{"dataset":{"id":"133","dataset_id":"nm000130","name":"Liu2022 – eldBETA SSVEP benchmark dataset for elderly population","description":"This dataset (eldBETA) provides 64-channel SSVEP-BCI EEG recordings from 100 elderly participants (aged 51-81, mean 63.17 years) performing a 9-target speller task using joint frequency and phase modulation (JFPM) stimuli. Each subject completed 7 sessions of 7 blocks with 9 trials each, recorded at 1000 Hz using a Synamps2 (Neuroscan) amplifier with a Cz reference. The dataset is intended as a benchmark for SSVEP-BCI algorithms in aging populations and has been converted to BIDS format with HED event annotations for use in the MOABB benchmarking framework.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000130","concept_doi":"10.82901/nemar.nm000130","latest_version_doi":"10.82901/nemar.nm000130.v1.0.3","created_at":"2026-03-06 21:34:15","updated_at":"2026-08-20 19:21:49","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"Liu2022 – eldBETA SSVEP benchmark dataset for elderly population\",\n  \"description\": \"This dataset (eldBETA) provides 64-channel SSVEP-BCI EEG recordings from 100 elderly participants (aged 51-81, mean 63.17 years) performing a 9-target speller task using joint frequency and phase modulation (JFPM) stimuli. Each subject completed 7 sessions of 7 blocks with 9 trials each, recorded at 1000 Hz using a Synamps2 (Neuroscan) amplifier with a Cz reference. The dataset is intended as a benchmark for SSVEP-BCI algorithms in aging populations and has been converted to BIDS format with HED event annotations for use in the MOABB benchmarking framework.\",\n  \"methods_description\": \"EEG was recorded from 64 channels at 1000 Hz using a Synamps2 (Neuroscan) amplifier with a standard_1005 montage and Cz reference. Participants performed a 9-target SSVEP speller task using joint frequency and phase modulation (JFPM) visual flicker stimuli, with frequencies ranging from 8.0 to 12.0 Hz in 0.5 Hz steps. Each trial consisted of a 4 s cue, 5 s of SSVEP stimulation, and 1 s rest; each subject completed 7 blocks of 9 trials across 7 sessions.\",\n  \"license\": \"CC BY 4.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Bingchuan Liu\": {\n      \"orcid\": \"0000-0001-5988-6051\"\n    },\n    \"Yijun Wang\": {},\n    \"Xiaorong Gao\": {},\n    \"Xiaogang Chen\": {}\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\": \"Electroencephalography\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D004569\"\n    },\n    {\n      \"term\": \"elderly\"\n    },\n    {\n      \"term\": \"aging\"\n    },\n    {\n      \"term\": \"benchmark\"\n    },\n    {\n      \"term\": \"JFPM\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      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eeg=64\n- **Montage**: standard_1005\n- **Hardware**: Synamps2 (Neuroscan)\n- **Reference**: Cz\n- **Line frequency**: 50.0 Hz\n- **Impedance threshold**: 20 kOhm\n\n## Participants\n\n- **Number of subjects**: 100\n- **Health status**: healthy\n- **Age**: mean=63.17, std=6.05, min=51, max=81\n- **Gender distribution**: male=33, female=67\n\n## Experimental Protocol\n\n- **Paradigm**: ssvep\n- **Task type**: 9-target SSVEP speller\n- **Number of classes**: 9\n- **Class labels**: 8, 9.5, 11, 8.5, 10, 11.5, 9, 10.5, 12\n- **Trial duration**: 5.0 s\n- **Feedback type**: visual\n- **Stimulus type**: JFPM visual flicker\n- **Stimulus modalities**: visual\n- **Primary modality**: visual\n- **Synchronicity**: synchronous\n- **Mode**: online\n- **Training/test split**: False\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  9.5\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/9_5\n\n  11\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/11\n\n  8.5\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/8_5\n\n  10\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/10\n\n  11.5\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/11_5\n\n  9\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/9\n\n  10.5\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/10_5\n\n  12\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/12\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] Hz\n- **Frequency resolution**: 0.5 Hz\n\n## Data Structure\n\n- **Trials**: 63\n- **Blocks per session**: 7\n\n## Signal Processing\n\n- **Classifiers**: TDCA, ms-eCCA, ensemble_msTRCA, ensemble_TRCA, Extended_CCA, ITCCA, L1MCCA, FBCCA, CVARS, tMSI, MEC, MSI, CCA\n- **Feature extraction**: TDCA, CCA, FBCCA, TRCA, ms-eCCA, msTRCA, Extended_CCA, ITCCA, L1MCCA, CVARS, tMSI, MEC, MSI\n- **Frequency bands**: bandpass=[6.0, 100.0] Hz\n- **Spatial filters**: TDCA, CCA, TRCA, ms-eCCA, msTRCA, Extended_CCA, ITCCA, L1MCCA, CVARS, MEC, MSI, tMSI\n\n## Cross-Validation\n\n- **Method**: leave-one-block-out\n- **Folds**: 7\n- **Evaluation type**: within_subject\n\n## BCI Application\n\n- **Applications**: speller\n- **Environment**: lab\n- **Online feedback**: True\n\n## Tags\n\n- **Pathology**: healthy\n- **Modality**: visual\n- **Type**: perception\n\n## Documentation\n\n- **DOI**: 10.1038/s41597-022-01372-9\n- **License**: CC BY 4.0\n- **Investigators**: Bingchuan Liu, Yijun Wang, Xiaorong Gao, Xiaogang Chen\n- **Senior author**: Xiaogang Chen\n- **Institution**: Tsinghua University\n- **Department**: Department of Biomedical Engineering, School of Medicine, Tsinghua University\n- **Country**: CN\n- **Repository**: Figshare\n- **Data URL**: https://doi.org/10.6084/m9.figshare.18032669\n- **Publication year**: 2022\n- **Funding**: National Natural Science Foundation of China (No. 62171473); Doctoral Brain+X Seed Grant Program of Tsinghua University; Strategic Priority Research Program of Chinese Academy of Sciences (No. XDB32040200)\n- **Ethics approval**: Institutional Review Board of Tsinghua University, No. 20210032\n- **Keywords**: SSVEP, BCI, EEG, elderly, aging, benchmark, JFPM\n\n## References\n\nB. Liu, Y. Wang, X. Gao, and X. Chen, \"eldBETA: A Large Eldercare-oriented Benchmark Database of SSVEP-BCI for the Aging Population,\" Scientific Data, vol. 9, p. 252, 2022. DOI: 10.1038/s41597-022-01372-9\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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