{"dataset":{"id":"123","dataset_id":"nm000120","name":"Oikonomou2016 – SSVEP MAMEM 2 dataset","description":"A 256-channel EEG dataset from 11 healthy subjects performing a steady-state visually evoked potential (SSVEP) brain-computer interface task. Subjects focused attention on flickering visual stimuli at five different frequencies (6.66, 7.50, 8.57, 10.00, 12.00 Hz) to select commands. The dataset includes 1,104 trials acquired at 250 Hz sampling rate and was used to systematically evaluate state-of-the-art signal processing algorithms for SSVEP-based BCIs, achieving 74.42% mean accuracy with optimized configurations.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000120","concept_doi":"10.82901/nemar.nm000120","latest_version_doi":"10.82901/nemar.nm000120.v1.0.2","created_at":"2026-03-06 21:14:55","updated_at":"2026-08-18 18:11:23","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 2 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 focused attention on flickering visual stimuli at five different frequencies (6.66, 7.50, 8.57, 10.00, 12.00 Hz) to select commands. The dataset includes 1,104 trials acquired at 250 Hz sampling rate and was used to systematically evaluate state-of-the-art signal processing algorithms for SSVEP-based BCIs, achieving 74.42% mean accuracy with optimized configurations.\",\n  \"methods_description\": \"EEG signals were recorded from 11 healthy subjects using an EGI 300 Geodesic EEG System with a 256-channel HydroCel Geodesic Sensor Net montage at 250 Hz sampling rate. Subjects performed a visual attention task focusing on flickering boxes presented at five simultaneous frequencies. Each trial consisted of 5 seconds of visual stimulation followed by 5 seconds of rest. Data were acquired in a single session per subject with an initial adaptation period. Signal processing evaluation included filtering (FIR vs IIR), artifact removal (AMUSE vs FastICA), feature extraction (PWelch, Periodogram, PYULEAR, DWT, STFT, Goertzel), feature selection, and classification using multiple algorithms (SVM, LDA, kNN, Naive Bayes, Random Forest, AdaBoost).\",\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\": \"brain-computer interfaces\"\n    },\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"visual evoked potentials\"\n    },\n    {\n      \"term\": \"signal processing\"\n    },\n    {\n      \"term\": \"feature extraction\"\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\": \"https://github.com/nemarDatasets/nm000120\",\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\": \"https://nemar.org/dataset/nm000120\",\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    \"10.4 GB (123 files)\"\n  ],\n  \"formats\": [\n    \".html\",\n    \".json\",\n    \".md\",\n    \".set\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"85c8043a308341a88855525c24731982c942b14bbc294a7409de046a726d1de6\"\n}","last_activity_at":"2026-08-16 13:24:59","source":null,"source_id":null,"subject_count":11,"modalities":"eeg","age_min":null,"age_max":null,"file_size":10441216797,"total_files":464,"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.nm000120-blue)](https://doi.org/10.82901/nemar.nm000120)\n\n# SSVEP MAMEM 2 dataset\n\nSSVEP MAMEM 2 dataset.\n\n## Dataset Overview\n\n- **Code**: MAMEM2\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- **Runs per session**: 5\n- **File format**: 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- **Reference**: Cz\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- **Age**: min=24, max=39\n- **Gender distribution**: male=8, female=3\n- **Handedness**: {'right': 10, 'left': 1}\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 visual stimuli flickering at different frequencies (6.66, 7.50, 8.57, 10.00, 12.00 Hz) to select commands. Each stimulus presented for 5 seconds followed by 5 seconds rest.\n- **Feedback type**: none\n- **Stimulus type**: flickering box\n- **Stimulus modalities**: visual\n- **Primary modality**: visual\n- **Synchronicity**: synchronous\n- **Mode**: offline\n- **Stimulus presentation**: SoftwareName=Microsoft Visual Studio 2010 with OpenGL, device=22 inch LCD monitor, refresh_rate=60 Hz, resolution=1680x1080\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**: Each session includes 23 trials (8 adaptation trials excluded from analysis). 5 sessions per subject (with exceptions: S001=3 sessions, S003=4 sessions, S004=4 sessions). Total: 1104 trials of 5 seconds each.\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, AdaBoost, Decision Trees, CCA\n- **Feature extraction**: PWelch, Periodogram, FFT, Goertzel, PYULEAR (Yule-AR), STFT, DWT, PSD, Wavelet, Spectrogram\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- **Accuracy**: 74.42%\n- **Mean Accuracy Default Config**: 72.47\n- **Mean Accuracy Optimal Config**: 74.42\n- **Processing Time Msec**: 68\n\n## BCI Application\n\n- **Applications**: command_selection\n- **Environment**: laboratory\n- **Online feedback**: False\n\n## Tags\n\n- **Pathology**: Healthy\n- **Modality**: Visual\n- **Type**: Research\n\n## Documentation\n\n- **DOI**: 10.48550/arXiv.1602.00904\n- **Associated paper DOI**: arXiv:1602.00904v2\n- **License**: ODC-By-1.0\n- **Investigators**: Vangelis P. Oikonomou, Georgios Liaros, Kostantinos Georgiadis, Elisavet Chatzilari, Katerina Adam, Spiros Nikolopoulos, Ioannis Kompatsiaris\n- **Institution**: Centre for Research and Technology Hellas (CERTH)\n- **Country**: GR\n- **Repository**: GitHub\n- **Data URL**: https://figshare.com/articles/dataset/3153409\n- **Publication year**: 2016\n- **Funding**: H2020-ICT-2014-644780\n- **Ethics approval**: Approved by ethics committee of Centre for Research and Technology Hellas, date 3/7/2015, grant H2020-ICT-2014-644780\n- **Keywords**: SSVEP, BCI, brain-computer interface, EEG, visual evoked potentials, signal processing, feature extraction, classification\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 study focuses on SSVEP-based BCIs and performs a comparative evaluation of state-of-the-art algorithms for filtering, artifact removal, feature extraction, feature selection and classification. Dataset consists of 256-channel EEG signals from 11 subjects with 5 flickering frequencies (6.66, 7.50, 8.57, 10.00, 12.00 Hz).\n\n## Methodology\n\nLeave-one-subject-out cross-validation was used to evaluate a general-purpose BCI system without subject-specific training. Systematic comparison of algorithms across all signal processing stages: (1) Signal filtering: FIR vs IIR filters; (2) Artifact removal: AMUSE vs FastICA; (3) Feature extraction: PWelch, Periodogram, PYULEAR, DWT, STFT, Goertzel; (4) Feature selection: entropy-based methods and PCA/SVD; (5) Classification: SVM, LDA, KNN, Naive Bayes, Random Forest, AdaBoost. Optimal configuration achieved 74.42% mean accuracy using IIR-Elliptic filter, AMUSE artifact removal, PWelch feature extraction with nfft=512, segment length=350, overlap=0.75, and channel-138.\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_II_256_channels_11_subjects_5_frequencies_presented_simultaneously_/3153409?file=4911931>`_\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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