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19 results for “SSVEP”
MAMEM EEG SSVEP Dataset III (14 channels, 11 subjects, 5 frequencies presented simultaneously)
<p>EEG signals with 14 channels captured from 11 subjects executing a SSVEP-based experimental protocol. Five different frequencies (6.66, 7.50, 8.57, 10.00 and 12.00 Hz) have been used for the visual stimulation, and the Emotiv EPOC, using 14 wireless channels has been used for capturing the signals</p>
MAMEM EEG SSVEP Dataset II (256 channels, 11 subjects, 5 frequencies presented simultaneously)
<p>EEG signals with 256 channels captured from 11 subjects executing a SSVEP-based experimental protocol. Five different frequencies (6.66, 7.50, 8.57, 10.00 and 12.00 Hz) have been used for the visual stimulation, and the EGI 300 Geodesic EEG System (GES 300), using a 256-channel HydroCel Geodesic Sensor Net (HCGSN) and a sampling rate of 250 Hz has been used for capturing the signals.</p>
MAMEM EEG SSVEP Dataset I (256 channels, 11 subjects, 5 frequencies presented in isolation)
<p>EEG signals with 256 channels captured from 11 subjects executing a SSVEP-based experimental protocol. Five different frequencies (6.66, 7.50, 8.57, 10.00 and 12.00 Hz) have been used for the visual stimulation, and the EGI 300 Geodesic EEG System (GES 300), using a 256-channel HydroCel Geodesic Sensor Net (HCGSN) and a sampling rate of 250 Hz has been used for capturing the signals. </p>
The Sensitivity of SSVEPs to Variations in Numerical Material for Automatic Processing of Small Magnitudes
<p>We investigated the human ability to automatically process small magnitude information, using an oddball fast-periodic visual stimulation paradigm featuring a periodic alteration of magnitude (2 vs. 4) at a frequency of 1.25 Hz. Participants were exposed to various types of numerical content, such as biological (fingers), analogical (dots), and symbolic (Arabic digits), presented either in their conventional format (canonical) or in alternative formats (non-canonical), all synchronized at a base rate of 6.25 Hz. Our primary objective was to ascertain the sensitivity of steady-state visual evoked potentials (SSVEPs) to subtle small magnitude variations in relation to the specific type of numerical material.</p><p>SSVEPs were consistently observed at the base rate, corresponding to the presentation of the visual stimuli. Variations across conditions in terms of their location is examined, as well as the amplitude of the SSVEPs which is influenced by the type of material presented. Additionally, oddball SSVEPs emerged at a frequency of 1.25 Hz (and its harmonics) for each numerical content, suggesting the ability to discriminating the change of magnitude in each instance. However, the neural response exhibited distinctive characteristics based on the type of material presented. </p><p>These findings demonstrate that SSVEPs, while maintaining consistency in their presence across conditions, exhibit a sensitivity to variations in the type of numerical material, shedding light on the neural processes involved in the automatic processing of small magnitude information.</p>
SSVEP database elicited by four visual stimuli types
<ul> <li>The database consists of 108 electroencephalographic files from 27 participants performing a 5-target selection task. </li> <li>Each participant performed one experimental session.</li> <li>All datasets were collected on channels PO7, PO3, POz, PO4, PO8, O1, Oz, and O2, according to the 10–20 EEG electrode placement standard.</li> <li>For the visual stimuli, we consider the On-Off and Checkerboard patterns with luminance modulated by rectangular and sinusoidal functions, resulting in a total of four types of visual stimuli: Checkerboard pattern with the rectangular modulated signal (Sxx-C.txt); Checkerboard pattern with sinusoidal modulated signal (Sxx-mC.txt); On-Off pattern with sinusoidal modulated signal (Sxx-mOO.txt) and On-Off pattern with rectangular modulated signal (Sxx-OO.txt), where "Sxx" represents the subject number and 01 <= xx <= 27.