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155 results for “BCI”
Instant classification for the spatially-coded BCI
<p>The 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]. Each file in the archive contains data from the online session of the respective participant. The Matlab data structure contains the following fields:</p> <p>data. fsample: sampling rate (512 Hz)<br> data.trial: EEG signals for each trial<br> data.time: sampling time points<br> data.classified: classifier output for each step<br> data.class: true class<br> data.accuracy: classification accuracy<br> data.probability: posterior class probabilities</p> <p>[1] Maye A, Zhang D, Engel AK (2017) "Utilizing Retinotopic Mapping for a Multi-Target SSVEP BCI With a Single Flicker Frequency", IEEE Transactions on Neural Systems and Rehabilitation Engineering, in press.</p> <p>[2] Maÿe A. Rauterberg R, Engel AK (2021) "Instant classification for the spatially-coded BCI", PLoS ONE, iunder review.</p>
Electrophysiological Signals of Embodiment and MI-BCI Training in VR
<p><strong>DATASET DETAILS:</strong></p> <p><strong>Participant demographics:</strong></p> <p>A total of 26 participants were included, consisting of 10 males (mean age 25.4 ± 7.4) and 16 females (mean age 23 ± 3.2). All participants were right-handed, reported normal or corrected-to-normal vision, and had no motor impairments. Three participants had previous experience with BCIs, and five participants used VR more than twice. Participants were randomly assigned to either the embodied group (N=13) or the non-embodied group (N=13), which served as a control. All participants signed an informed consent before participating in the study in accordance with the 1964 Declaration of Helsinki. </p> <p><strong>Experiment Description:</strong></p> <p>A between-subject design was used to investigate the effect of virtual embodiment priming phase on the subsequent motor-imagery training phase in VR. The experiment comprised four main blocks: (1) equipment setup and instructions (45-60 minutes), (2) resting state EEG recording (4 minutes), (3) inducing or breaking the sense of embodiment in VR (5 minutes), and (4) MI training in VR (15 minutes). The entire experiment lasted approximately 90-120 minutes. Directly after block 3, participants answered a questionnaire that measured their subjective sense of embodiment and physical presence.</p> <p><strong>Equipment:</strong></p> <p>A wireless EEG amplifier (LiveAmp; Brain Products GmbH, Gilching, Germany) was used, with 32 active <strong>EEG</strong> electrodes (+3 ACC) with a sampling rate of 500Hz. In addition, <strong>EMG, </strong>and <strong>Temperature</strong> signals (in uV) have been recorded synchronously in a bipolar montage and connected to the EEG amplifier’s AUX input through the Brain Products BIP2AUX adapter.</p> <p>Visual feedback was provided through an Oculus Rift CV1 headset (Reality Labs, formerly Facebook, Inc., CA, USA).<br> </p> <p><strong>Channel Indices:</strong></p> <p><strong>EEG</strong>: 1-32<br> <strong>EMG Left</strong> (AUX1): 33<br> <strong>EMG Right</strong>. (AUX2): 34<br> <strong>Temperature</strong> (AUX3): 35<br> <strong>ACC</strong>: 36-38</p> <p> </p> <p><strong>Event codes:</strong></p> <table> <tbody> <tr> <td><strong>Code</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>S01</td> <td>Experiment Start</td> </tr> <tr> <td>S02</td> <td>Baseline Start</td> </tr> <tr> <td>S03</td> <td>Baseline Stop</td> </tr> <tr> <td>S04</td> <td>Start Of Trial</td> </tr> <tr> <td>S05</td> <td>Cross On Screen</td> </tr> <tr> <td>S07</td> <td>class1, Left hand </td> </tr> <tr> <td>S08</td> <td>class2, Right hand </td> </tr> <tr> <td>S09</td> <td>Feedback Continuous</td> </tr> <tr> <td>S10</td> <td>End of Trial</td> </tr> <tr> <td>S11</td> <td>End Of Session</td> </tr> <tr> <td>S12</td> <td>Experiment Stop</td> </tr> </tbody> </table> <p> </p> <p><strong>Directory tree:</strong></p> <p>ROOT<br> |<br> +--- GROUP [Control or Embodied]<br> | +---USER #<br> | | +---TASK #<br> | | | +---Resting State<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk<br> | | | +---Embodiment<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk<br> | | | +---MI<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk</p> <p> </p> <p>For demographics and questionnaire data, please contact the authors.</p>
Less is more: selection from a small set of options improves BCI velocity control
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Reconfiguring motor circuits for a joint manual and BCI task
<p>SQL database containing all models and analysis results for listed publication. Results and figures from paper can be reproduced by using the code at https://github.com/benlansdell/dualbci</p>
CNBI EPFL Brain Tweakers Cybathlon BCI race dataset
<p>This dataset contains training and competition EEG data (GDF format) and application log files for the two pilots of the Brain Tweakers team in the Cybathlon BCI race event. The Cybathlon is the first international competition for bionic technologies. It was held in Zurich, Switzerland in October 2016 and encompassed the BCI race event, where 4 disabled contestants were driving their avatar by means of 3 mental commands towards the finish line of a race track. Pilot MA25VE was the winner of the competition and pilot AN14VE the recordman.</p> <p>More information on the event can be found at: http://www.cybathlon.ethz.ch/</p> <p>All EEG data were recorded with a gTec gUSBamp 16-channel system at 512 Hz on scalp locations (10-20 system, in order): Fz,FC3,FC1,FCz,FC2,FC4,C3,C1,Cz,C2,C4,CP3,CP1,CPz,CP2,CP4. The brain-computer interface used was a motor-imagery BCI. This dataset will be linked to a publication upon acceptance of the latter.</p> <p>For more information on the dataset, please contact the authors.</p>
