{
"Name": "Oikonomou2016 – SSVEP MAMEM 1 dataset",
"BIDSVersion": "1.9.0",
"HEDVersion": "8.4.0",
"DatasetType": "derivative",
"License": "ODC-By-1.0",
"Authors": [
"Vangelis P. Oikonomou",
"Georgios Liaros",
"Kostantinos Georgiadis",
"Elisavet Chatzilari",
"Katerina Adam",
"Spiros Nikolopoulos",
"Ioannis Kompatsiaris"
],
"Funding": [
"H2020-ICT-2014-644780"
],
"EthicsApprovals": [
"Centre for Research and Technology Hellas ethics committee, dated 3/7/2015, grant H2020-ICT-2014-644780"
],
"ReferencesAndLinks": [
"https://dx.doi.org/10.6084/m9.figshare.2068677.v1",
"10.48550/arXiv.1602.00904"
],
"GeneratedBy": [
{
"CodeURL": "https://github.com/NeuroTechX/moabb",
"Name": "moabb",
"Description": "Mother of All BCI Benchmarks",
"Version": "1.4.3"
}
],
"SourceDatasets": [
{
"DOI": "10.48550/arXiv.1602.00904",
"URL": "https://dx.doi.org/10.6084/m9.figshare.2068677.v1"
}
],
"Keywords": [
"SSVEP",
"BCI",
"EEG",
"brain-computer interface",
"comparative evaluation",
"state-of-the-art algorithms"
],
"PublicationYear": 2016,
"ExperimentName": "Oikonomou2016",
"Description": "SSVEP MAMEM 1 dataset.\n\n\nDataset summary:\n\n#Subj 10\n#Chan 256\n#Classes 5\n#Trials / class 12-15\nTrials length 3 s\nFreq 250 Hz\n#Sessions 1\n\n\nParticipants:\n\n- Population: healthy\n- Clinical population: able-bodied subjects without any known neuro-muscular or mental disorders\n- Handedness: {'right': 10, 'left': 1}\n\n\nEquipment:\n\n- Amplifier: EGI 300 Geodesic EEG System (GES 300)\n- Montage: GSN-HydroCel-256\n\n\nPreprocessing:\n\n- Data state: raw\n\n\nData Access:\n\n- DOI: 10.6084/m9.figshare.2068677.v1\n- Data URL: https://dx.doi.org/10.6084/m9.figshare.2068677.v1\n- Repository: Figshare\n\n\nExperimental Protocol:\n\n- Paradigm: ssvep\n- Feedback: none\n- Stimulus: flickering box\n\nDataset from [1]_.\n\nEEG signals with 256 channels captured from 11 subjects executing a\nSSVEP-based experimental protocol. Five different frequencies\n(6.66, 7.50, 8.57, 10.00 and 12.00 Hz) have been used for the visual\nstimulation,and the EGI 300 Geodesic EEG System, using a\nstimulation, HydroCel Geodesic Sensor Net (HCGSN) and a sampling rate of\n250 Hz has been used for capturing the signals.\n\nCheck the technical report [2]_ for more detail.\nFrom [1]_, subjects were exposed to non-overlapping flickering lights from five\nmagenta boxes with frequencies [6.66Hz, 7.5Hz, 8.57Hz 10Hz and 12Hz].\n256 channel EEG recordings were captured.\n\nEach session of the experimental procedure consisted of the following:\n\n1. 100 seconds of rest.\n2. An adaptation period in which the subject is exposed to eight\n5 second windows of flickering from a magenta box. Each flickering\nwindow is of a single isolated frequency, randomly chosen from the\nabove set, specified in the FREQUENCIES1.txt file under\n'adaptation'. The individual flickering windows are separated by 5\nseconds of rest.\n3. 30 seconds of rest.\n4. For each of the frequencies from the above set in ascending order,\nalso specified in FREQUENCIES1.txt under 'main trials':\n\n1. Three 5 second windows of flickering at the chosen frequency,\nseparated by 5 seconds of rest.\n2. 30 seconds of rest.\n\nThis gives a total of 15 flickering windows, or 23 including the\nadaptation period.\n\nThe order of chosen frequencies is the same for each session, although\nthere are small-moderate variations in the actual frequencies of each\nindividual window. The .freq annotations list the different frequencies at\na higher level of precision.\n\nNote: Each 'session' in experiment 1 includes an adaptation period, unlike\nexperiment 2 and 3 where each subject undergoes only one adaptation period\nbefore their first 'session'.\n\nFrom [3]_:\n\nEligible signals: The EEG signal is sensitive to external factors that have\nto do with the environment or the configuration of the acquisition setup\nThe research stuff was responsible for the elimination of trials that were\nconsidered faulty. As a result the following sessions were noted and\nexcluded from further analysis:\n1. S003, during session 4 the stimulation program crashed\n2. S004, during session 2 the stimulation program crashed, and\n3. S008, during session 4 the Stim Tracker was detuned.\nFurthermore, we must also note that subject S001 participated in 3 sessions\nand subjects S003 and S004 participated in 4 sessions, compared to all\nother subjects that participated in 5 sessions (NB: in fact, there is only\n3 sessions for subjects 1, 3 and 8, and 4 sessions for subject 4 available\nto download). As a result, the utilized dataset consists of 1104 trials of\n5 seconds each.\n\nFlickering frequencies: Usually the refresh rate for an LCD Screen is 60 Hz\ncreating a restriction to the number of frequencies that can be selected.\nSpecifically, only the frequencies that when divided with the refresh rate\nof the screen result in an integer quotient could be selected. As a result,\nthe frequendies that could be obtained were the following: 30.00. 20.00,\n15.00, 1200, 10.00, 857. 7.50 and 6.66 Hz. In addition, it is also\nimportant to avoid using frequencies that are multiples of another\nfrequency, for example making the choice to use 10.00Hz prohibits the use\nof 20.00 and 30.00 Mhz. With the previously described limitations in mind,\nthe selected frequencies for the experiment were: 12.00, 10.00, 8.57, 7.50\nand 6.66 Hz.\n\nStimuli Layout: In an effort to keep the experimental process as simple as\npossible, we used only one flickering box instead of more common choices,\nsuch as 4 or 5 boxes flickering simultaneously The fact that the subject\ncould focus on one stimulus without having the distraction of other\nflickering sources allowed us to minimize the noise of our signals and\nverify the appropriateness of our acquisition setup Nevertheless, having\nconcluded the optimal configuration for analyzing the EEG signals, the\nexperiment will be repeated with more concurrent visual stimulus.\n\nTrial duration: The duration of each trial was set to 5 seconds, as this\ntime was considered adequate to allow the occipital part of the bran to\nmimic the stimulation frequency and still be small enough for making a\nselection in the context\n\nReferences\n----------\n.. [1] Oikonomou, V. P., Liaros, G., Georgiadis, K., Chatzilari, E., Adam, K.,\nNikolopoulos, S., & Kompatsiaris, I. (2016). Comparative evaluation of\nstate-of-the-art algorithms for SSVEP-based BCIs. arXiv preprint\narXiv:1602.00904.\n.. [2] MAMEM Steady State Visually Evoked Potential EEG Database\n``_\n.. [3] S. Nikolopoulos, 2016, DataAcquisitionDetails.pdf\n``_",
"DatasetDOI": "10.82901/nemar.nm000119",
"Version": "1.0.2"
}