</li> <li>In each file columns 1 to 8 correspond to EEG data and column 9 corresponds to the marks channel.</li> <li>Each phase of the experiment block is identified with a marker.</li> <li>The phases of one experiment trial are Fixation(201), Target Presentation(202), Preparation(203), Stimulation(101-105), and Rest(200).</li> <li>Marker numbers 101, 102, 103, 104, and 105, encodes de target frequency applied during the "Stimulation" stage in a trial. They are associated with the stimulation frequencies as follows: 101 - 24 Hz; 102 - 20 Hz; 103 - 15 Hz; 104 - 10.909 Hz and, 105 - 8.57 Hz</li> <li>Files can be easily accessible with EEG-dedicated MATLAB toolboxes, such as Fieldtrip and EEGLAB.</li> </ul>
Single-flicker online SSVEP BCI datset
<p>The zip archive contains EEG data from a newly developed brain-computer interface paradigm. The method is described in [1], and the dataset has been recorded for the application described in [2]. The two folders in the archive contain data from the training (offline) session and from the online session.</p> <p>Training data: Each subject has two training sessions. The .xdf files were generated by the Lab Streaming Layer (https://github.com/sccn/labstreaminglayer) from EEG data that were recorded using a BioSemi 32 channel amplifier. The files name after (subject_number)_(session_num).xdf. Each file includes 200 trials (50 trials for each direction).</p> <p>Online data: Each file contains data from one round of the game. The file name pattern is (subject_num)_(repeat_num) _(ramdon_background_num).mat. The Matlab data structure contains the following fields:<br> data. fsample: sampling rate (512 Hz)<br> data.trial: EEG signals for each step<br> data.time: time series of EEG signals for each step<br> data.class: classifier output for each step</p> <p>[1] Maye A, Zhang D, Engel AK (2017) "<a href="https://ieeexplore.ieee.org/abstract/document/7911330">Utilizing Retinotopic Mapping for a Multi-Target SSVEP BCI With a Single Flicker Frequency</a>", IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 7, pp. 1026-1036, July 2017, doi: 10.1109/TNSRE.2017.2666479.</p> <p>[2] Chen J, Zhang D, Engel AK, Gong Q, Maye A (2017) "<a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0178385">Application of a single-flicker online SSVEP BCI</a>" for spatial navigation", PLoS ONE, 12(5): e0178385. <a href="https://doi.org/10.1371/journal.pone.0178385">https://doi.org/10.1371/journal.pone.0178385</a></p>
Hyperscanning brain-computer interface based on synchronous and asynchronous interindividual SSVEP signals
<div> <p>Here we provide hyperscanning EEG (electroencephalogram) data recorded during BCI (brain-computer interface) control. The BCI was intended for the decoding of brain synchrony during visual stimulation, specifically the stimuli flickered at two different fequencies.<br>Each of seven pairs of participants performed more than 100 trials, which included 5s visual stimulation of synchronous or asynchronous flicker. See the PDF file for detailed description.</p> </div>
BRAIN Journal-Novel Detection Features for SSVEP Based BCI: Coefficient of Variation and Variation Speed-Figure 4: Visual stimuli
<p>In order to test the developed features, the SSVEP datasets recorded in (Nakanishi et al. 2014) is used. Flickering boxes had been presented on 24-inch LCD monitor with a refresh rate of 75Hz. 32 visual stimuli had been generated with 8 different frequencies (8 Hz, 9 Hz, …, 15 Hz) and 4 different phases (0<sup>o </sup>, 90<sup>o</sup> , 180<sup>o</sup> , 270<sup>o</sup> ) as shown in Figure 4. Thirteen healthy adults had participated in the experiments. EEG data had been recorded by 16 electrodes (FPz, F3, F4, Fz, C<sub>z</sub>, P1, P2, P<sub>z</sub>, PO3, PO4, PO7, PO8, PO<sub>z</sub>, O1, O2 and Oz). The sampling rate had been 512 Hz. The datasets are grouped into 4 groups. The 0-degree stimuli formed the 1st group, the 90- degree stimuli the 2<sup>nd</sup> group, the 180-degree stimuli the 3<sup>rd</sup> group and the 270-degree stimuli the 4<sup>th</sup> group. Thus, it is made possible to test the developed features in more datasets. </p>
