inducing sessions, enabling study of how mental fatigue affects SSVEP-BCI performance and classification accuracy. The dataset was converted to BIDS format using MOABB and is intended to support research on dynamic stopping strategies and fatigue-aware BCI algorithms.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000124","concept_doi":"10.82901/nemar.nm000124","latest_version_doi":"10.82901/nemar.nm000124.v1.0.1","created_at":"2026-03-06 21:19:37","updated_at":"2026-08-18 22:40:43","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"Han2024 – SSVEP fatigue dataset with two frequency paradigms\",\n  \"description\": \"This dataset contains 64-channel EEG recordings from 24 healthy subjects performing two SSVEP-BCI paradigms (low-frequency 8-15.5 Hz and high-frequency 25.5-33 Hz, 16 targets each) using JFPM visual flicker stimulation. Each subject completed training and fatigue-inducing sessions, enabling study of how mental fatigue affects SSVEP-BCI performance and classification accuracy. The dataset was converted to BIDS format using MOABB and is intended to support research on dynamic stopping strategies and fatigue-aware BCI algorithms.\",\n  \"methods_description\": \"EEG was recorded at 1000 Hz using a 64-channel Synamps2 amplifier (Neuroscan) with standard_1005 montage, referenced to Cz, with an online bandpass filter of 0.15-200 Hz. Subjects performed gaze-shifting SSVEP tasks with JFPM-encoded visual flicker targets arranged in a 4x4 matrix, across a training phase (6 blocks per frequency condition) and a fatigue phase (24 blocks per condition), with 2 s stimulation per trial.\",\n  \"license\": \"CC BY 4.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Yuheng Han\": {\n      \"orcid\": \"0009-0003-2474-3300\",\n      \"affiliations\": [\n        {\n          \"name\": \"Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China\"\n        }\n      ]\n    },\n    \"Yufeng Ke\": {\n      \"orcid\": \"0000-0002-8434-0322\",\n      \"affiliations\": [\n        {\n          \"name\": \"Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China\"\n        }\n      ]\n    },\n    \"Ruiyan Wang\": {\n      \"orcid\": \"0009-0007-4758-0507\",\n      \"affiliations\": [\n        {\n          \"name\": \"Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China\"\n        }\n      ]\n    },\n    \"Tao Wang\": {\n      \"orcid\": \"0000-0001-9085-7240\",\n      \"affiliations\": [\n        {\n          \"name\": \"Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China\"\n        }\n      ]\n    },\n    \"Dong Ming\": {\n      \"orcid\": \"0000-0002-8192-2538\",\n      \"affiliations\": [\n        {\n          \"name\": \"Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China\"\n        }\n      ]\n    }\n  },\n  \"keywords\": [\n    {\n      \"term\": \"SSVEP\"\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\": \"fatigue\"\n    },\n    {\n      \"term\": \"dynamic stopping\"\n    },\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"Electroencephalography\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D004569\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.1109/TNSRE.2024.3380635\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000124\",\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/nm000124\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": 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13:24:38","source":null,"source_id":null,"subject_count":24,"modalities":"eeg","age_min":null,"age_max":null,"file_size":35490530374,"total_files":637,"tasks":"ssvep","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Yuheng Han, Yufeng Ke, Ruiyan Wang, Tao Wang, Dong Ming","license":"CC BY 4.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000124-blue)](https://doi.org/10.82901/nemar.nm000124)\n\n# SSVEP fatigue dataset with two frequency paradigms\n\nSSVEP fatigue dataset with two frequency paradigms.\n\n## Dataset Overview\n\n- **Code**: Han2024Fatigue\n- **Paradigm**: ssvep\n- **DOI**: 10.1109/TNSRE.2024.3380635\n- **Subjects**: 24\n- **Sessions per subject**: 2\n- **Events**: 8=1, 8.5=2, 9=3, 9.5=4, 10=5, 10.5=6, 11=7, 11.5=8, 12=9, 12.5=10, 13=11, 13.5=12, 14=13, 14.5=14, 15=15, 15.5=16, 25.5=17, 26=18, 26.5=19, 27=20, 27.5=21, 28=22, 28.5=23, 29=24, 29.5=25, 30=26, 30.5=27, 31=28, 31.5=29, 32=30, 32.5=31, 33=32\n- **Trial interval**: [0.14, 2.14] s\n- **File format**: MAT\n\n## Acquisition\n\n- **Sampling rate**: 1000.0 Hz\n- **Number of channels**: 64\n- **Channel types**: eeg=64\n- **Channel names**: Fp1, Fpz, Fp2, AF3, AF4, 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, M1, TP7, CP5, CP3, CP1, CPz, CP2, CP4, CP6, TP8, M2, P7, P5, P3, P1, Pz, P2, P4, P6, P8, PO7, PO5, PO3, POz, PO4, PO6, PO8, CB1, O1, Oz, O2, CB2\n- **Montage**: standard_1005\n- **Hardware**: Synamps2 (Neuroscan)\n- **Reference**: Cz\n- **Ground**: midway between Fz and FPz\n- **Line frequency**: 50.0 Hz\n- **Online filters**: {'bandpass_hz': [0.15, 200.0]}\n- **Impedance threshold**: 