{"dataset":{"id":"122","dataset_id":"nm000119","name":"Oikonomou2016 – SSVEP MAMEM 1 dataset","description":"A 256-channel EEG dataset from 11 healthy subjects performing a steady-state visually evoked potential (SSVEP) brain-computer interface task. Subjects attended to flickering visual stimuli at five frequencies (6.66, 7.50, 8.57, 10.00, and 12.00 Hz) presented sequentially, with 1104 total trials recorded at 250 Hz sampling rate. This dataset was used for comparative evaluation of state-of-the-art signal processing algorithms and classification methods for SSVEP-based BCIs.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000119","concept_doi":"10.82901/nemar.nm000119","latest_version_doi":"10.82901/nemar.nm000119.v1.0.2","created_at":"2026-03-06 21:12:26","updated_at":"2026-08-16 13:17:27","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"Oikonomou2016 – SSVEP MAMEM 1 dataset\",\n  \"description\": \"A 256-channel EEG dataset from 11 healthy subjects performing a steady-state visually evoked potential (SSVEP) brain-computer interface task. Subjects attended to flickering visual stimuli at five frequencies (6.66, 7.50, 8.57, 10.00, and 12.00 Hz) presented sequentially, with 1104 total trials recorded at 250 Hz sampling rate. This dataset was used for comparative evaluation of state-of-the-art signal processing algorithms and classification methods for SSVEP-based BCIs.\",\n  \"methods_description\": \"EEG signals were acquired using an EGI 300 Geodesic EEG System with a 256-channel GSN-HydroCel montage at 250 Hz sampling rate. Subjects focused attention on a single violet flickering box presented on a 22-inch LCD monitor (60 Hz refresh rate, 1680x1080 resolution). Each trial consisted of 5 seconds of visual stimulation at a single frequency followed by 5 seconds of rest, with 3 repetitions per frequency and 30 seconds rest between frequencies. Data were acquired in raw, unpreprocessed format.\",\n  \"license\": \"ODC-By-1.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Vangelis P. Oikonomou\": {},\n    \"Georgios Liaros\": {},\n    \"Kostantinos Georgiadis\": {},\n    \"Elisavet Chatzilari\": {},\n    \"Katerina Adam\": {},\n    \"Spiros Nikolopoulos\": {},\n    \"Ioannis Kompatsiaris\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"SSVEP\"\n    },\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"brain-computer interface\"\n    },\n    {\n      \"term\": \"visual evoked potentials\"\n    },\n    {\n      \"term\": \"signal processing\"\n    },\n    {\n      \"term\": \"classification algorithms\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.48550/arXiv.1602.00904\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"10.6084/m9.figshare.2068677.v1\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000119\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/nm000119\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"European Commission\",\n      \"award_number\": \"H2020-ICT-2014-644780\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"12.3 GB (96 files)\"\n  ],\n  \"formats\": [\n    \".html\",\n    \".json\",\n    \".md\",\n    \".set\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"b26ef8e86f9269b155728aa68b763756f02877503dca2fa9added4f943f0cfda\"\n}","last_activity_at":"2026-08-15 20:37:58","source":null,"source_id":null,"subject_count":11,"modalities":"eeg","age_min":24,"age_max":39,"file_size":12255770504,"total_files":397,"tasks":"ssvep","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Vangelis P. Oikonomou, Georgios Liaros, Kostantinos Georgiadis, Elisavet Chatzilari, Katerina Adam, Spiros Nikolopoulos, Ioannis Kompatsiaris","license":"ODC-By-1.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000119-blue)](https://doi.org/10.82901/nemar.nm000119)\n\n# SSVEP MAMEM 1 dataset\n\nSSVEP MAMEM 1 dataset.