SSVEP MAMEM 1 dataset

SSVEP MAMEM 1 dataset.

Dataset Overview

• Code: MAMEM1
• Paradigm: ssvep
• DOI: 10.48550/arXiv.1602.00904
• Subjects: 11
• Sessions per subject: 1
• Events: 6.66=1, 7.50=2, 8.57=3, 10.00=4, 12.00=5
• Trial interval: [1, 4] s
• File format: MATLAB .mat

Acquisition

• Sampling rate: 250.0 Hz
• Number of channels: 256
• Channel types: eeg=256
• 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
• Montage: GSN-HydroCel-256
• Hardware: EGI 300 Geodesic EEG System (GES 300)
• Line frequency: 50.0 Hz
• Impedance threshold: 80.0 kOhm
• Cap manufacturer: EGI
• Cap model: HydroCel Geodesic Sensor Net (HCGSN)

Participants

• Number of subjects: 11
• Health status: healthy
• Clinical population: able-bodied subjects without any known neuro-muscular or mental disorders
• Age: min=24, max=39
• Gender distribution: male=8, female=3
• Handedness: {'right': 10, 'left': 1}
• Species: human

Experimental Protocol

• Paradigm: ssvep
• Number of classes: 5
• Class labels: 6.66, 7.50, 8.57, 10.00, 12.00
• Trial duration: 5.0 s
• 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.
• Feedback type: none
• Stimulus type: flickering box
• Stimulus modalities: visual
• Primary modality: visual
• Synchronicity: synchronous
• Mode: offline
• Instructions: Subjects were instructed to focus attention on the flickering box, limit movements, and avoid swallowing or blinking during visual stimulation
• Stimulus presentation: SoftwareName=Microsoft Visual Studio 2010 with OpenGL, monitor=22 inch LCD monitor, refresh_rate=60 Hz, resolution=1680x1080 pixels

HED Event Annotations

Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser

  6.66
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/6_66

  7.50
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/7_50

  8.57
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/8_57

  10.00
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/10_00

  12.00
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Label/12_00

Paradigm-Specific Parameters

• Detected paradigm: ssvep
• Stimulus frequencies: [6.66, 7.5, 8.57, 10.0, 12.0] Hz
• Number of targets: 5
• Number of repetitions: 3

Data Structure

• Trials: 1104
• 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.

Preprocessing

• Data state: raw
• Preprocessing applied: False

Signal Processing

• Classifiers: LDA, SVM, Random Forest, kNN, Naive Bayes, CCA, AdaBoost, Decision Trees
• Feature extraction: Periodogram, Welch Spectrum, Goertzel algorithm, Yule-AR Spectrum, FFT, PSD, Discrete Wavelet Transform
• Frequency bands: analyzed=[5.0, 48.0] Hz
• Spatial filters: CAR, CSP, Minimum Energy

Cross-Validation

• Method: leave-one-subject-out
• Evaluation type: cross_subject

Performance (Original Study)

• Default Accuracy: 72.47
• Optimal Accuracy: 79.47

BCI Application

• Applications: communication
• Environment: laboratory
• Online feedback: False

Tags

• Pathology: Healthy
• Modality: Visual
• Type: Perception

Documentation

• Description: Comparative evaluation of state-of-the-art algorithms for SSVEP-based BCIs
• DOI: 10.6084/m9.figshare.2068677.v1
• Associated paper DOI: 10.48550/arXiv.1602.00904
• License: ODC-By-1.0
• Investigators: Vangelis P. Oikonomou, Georgios Liaros, Kostantinos Georgiadis, Elisavet Chatzilari, Katerina Adam, Spiros Nikolopoulos, Ioannis Kompatsiaris
• Senior author: Ioannis Kompatsiaris
• Institution: Centre for Research and Technology Hellas (CERTH)
• Country: GR
• Repository: Figshare
• Data URL: https://dx.doi.org/10.6084/m9.figshare.2068677.v1
• Publication year: 2016
• Funding: H2020-ICT-2014-644780
• Ethics approval: Centre for Research and Technology Hellas ethics committee, dated 3/7/2015, grant H2020-ICT-2014-644780
• Keywords: SSVEP, BCI, EEG, brain-computer interface, comparative evaluation, state-of-the-art algorithms

Abstract

Brain-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.

Methodology

Empirical 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.

References

Oikonomou, 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.

MAMEM Steady State Visually Evoked Potential EEG Database <https://archive.physionet.org/physiobank/database/mssvepdb/>_

S. Nikolopoulos, 2016, DataAcquisitionDetails.pdf <https://figshare.com/articles/dataset/MAMEM_EEG_SSVEP_Dataset_I_256_channels_11_subjects_5_frequencies_/2068677?file=3793738>_ Appelhoff, 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

Pernet, 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. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8

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