10 kOhm\n\n## Participants\n\n- **Number of subjects**: 24\n- **Health status**: healthy\n- **Age**: min=18, max=26\n- **Gender distribution**: male=12, female=12\n\n## Experimental Protocol\n\n- **Paradigm**: ssvep\n- **Task type**: gaze-shifting\n- **Number of classes**: 32\n- **Class labels**: 8, 8.5, 9, 9.5, 10, 10.5, 11, 11.5, 12, 12.5, 13, 13.5, 14, 14.5, 15, 15.5, 25.5, 26, 26.5, 27, 27.5, 28, 28.5, 29, 29.5, 30, 30.5, 31, 31.5, 32, 32.5, 33\n- **Trial duration**: 2.0 s\n- **Feedback type**: none\n- **Stimulus type**: JFPM visual flicker\n- **Stimulus modalities**: visual\n- **Primary modality**: visual\n- **Synchronicity**: synchronous\n- **Mode**: offline\n- **Training/test split**: True\n\n## HED Event Annotations\n\nSchema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n\n```\n  8\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/8\n\n  8.5\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/8_5\n\n  9\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/9\n\n  9.5\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/9_5\n\n  10\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/10\n\n  10.5\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/10_5\n\n  11\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/11\n\n  11.5\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/11_5\n\n  12\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/12\n\n  12.5\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/12_5\n\n  13\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/13\n\n  13.5\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/13_5\n\n  14\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/14\n\n  14.5\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/14_5\n\n  15\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/15\n\n  15.5\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/15_5\n\n  25.5\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/25_5\n\n  26\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/26\n\n  26.5\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/26_5\n\n  27\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/27\n\n  27.5\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/27_5\n\n  28\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/28\n\n  28.5\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/28_5\n\n  29\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/29\n\n  29.5\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/29_5\n\n  30\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/30\n\n  30.5\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/30_5\n\n  31\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/31\n\n  31.5\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/31_5\n\n  32\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/32\n\n  32.5\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/32_5\n\n  33\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/33\n\n```\n## Paradigm-Specific Parameters\n\n- **Detected paradigm**: ssvep\n- **Stimulus frequencies**: [8.0, 8.5, 9.0, 9.5, 10.0, 10.5, 11.0, 11.5, 12.0, 12.5, 13.0, 13.5, 14.0, 14.5, 15.0, 15.5, 25.5, 26.0, 26.5, 27.0, 27.5, 28.0, 28.5, 29.0, 29.5, 30.0, 30.5, 31.0, 31.5, 32.0, 32.5, 33.0] Hz\n- **Frequency resolution**: 0.5 Hz\n\n## Data Structure\n\n- **Trials**: 960 per frequency band (16 targets x 60 blocks)\n- **Blocks per session**: 60\n- **Trials context**: 6 training + 24 fatigue blocks per frequency condition\n\n## Preprocessing\n\n- **Data state**: epoched\n\n## Signal Processing\n\n- **Classifiers**: TRCA\n- **Spatial filters**: TRCA\n\n## BCI Application\n\n- **Environment**: lab\n- **Online feedback**: False\n\n## Tags\n\n- **Pathology**: healthy\n- **Modality**: visual\n- **Type**: perception\n\n## Documentation\n\n- **DOI**: 10.1109/TNSRE.2024.3380635\n- **License**: CC BY 4.0\n- **Investigators**: Yuheng Han, Yufeng Ke, Ruiyan Wang, Tao Wang, Dong Ming\n- **Senior author**: Dong Ming\n- **Institution**: Tianjin University\n- **Department**: Academy of Medical Engineering and Translational Medicine, Tianjin University\n- **Country**: CN\n- **Repository**: Zenodo\n- **Data URL**: https://zenodo.org/records/10507229\n- **Publication year**: 2024\n- **Funding**: National Key Research and Development Program of China (Grant 2021YFF1200603); National Natural Science Foundation of China (Grants 62276184, 61806141)\n- **Ethics approval**: Research Ethics Committee of Tianjin University\n- **Keywords**: SSVEP, BCI, fatigue, dynamic stopping, EEG\n\n## References\n\nY. Han, Y. Ke, R. Wang, T. Wang, and D. Ming, \"Enhancing SSVEP-BCI Performance Under Fatigue State Using Dynamic Stopping Strategy,\" IEEE Trans. Neural Syst. Rehab. Eng., vol. 32, pp. 1407-1415, 2024. DOI: 10.1109/TNSRE.2024.3380635\nAppelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Hochenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896\n\nPernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. 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