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155 results for “BCI”
An EEG dataset for cross-session mental workload estimation: Passive BCI competition of the Neuroergonomics Conference 2021
<p>The dataset is part of a new open EEG database designed to answer a need for more publicly available EEG-based dataset to design and benchmark passive brain-computer interface pipelines (as detailed in [Hinss2021]). This database is currently being created and will be fully released before the end of the year. It will include data acquired over 30 participant, 4 tasks and 3 sessions. For this competition, hosted by the Neuroergonomics Conference 2021, only one task and half the participants will be analyzed. Hence, this competition focuses on a renowned task that elicits various levels of mental/cognitive workload: the Multi-Atribute Task Battery-II (MATB-II) developed by NASA (https://matb.larc.nasa.gov/). It is composed of 4 sub-tasks: system monitoring, tracking, resource management and communications. By varying the number and complexity of the sub-tasks, 3 levels of workload were elicited (verified through statistical analyzes of both subjective and objective -behavioral and cardiac- data). Each difficulty level was performed by 15 subjects (6 female; 9 average 25 y.o.) during 5 minutes per session, in a pseudo-randomized order. Each session was separated by 7 days. We used a 62 actiChamp EEG channels device (BrainProducts; electrode placement 10-20 system).</p> <p> </p> <p><strong>For the competition, your goal is to predict the mental workload for a given subject (intra-subject estimation) using the EEG data from another session (inter-session adaptation). More information on the conference website and in the documentation file.</strong></p>
COG-BCI database: A multi-session and multi-task EEG cognitive dataset for passive brain-computer interfaces
<p>Brain-Computer Interfaces, and especially passive Brain-Computer Interfaces (pBCI), with their ability to estimate and detect mental states, are receiving increasing attention from both the scientific and the research and development communities. Many pBCIs aim to increase the safety of complex work environments such as in the aeronautical domain. Therefore, mental workload, vigilance and decision-making are some of the most commonly examined aspects of cognition within this field of research. A large proportion of pBCIs involve a component of machine learning and signal processing as the data that are collected need to be transformed into a reliable estimate of the users’ current mental state (e.g. mental workload). Improving this component is a major challenge for researchers, requiring large quantities of data. While data sharing is common for the active BCI community, open pBCI datasets are scarcer and generally incomplete with regards to the information they report. This is particularly true for datasets encompassing several tasks or sessions, which are of importance for tackling the challenges of transfer learning. Testing new pipelines, feature extraction algorithms and classifiers are central issues for future advances in research within this domain, as well as for algorithm benchmark and research reproducibility.The COG-BCI database presented here is comprised of the recordings of 29 participants over 3 individual sessions with 4 different tasks designed to elicit different cognitive states. This results in a total of over 100 hours of open electrophysiological (EEG) and electrocardiogram (ECG) data. The project was validated by the local ethical committee of the University of Toulouse (CER number 2021-342). The dataset was validated on a subjective, behavioral and physiological level (i.e. cardiac and cerebral activity), to ensure its usefulness to the pBCI community. This body of work represents a large effort to promote the use of pBCIs, as well as the use of open science.</p> <p> </p> <p><strong>The data are in the Brain Imaging Data Structure (BIDS) format. For more information, please read the COG-BCI_info.pdf file.</strong></p> <p><strong>Please note that version 4 corrected an electrode name mismatch, for which we sincerely apologize. The answers to the RSME and KSS questionnaires are provided in two separate .txt files.</strong></p>
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>
BRAIN Journal-Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction-Figure 5. PSD (dB/Hz) vs freaquency (Hz) of each IMF showen in fig 4 in channel C4 (a) and in C3 (b)
<p> In Fig 5, we noted that ocular artifact frequency is generally low around 5Hz with high amplitude. This artifact appears mainly in IMF3 and IMF4. Finally, band power was applied for the new signal. As a last step, the logarithm of the BP is calculated in order to transform the distribution of this feature to a more Gaussian like shape, because the classifiers we used, such as HMMs and SVM assume normally distributed features.</p>
BRAIN Journal-Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction-Figure 4. The EMD decomposition results for subject 2 when he imagines left hand movement