\n\n## Dataset Overview\n\n- **Code**: MAMEM1\n- **Paradigm**: ssvep\n- **DOI**: 10.48550/arXiv.1602.00904\n- **Subjects**: 11\n- **Sessions per subject**: 1\n- **Events**: 6.66=1, 7.50=2, 8.57=3, 10.00=4, 12.00=5\n- **Trial interval**: [1, 4] s\n- **File format**: MATLAB .mat\n\n## Acquisition\n\n- **Sampling rate**: 250.0 Hz\n- **Number of channels**: 256\n- **Channel types**: eeg=256\n- **Channel names**: E1, E10, E100, E101, E102, E103, E104, E105, E106, E107, E108, E109, E11, E110, E111, E112, E113, E114, E115, E116, E117, E118, E119, E12, E120, E121, E122, E123, E124, E125, E126, E127, E128, E129, E13, E130, E131, E132, E133, E134, E135, E136, E137, E138, E139, E14, E140, E141, E142, E143, E144, E145, E146, E147, E148, E149, E15, E150, E151, E152, E153, E154, E155, E156, E157, E158, E159, E16, E160, E161, E162, E163, E164, E165, E166, E167, E168, E169, E17, E170, E171, E172, E173, E174, E175, E176, E177, E178, E179, E18, E180, E181, E182, E183, E184, E185, E186, E187, E188, E189, E19, E190, E191, E192, E193, E194, E195, E196, E197, E198, E199, E2, E20, E200, E201, E202, E203, E204, E205, E206, E207, E208, E209, E21, E210, E211, E212, E213, E214, E215, E216, E217, E218, E219, E22, E220, E221, E222, E223, E224, E225, E226, E227, E228, E229, E23, E230, E231, E232, E233, E234, E235, E236, E237, E238, E239, E24, E240, E241, E242, E243, E244, E245, E246, E247, E248, E249, E25, E250, E251, E252, E253, E254, E255, E256, E26, E27, E28, E29, E3, E30, E31, E32, E33, E34, E35, E36, E37, E38, E39, E4, E40, E41, E42, E43, E44, E45, E46, E47, E48, E49, E5, E50, E51, E52, E53, E54, E55, E56, E57, E58, E59, E6, E60, E61, E62, E63, E64, E65, E66, E67, E68, E69, E7, E70, E71, E72, E73, E74, E75, E76, E77, E78, E79, E8, E80, E81, E82, E83, E84, E85, E86, E87, E88, E89, E9, E90, E91, E92, E93, E94, E95, E96, E97, E98, E99\n- **Montage**: GSN-HydroCel-256\n- **Hardware**: EGI 300 Geodesic EEG System (GES 300)\n- **Line frequency**: 50.0 Hz\n- **Impedance threshold**: 80.0 kOhm\n- **Cap manufacturer**: EGI\n- **Cap model**: HydroCel Geodesic Sensor Net (HCGSN)\n\n## Participants\n\n- **Number of subjects**: 11\n- **Health status**: healthy\n- **Clinical population**: able-bodied subjects without any known neuro-muscular or mental disorders\n- **Age**: min=24, max=39\n- **Gender distribution**: male=8, female=3\n- **Handedness**: {'right': 10, 'left': 1}\n- **Species**: human\n\n## Experimental Protocol\n\n- **Paradigm**: ssvep\n- **Number of classes**: 5\n- **Class labels**: 6.66, 7.50, 8.57, 10.00, 12.00\n- **Trial duration**: 5.0 s\n- **Study design**: Subjects focus attention on a single violet box flickering at different frequencies (6.66, 7.50, 8.57, 10.00, 12.00 Hz) presented sequentially. Each frequency is presented for 5 seconds (trial) followed by 5 seconds rest, repeated 3 times per frequency, with 30 seconds rest between different frequencies.\n- **Feedback type**: none\n- **Stimulus type**: flickering box\n- **Stimulus modalities**: visual\n- **Primary modality**: visual\n- **Synchronicity**: synchronous\n- **Mode**: offline\n- **Instructions**: Subjects were instructed to focus attention on the flickering box, limit movements, and avoid swallowing or blinking during visual stimulation\n- **Stimulus presentation**: SoftwareName=Microsoft Visual Studio 2010 with OpenGL, monitor=22 inch LCD monitor, refresh_rate=60 Hz, resolution=1680x1080 pixels\n\n## HED Event Annotations\n\nSchema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n\n```\n  6.66\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/6_66\n\n  7.50\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/7_50\n\n  8.57\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/8_57\n\n  10.00\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/10_00\n\n  12.00\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/12_00\n\n```\n## Paradigm-Specific Parameters\n\n- **Detected paradigm**: ssvep\n- **Stimulus frequencies**: [6.66, 7.5, 8.57, 10.0, 12.0] Hz\n- **Number of targets**: 5\n- **Number of repetitions**: 3\n\n## Data Structure\n\n- **Trials**: 1104\n- **Trials context**: Total 1104 trials across all subjects. Each session includes 23 trials (8 adaptation + 15 main). S001: 3 sessions, S003 and S004: 4 sessions, others: 5 sessions. Some sessions excluded due to technical issues.