BRAIN Journal-Novel Detection Features for SSVEP Based BCI: Coefficient of Variation and Variation Speed-Figure 2: Overlapped EEG segments
<p>In this study, the short time Fourier transformation (STFT) is used to examine the stability of the SSVEP. As the frequency resolution decreases with window size, overlapped EEG segments (Figure 2) that give sufficient frequency resolution are used. Coefficient of variation and variation speed detection features are proposed by using frequency spectrum of the segments. In Equation 1, x(t) sequence defines overlapped EEG segments in time domain, and in Equation 2 the x(f) sequence defines these segments in frequency domain and m is number of segments. </p> <p> </p>
BRAIN Journal-Novel Detection Features for SSVEP Based BCI: Coefficient of Variation and Variation Speed-Figure 1: Time vs frequency analysis of 10 Hz SSVEP response
<p>The stability of the SSVEP signal was examined by using wavelet analysis (Wu and Yao 2008). Since there is a trade-off between time and frequency resolution in wavelet analysis, examining the stability of SSVEP with wavelet analysis is getting harder in systems where the visual stimulus frequencies are close to each other, as shown in Figure 1. </p>
BRAIN Journal-Novel Detection Features for SSVEP Based BCI: Coefficient of Variation and Variation Speed-Figure 5. SSVEP detection accuracies by using PSD, CV and VS features
<p>When the results in Figure 5 are analyzed, it is seen that CV and VS features provide detection results similar to PSD, which is a familiar feature. Considering that the chance level is 12.5% in these dataset, CV and VS can be used as discriminative features for SSVEP. Also on some subjects, like S3 on 1st and 4th datasets, and S11 on 1st, 2nd and 4th datasets, the proposed features have given clearly better detection results than PSD.</p>
BRAIN Journal-Novel Detection Features for SSVEP Based BCI: Coefficient of Variation and Variation Speed-Figure 3: (fi) sequences at different frequencies
<p>In Equation 4, f<sub>nbk</sub> defines neighboring frequency and L defines number of neighboring. ► sequences are shown graphically in Figure 3. </p> <p>The first developed parameter for the stability of SSVEP is the coefficient of variation (CV). The variation in <span class="math-tex">\( (fi)\)</span> sequence that is obtained at EEG component with SSVEP response is expected to be less than the other sequences. . Equation 5 shows the calculation of CV. <span class="math-tex">\( ( ) i f \)</span> standard deviation of (f<sub>i</sub>) sequence. ( ) i f is the averaged value of (f<sub>i</sub>) sequence. </p>
Dataset used in "Steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) performance under different perturbations" 2018, PLOS ONE
<p>This file contains the segmented EEG data in offline and online conditions. Please read the readme.doc file for the detailed explanation.</p>
SSVEP Starlab
<p>Five Caucasian male volunteers S1 to S5 with average age 33.6 years participated in six recording sessions each, where oscillatory visual stimuli were presented at six different frequencies : 12, 14, 16, 18, 20 and 22 Hz. Oral informed consent was given by the volunteers and data was anonymized before its analysis. The visual stimuli were presented using stimulation sources consisting of an array of flickering light emitting diodes (LEDs) through a diffusing panel of 100 squared centimeters.</p> <p>Each session consisted of one recording per stimulation frequency. In each recording, stimulation trials (duration randomly ranging from 4 to 5 seconds), where the visual stimulus was presented, were followed by the same number of non-stimulation trials (duration randomly ranging from 5 to 8 seconds) with no visual stimulation. One stimulation source placed on the right of the subject presented the stimulation frequency under evaluation, while another source on the left presented a frequency randomly selected among the other frequencies used in the experiment. The goal of this montage was to simulate background interferences as in brain computer interfaces applications. Stimulation sources were separated by approximately 25 cm.