<p>Fig. 4 shows the EMD decomposition result of one-trial (left hand movement imagination) for subject 2 in the channels C3 and C4 respectively (the pre-filtered EEG signal used for this illustration is not corrupted by blinking artifact.). Each channel is decomposed into ten IMFs and one residue</p>
BRAIN Journal-Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction-Figure 5b. PSD (dB/Hz) vs freaquency (Hz) of each IMF showen in fig 4 in channel C4 (a) and in C3 (b)
<p>Therefore, the new signal is reconstructed by keeping only the two first IMFs. EMD also allows eliminating the artifacts in the EEG during the recording sessions like eye blinks and eyeball movements. In Fig 5, we noted that ocular artifact frequency is generally low around 5Hz with high amplitude. This artifact appears mainly in IMF3 and IMF4. Finally, band power was applied for the new signal. As a last step, the logarithm of the BP is calculated in order to transform the distribution of this feature to a more Gaussian like shape, because the classifiers we used, such as HMMs and SVM assume normally distributed features.</p>
BRAIN Journal-Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction-Figure 3. Hybrid EMD-BP approach for one trail feature extraction
<p>In this work, we propose a direct nonlinear approach to extract the more relevant IMFs corresponding to the different frequency components in the and bands and then obtain the BP in order to use them as features for mental task classification (see Fig. 3). The feature vector p used for the demonstration in this paper is composed, for each sample I, 1 < i < 2048, in a given trial (among a total of 160 trials) of four bandpower, calculated of the rhythms and in positions C3 and C4 Trad et al., 2011).</p>
BRAIN Journal-Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction-Figure 4b. The EMD decomposition results for subject 2 when he imagines left hand movement
<p>d et al., 2011). Fig. 4 shows the EMD decomposition result of one-trial (left hand movement imagination) for subject 2 in the channels C3 and C4 respectively (the pre-filtered EEG signal used for this illustration is not corrupted by blinking artifact.). Each channel is decomposed into ten IMFs and one residue.</p>
BRAIN Journal-Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction-Figure 6. The general conception of our asynchronous system BCI (offline - online) for reinforcement of a joystick movement
<p>Once the motor imagery is identified, a command may be associated to this mental task in order to control a machine (Prataksita et al., (2014)) (Guger et al., 1999). In this work, we constructed a new Simuhnk/MathWork model to translate on-line the EEG signals into low-level commands. Fig. 6 shows our experimental EEG-based BCI System </p>
BRAIN Journal-Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction-Figure 1. General architecture of an online (BCI)
<p>One major challenge of our BCI system is to describe the signals EEG by a few relevant values called features i.e. step 3 in Fig (1). The success of the mental imagery classification depends on the choice of features used to characterize the raw EEG signals. These features can then be used in step 4 in order to classify the user’s mental state. Several approaches for feature extraction have been proposed in literature. </p>
BRAIN Journal-Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction-Figure 3. Hybrid EMD-BP approach for one trail feature extraction
<p>In this work, we propose a direct nonlinear approach to extract the more relevant IMFs corresponding to the different frequency components in the and bands and then obtain the BP in order to use them as features for mental task classification (see Fig. 3). The feature vector pi used for the demonstration in this paper is composed, for each sample I, 1 < i < 2048, in a given trial (among a total of 160 trials) of four bandpower, calculated of the rhythms and in positions C3 and C4 (Trad et al., 2011).</p>
BRAIN Journal-Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction-Figure 2. Timing of one trial of the experiment with continuous feedback (Guger et al, 2001)
<p>At the beginning of each trial (t = 0 s), a fixation cross appeared on the black screen. After two seconds a warning stimulus was given in the form of a beep. From 3 to 4.25s, an arrow (cue stimulus), pointing to the left or right, was shown on the screen. The subject was instructed to imagine a left or right hand movement until the end of the trial, depending on the direction of the arrow. The EEG was sampled and classified on line throughout the session. Between 4.25 and 8s, the classification result was used to give a continuously updated feedback stimulus in the form of a horizontal bar that appeared in the center of the screen. The paradigm is illustrated in fig (2).</p>
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 of an EEG-based BCI experiment in Virtual Reality and on a Personal Computer