\n\n## Preprocessing\n\n- **Data state**: raw\n- **Preprocessing applied**: False\n\n## Signal Processing\n\n- **Classifiers**: LDA, SVM, Random Forest, kNN, Naive Bayes, CCA, AdaBoost, Decision Trees\n- **Feature extraction**: Periodogram, Welch Spectrum, Goertzel algorithm, Yule-AR Spectrum, FFT, PSD, Discrete Wavelet Transform\n- **Frequency bands**: analyzed=[5.0, 48.0] Hz\n- **Spatial filters**: CAR, CSP, Minimum Energy\n\n## Cross-Validation\n\n- **Method**: leave-one-subject-out\n- **Evaluation type**: cross_subject\n\n## Performance (Original Study)\n\n- **Default Accuracy**: 72.47\n- **Optimal Accuracy**: 79.47\n\n## BCI Application\n\n- **Applications**: communication\n- **Environment**: laboratory\n- **Online feedback**: False\n\n## Tags\n\n- **Pathology**: Healthy\n- **Modality**: Visual\n- **Type**: Perception\n\n## Documentation\n\n- **Description**: Comparative evaluation of state-of-the-art algorithms for SSVEP-based BCIs\n- **DOI**: 10.6084/m9.figshare.2068677.v1\n- **Associated paper DOI**: 10.48550/arXiv.1602.00904\n- **License**: ODC-By-1.0\n- **Investigators**: Vangelis P. Oikonomou, Georgios Liaros, Kostantinos Georgiadis, Elisavet Chatzilari, Katerina Adam, Spiros Nikolopoulos, Ioannis Kompatsiaris\n- **Senior author**: Ioannis Kompatsiaris\n- **Institution**: Centre for Research and Technology Hellas (CERTH)\n- **Country**: GR\n- **Repository**: Figshare\n- **Data URL**: https://dx.doi.org/10.6084/m9.figshare.2068677.v1\n- **Publication year**: 2016\n- **Funding**: H2020-ICT-2014-644780\n- **Ethics approval**: Centre for Research and Technology Hellas ethics committee, dated 3/7/2015, grant H2020-ICT-2014-644780\n- **Keywords**: SSVEP, BCI, EEG, brain-computer interface, comparative evaluation, state-of-the-art algorithms\n\n## Abstract\n\nBrain-computer interfaces (BCIs) have been gaining momentum in making human-computer interaction more natural, especially for people with neuro-muscular disabilities. This report focuses on SSVEP-based BCIs and performs a comparative evaluation of the most promising algorithms. A dataset of 256-channel EEG signals from 11 subjects is provided, along with a processing toolbox for reproducing results and supporting further experimentation.\n\n## Methodology\n\nEmpirical approach where each signal processing parameter (filtering, artifact removal, feature extraction, feature selection, classification) is studied independently by keeping all other parameters fixed. Leave-one-subject-out cross-validation used to evaluate system without subject-specific training. Multiple algorithms compared for each processing stage to obtain state-of-the-art baseline.\n\n## References\n\nOikonomou, V. P., Liaros, G., Georgiadis, K., Chatzilari, E., Adam, K., Nikolopoulos, S., & Kompatsiaris, I. (2016). Comparative evaluation of state-of-the-art algorithms for SSVEP-based BCIs. arXiv preprint arXiv:1602.00904.\n\nMAMEM Steady State Visually Evoked Potential EEG Database `<https://archive.physionet.org/physiobank/database/mssvepdb/>`_\n\nS. Nikolopoulos, 2016, DataAcquisitionDetails.pdf `<https://figshare.com/articles/dataset/MAMEM_EEG_SSVEP_Dataset_I_256_channels_11_subjects_5_frequencies_/2068677?file=3793738>`_\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. 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