</p> <p>The user was comfortably seated at one-meter distance from the stimulation sources and was instructed to look at the stimulation source placed on his right when hearing a beep sound (played one second before the stimulation started). EEG was acquired using an Enobio® recording system at a sampling rate of 250Hz from three channels placed in O1, Oz and O2, according to the 10-20 system, with the electrical reference placed in the right ear-lobe. Background ambient light remained homogeneous throughout all experimental sessions. The AsTeRICS platform was used to record the EEG streaming data, control the stimulation panels, and trigger the recording.</p>
SSVEP-exoskeleton
<p>This dataset gathers SSVEP-based BCI recordings of 12 subjects operating an upper limb exoskeleton during a shared control task. The exoskeleton is either controlled with a touchless interface detecting hand poses or with SSVEP-based BCI.</p> <p>This dataset is used in the following publications:</p> <ul> <li>Emmanuel K. Kalunga, Sylvain Chevallier, Olivier Rabreau, Eric Monacelli. <em>Hybrid interface : Integrating BCI in Multimodal Human-Machine Interfaces</em>. IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2014, Besancon, France.</li> <li>Emmanuel Kalunga, Sylvain Chevallier, Quentin Barthelemy. <em>Data augmentation in Riemannian space for Brain-Computer Interfaces</em>, STAMLINS (ICML workshop), 2015, Lille, France.</li> <li>Emmanuel K. Kalunga, Sylvain Chevallier, Quentin Barthelemy. <a href="http://www.sciencedirect.com/science/article/pii/S0925231216000540"><em>Online SSVEP-based BCI using Riemannian Geometry</em></a>. Neurocomputing, 2016. arXiv research report on arXiv:1501.03227.</li> </ul> <p>It should be indicated that for these publications, the authors excluded the following sessions due to protocol issues (synchronization problem, hardware failure, etc): subject 08 session <em>record-[2013.04.06-16.22.32]</em>, subject 10 sessions <em>record-[2014.02.26-15.10.48]</em> and <em>record-[2014.02.26-15.50.09]</em>, subject 11 sessions <em>record-[2014.02.24-17.56.37]</em> and <em>record-[2014.02.24-18.02.40]</em>.</p> <ol> <li> <p><em>Arm exoskeleton:</em> The exoskeleton used here is the ESTA robotic arm [1]. ESTA is designed to compensate for muscular dystrophy in the shoulder and elbow muscles occurring in several degenerative diseases, which affect the large muscles but spare the wrists and hands motor capacities.</p> </li> <li> <p><em>Touchless interface:</em> Our touchless interface embeds 5 IR-sensors which could set up in different spatial positions, according to the user requirements. The control system relies on a iterative kNN scheme to learn hand poses of each user. The details of the algorithm is provided in [2].</p> </li> <li> <p><em>Steady-state visually evoked potentials:</em> The g.Mobilab+ device is used for recording EEG at 256 Hz on 8 channels. For SSVEP stimulation, flash stimulus technique has been chosen. To avoid limitation imposed by refresh rate of computer screens, a microcontroller is set up to flash stimuli with light emitting diodes (LED) at frequencies F ={13, 17, 21} Hz. The device has been controlled and the LED blinking is precise up to the millisecond. The eight electrodes are placed according to the 10/20 system on Oz, O1, O2, POz, PO3, PO4, PO7 and PO8. The ground was placed on Fz and the reference was located on the right (or left) hear mastoid.</p> </li> </ol> <p>The datasets contains 12 directories, containing recording from 12 male and female subjects aged between 20 and 28 years. Informed consent was obtained from all subjects, each one has signed a form attesting her or his consent. The subject sits in an electric wheelchair, his right upper limb is resting on the exoskeleton. The exoskeleton is functional but is not used during the recording of this experiment.