<p><strong>Summary:</strong></p> <p>This dataset contains electroencephalographic recordings on 21 subjects doing a visual P300 experiment on PC (personal computer) and VR (virtual reality). The visual P300 is an event-related potential elicited by a visual stimulation, peaking 240-600 ms after stimulus onset. The experiment was designed in order to compare the use of a P300-based brain-computer interface on a PC and with a virtual reality headset, concerning the physiological, subjective and performance aspects. The brain-computer interface is based on electroencephalography (EEG). EEG data were recorded thanks to 16 electrodes. The virtual reality headset consisted of a passive head-mounted display, that is, a head-mounted display which does not include any electronics with the exception of a smartphone. A full description of the experiment is available at <a href="https://hal.archives-ouvertes.fr/hal-02078533">https://hal.archives-ouvertes.fr/hal-02078533</a>. This experiment was carried out at GIPSA-lab (University of Grenoble Alpes, CNRS, Grenoble-INP) in 2018, and promoted by the IHMTEK Company (Interaction Homme-Machine Technologie). The study was approved by the Ethical Committee of the University of Grenoble Alpes (Comité d’Ethique pour la Recherche Non-Interventionnelle). Python code for manipulating the data is available at <a href="https://github.com/plcrodrigues/py.VR.EEG.2018-GIPSA">https://github.com/plcrodrigues/py.VR.EEG.2018-GIPSA</a>. The ID of this dataset is <em>VR.EEG.2018-GIPSA.</em></p> <p> </p> <p><strong>Full description of the experiment and dataset:</strong> <a href="https://hal.archives-ouvertes.fr/hal-02078533">https://hal.archives-ouvertes.fr/hal-02078533</a></p> <p> </p> <p><strong>An analysis of the experiment: </strong><a href="https://hal.archives-ouvertes.fr/hal-02464023">https://hal.archives-ouvertes.fr/hal-02464023</a></p> <p> </p> <p><strong><em>Principal Investigator</em>:</strong> Eng. Grégoire Cattan</p> <p> </p> <p><strong><em>Technical Supervisors</em>:</strong> Eng. Anton Andreev, Eng. Pedro L. C. Rodrigues</p> <p> </p> <p><strong><em>Scientific Supervisor:</em></strong> Dr. Marco Congedo</p> <p> </p> <p><strong>ID of the dataset: </strong><em>VR.EEG.2018-GIPSA</em></p> <p> </p>
Brain Invaders calibration-less P300-based BCI with modulation of flash duration Dataset (bi2015a)
<p><strong>Summary:</strong></p> <p>This dataset contains electroencephalographic (EEG) recordings of 50 subjects playing to a visual P300 Brain-Computer Interface (BCI) videogame named <em>Brain </em><em>Invaders</em>. The interface uses the oddball paradigm on a grid of 36 symbols (1 Target, 35 Non-Target) that are flashed pseudo-randomly to elicit the P300 response. EEG data were recorded using 32 active wet electrodes with three conditions: flash duration 50ms, 80ms or 110ms. The experiment took place at GIPSA-lab, Grenoble, France, in 2015. A full description of the experiment is available at <a href="https://hal.archives-ouvertes.fr/hal-02172347">https://hal.archives-ouvertes.fr/hal-02172347</a>. Python code for manipulating the data is available at <a href="https://github.com/plcrodrigues/py.BI.EEG.2015a-GIPSA">https://github.com/plcrodrigues/py.BI.EEG.2015a-GIPSA</a>. The ID of this dataset is <em>bi2015a.</em></p> <p> </p> <p><strong>Full description of the experiment and dataset: </strong><a href="https://hal.archives-ouvertes.fr/hal-02172347">https://hal.archives-ouvertes.fr/hal-02172347</a></p> <p> </p> <p><strong><em>Investigators</em>:</strong> Eng. Louis Korczowski, B. Sc. Martine Cederhout</p> <p> </p> <p><strong><em>Technical</em></strong> <strong><em>Support</em></strong>: Eng. Anton Andreev, Eng. Grégoire Cattan, Eng. Pedro. L. C. Rodrigues, M. Sc. Violette Gautheret</p> <p> </p> <p><strong><em>Scientific Supervisor:</em></strong> Ph.D. Marco Congedo</p> <p> </p> <p><strong>ID of the dataset: </strong><em>bi2015a</em></p>
Brain Invaders calibration-less P300-based BCI using dry EEG electrodes Dataset (bi2014a)
<p><strong>Summary:</strong></p> <p>This dataset contains electroencephalographic (EEG) recordings of 71 subjects playing to a visual P300 Brain-Computer Interface (BCI) videogame named <em>Brain </em><em>Invaders</em>. The interface uses the oddball paradigm on a grid of 36 symbols (1 Target, 35 Non-Target) that are flashed pseudo-randomly to elicit the P300 response. EEG data were recorded using 16 active dry electrodes with up to three game sessions. The experiment took place at GIPSA-lab, Grenoble, France, in 2014. A full description of the experiment is available at <a href="https://hal.archives-ouvertes.fr/hal-02171575">https://hal.archives-ouvertes.fr/hal-02171575</a>. Python code for manipulating the data is available at <a href="https://github.com/plcrodrigues/py.BI.EEG.2014a-GIPSA">https://github.com/plcrodrigues/py.BI.EEG.2014a-GIPSA</a>. The ID of this dataset is <em>bi2014a.</em></p> <p> </p> <p><strong>Full description of the experiment and dataset: </strong><a href="https://hal.archives-ouvertes.fr/hal-02171575">https://hal.archives-ouvertes.fr/hal-02171575</a></p> <p> </p> <p><strong><em>Investigators</em>:</strong> Eng. Louis Korczowski, B. Sc. Ekaterina Ostaschenko</p> <p> </p> <p><strong><em>Technical</em></strong> <strong><em>Support</em></strong>: Eng. Anton Andreev, Eng. Grégoire Cattan, Eng. Pedro. L. C. Rodrigues, M. Sc. Violette Gautheret</p> <p> </p> <p><strong><em>Scientific Supervisor:</em></strong> Ph.D. Marco Congedo</p> <p> </p> <p><strong>ID of the dataset: </strong><em>bi2014a</em></p>
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