</p> <p>A panel of size 20x30 cm is attached on the left side of the chair, with 3 groups of 4 LEDs blinking at different frequencies. Even if the panel is on the left side, the user could see it without moving its head. The subjects were asked to sit comfortably in the wheelchair and to follow the auditory instructions, they could move and blink freely.</p> <p>A sequence of trials is proposed to the user. A trial begin by an audio cue indicating which LED to focus on, or to focus on a fixation point set at an equal distance from all LEDs for the reject class. A trial lasts 5 seconds and there is a 3 second pause between each trial. The evaluation is conducted during a session consisting of 32 trials, with 8 trials for each frequency (13Hz, 17Hz and 21Hz) and 8 trials for the reject class, i.e. when the subject is not focusing on any specific blinking LED.</p> <p>The recording are saved in GDF format [3], the stimulations code for each class are available as time events. There is between 2 and 5 sessions for each user, recorded on different days, by the same operators, on the same hardware and in the same conditions.</p> <p>To allow direct Python processing, a gzip pickle version of the data are also available as '.pz' file. Sample code to use these file is provided below.</p> <p>The stimulation code used in GDF file are those defined by OpenVibe:</p> <ul> <li>ExperimentStart: 32769, 0x00008001,</li> <li>ExperimentStop: 32770, 0x00008002</li> <li>VisualStimulationStart: 32779, 0x0000800b</li> <li>VisualStimulationStop: 32780, 0x0000800c</li> <li>Label_00: 33024, 0x00008100</li> <li>Label_01: 33025, 0x00008101</li> <li>Label_02: 33026, 0x00008102</li> <li>Label_03: 33027, 0x00008103</li> </ul> <p>The stimulation code are used as follows: <em>ExperimentStart</em> and <em>ExperimentStop</em> indicate the begining and the end of the session. A trial start with a <em>Label_XX</em> stimulation code indicating the class of the example, there is a 3s pause before the audio cue indicating the stimulus to focus. The audio cue onset is indicated by <em>VisualStimulationStart</em>, this is the start of the trial. The end of the trial take place 5s after and is indicated by <em>VisualStimulationStop</em>. <em>Label_00</em> is for resting class, <em>Label_01</em> is for 13Hz stimulation, <em>Label_02</em> is for 21Hz stimulation and <em>Label_03</em> is for 17Hz stimulation.</p> <p>Two example notebooks explain how to <a href="https://gist.github.com/sylvchev/04b0ee8760f30da9d9c9">extract covariance matrices</a> from the dataset based on the stimulation code and how to use a <a href="https://gist.github.com/sylvchev/3d7442bcc4d78b419afa">Riemannian-based classification</a> scheme with the extracted spatial covariance matrices. It is also possible to rely on <a href="https://gist.github.com/sylvchev/0b8f37252aca85f41f0d">MNE to open and process</a> this dataset.</p> <p>[1] M. Baklouti, P. A. Guyot, E. Monacelli, and S. Couvet. <em>Force controlled upper-limb powered exoskeleton for rehabilitation,</em> in Intelligent Robots and Systems (IROS), 2008, p. 4202.</p> <p>[2] H. Martin, S. Chevallier, and E. Monacelli, <em>Fast calibration of hand movement-based interface for arm exoskeleton control,</em> in European Symposium on Artificial Neural Networks (ESANN), 2012, pp. 573– 578.</p> <p>[3] A. Schlogl, <em>GDF - A general dataformat for biosignals</em>, <a href="http://arxiv.org/abs/cs/0608052">http://arxiv.org/abs/cs/0608052</a></p>
Low amplitude depths and high frequencies SSVEp stimuli for reactive BCI
<p>We have conducted two distinct study to systematically design more comfortable SSVEP stimuli, exploring both reduction of amplitude depth and the use of high frequency stimuli.<br> The participants did not report any of the exclusion criteria (neurological antecedents, usage of psychoactive medication). The study was approved by the ethics committee of the University of Toulouse (CER approval number 2020-334) and was carried in accordance with the declaration of Helsinki. Participants gave informed written consent prior to the experiment.<br> Experiment took place in ISAE-Supaero, Toulouse, FRANCE.</p> <p># EEG data acquisition</p> <p>EEG data was recorded from 32 electrodes fitted in an elastic cap according to the 10-20 international system and connected to a LiveAmp amplifier (Brain Products, Munich, Germany). The ground electrode was placed at the Fpz electrode location and all electrodes were referenced to FCz electrode. The electrode impedance were brought below 20kOhm prior recording. The signal was acquired at a rate of 500Hz with a digital band-pass filter ranging from 0.1 to 250Hz. At the onset of every stimulus presentation, an event trigger was generated by the stimulus presentation program and synchronized to the EEG data stream via Lab Streaming Layer(LSL).<br> Data are in .set/.fdt format from EEGLab.<br> <br> # High frequencies vs low frequencies</p> <p>The twelve helathy subjects (mean age=27, SD=6, 8 male and 4 female) underwent a single session that was cut in two separated parts. Firstly, we have presented sequentially and individually stimuli with twelve, low, frequencies from 8 to 30Hz with a range of 2Hz and then another, high, frequencies from 32 to 60 with a 2Hz range (48, 50, 52Hz were excluded because of power line noise). Half of the subjects started the session with low frequencies and the other half with high ones. Each stimulation was preceded by a red circling, visual cue, for 0.5s, it then lasted for 3s, followed by an inter-trial of 0.5s with a self-paced break (space bar press to continue) between blocks. A block consists of the presentation of the twelve corresponding frequencies of the condition. Each block was repeated 15 times.<br> Naming of the files follow the convention: P{subject number}_high for high frequencies or P{subject number}_low for the low frequencies.<br> The data includes all the triggers and the corresponding label. Use for instance</p> <pre><code class="language-python">mne.io.read_raw_eeglab</code></pre> <p># Amplitude reduction</p> <p>We have studied amplitude depth reduction for both high and low frequency stimuli. Amplitude depth corresponds to the contrast, i.e. the difference between the two antagonist states of the stimuli. In our case we have reduced the maximum amplitude reachable and kept the minimum the same. For instance, full amplitude depth stimulus oscillates between white (minimum) and black (maximum). With reduced amplitude it oscillates between white (minimum) and gray (maximum that vary).<br> Twelve healthy individuals (mean age=27, SD=5, 8 male and 4 female) took part in this experiment. We have selected 12, 14, 16 and 18Hz as for the low frequencies condition and 32, 34, 36 and 38Hz frequencies. Each stimulation was preceded by a red circling, visual cue, for 0.5s, it then lasted for 3s, followed by an inter-trial of 0.5s with a self-paced break (space bar press to continue) between blocks. A block consists of the presentation of the four corresponding frequencies of the condition. Each block was repeated 15 times.<br> The amplitude depth was manipulated across six levels (100, 80, 60, 50, 40, 30).<br> The total duration of this experiment was twice as long as the first one and therefore was split in two sessions. The two sessions were separated by a maximum of one week.</p> <p> </p> <p> </p> <p>Fore more details on the data please contact simon.ladouce@isae-supaero.fr or ludovic.darmet@isae-supaero.fr.</p>
SSVEP Evaluation of Brain Function (PRO)
ClinicalTrials.gov study NCT06013566. IPD Sharing: NO. Countries: 1. Publications: 0.
SSVEP and Distractor Processing During Visual Search
ClinicalTrials.gov study NCT05633238. IPD Sharing: YES. Countries: 1. Publications: 0.
SSVEP Evaluation of Brain Function
ClinicalTrials.gov study NCT05614570. IPD Sharing: NO. Countries: 1. Publications: 0.
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