Recruitment limitation for three tree species in BCI, Panama
<p><span>Recruitment limitation – the failure of a species to establish recruits at an available site – is a potential determinant of plant communities' structure, causing local communities to be a limited subset of the regional species pool. Recruitment limitation results from three mechanisms: i) lack of seed sources (i.e., source limitation), ii) failure of available seeds to reach recruitment sites (i.e., dispersal limitation), and iii) failure of arrived seeds to establish at a location (i.e., establishment limitation). Here, we evaluated the relative importance of these mechanisms in three co-occurring tree species (<i>Dipteryx oleifera, Attalea butyracea</i>,<i> </i>and <i>Astrocaryum standleyanum</i>) that share seed dispersers/predators. The study was set up on Barro Colorado Island (Panama) at 62 1-ha sites with varying tree densities. Source limitation was estimated as the proportion of sites that would be reached by seeds if seeds were distributed uniformly. Dispersal limitation was estimated from the number of sites with seeds in the soil bank. Establishment limitation was evaluated by measuring germination and 1-year survival in seed addition experiments. The effect of conspecific and heterospecific densities on the mechanisms was evaluated at three spatial scales (1, 5 and 9 ha). For all species, seed predation was the most important recruitment component (~80% decrease in seed survival). Establishment varied among species and was affected by conspecific and heterospecific species densities across spatial scales. Given that species identity, distribution and seed dispersal/predation affect recruitment at multiple scales, multiscale studies are required to understand how recruitment limitation determines community structure in tropical forests. </span></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>
BCI speller task - EEG data
<p>EEG data (version 1) from 10 autistic and 10 non-autistic individuals who performed a pseudoword inversion task using a BCI speller.</p> <p>Details about the dataset:</p> <p>Groups:<br>S - Non-autistic<br>P - Autistic</p> <p>Each word of each participant has 2 matrixes: eeg_word (with the eeg data values) and eeg_events (with the protocol/events).</p> <p>eeg_word:</p> <p>channels x time, srate = 256 Hz</p> <p>Channels (rows):<br>1. FPz<br>2. Fz<br>3. FC1<br>4. FCz<br>5. FC2<br>6. C3<br>7. Cz<br>8. C4<br>9. CPz<br>10. P3<br>11. Pz<br>12. P4<br>13. PO7<br>14. POz<br>15. PO8<br>16. Oz</p> <p>eeg_events:</p> <p>c: control word<br>5: 5-letter word<br>7: 7-letter word</p> <p>cue: Instruction<br>imag: Encoding<br>blink: BCI letters start blinking<br>blink_stop: BCI letters stop blinking<br>Corr: Correct letter detected<br>Err_syst: System error (letter)<br>Err_part: Participant error (letter)<br>finalWord_corr: Outcome word (correct)<br>finalWord_errS: Outcome word with system errors<br>finalWord_errP: Outcome word with participant errors<br>finalWord_errPS: Outcome word with system and participant errors</p>
Lecture of physiological activity with EEG techniques in two groups of students using a BCI device
<p>Currently, there is a great interest in knowing more in detail what happens in a student's mind in the classroom. This dataset seeks to help those researchers who want to perform data analytics of student behavior in a classroom.</p> <p>This dataset has the peculiarity that it has been built inside a classroom in an authentic learning situation wherein a face-to-face model a teacher teaches his class in front of a traditional group.</p> <p>The data set achieved seeks to provide support for the analysis of learning activities to identify the good and bad practices and the availability of students to appropriate new thoughts. In addition, these results also serve as support to generate new research through other learning scenarios, whether traditional or distance learning, where the results of the physiological activity of the brain can be used to compare the didactics that produce the best results in the students and the areas of opportunity in the teaching processes.</p>
c-VEP-based BCI for PIN pad task on a T9 layout
<p># Participants<br> Eleven 11 volunteers (2 woman, mean age 28) with normal or corrected-to-normal vision participated in this experiment in our laboratory. The participants did not report any of the exclusion criteria (epilepsy, neurological antecedents, being under psychoactive medication). The study was approved by the ethics committee of the University of Toulouse (CER approval number 2020-334) and carried in accordance with the declaration of Helsinki. Participants gave informed written consent, prior to the experiment. The consent form included a section to approve the online data sharing.</p> <p># Experimental protocol<br> We used the BrainProduct LiveAmp 32 active electrodes wet-EEG setup with a refresh rate of 500Hz to record the surface brain activity. The 32 electrodes followed the 10-20 international system. The ground electrode was placed at the Fpz electrode location and all electrodes were referenced to FCz electrode. The electrodes impedance were brought below 20 $k\Omega$ prior to the recording. We used a single maximum length sequence with 11 different shifts to animate the stimulation for the different targets. The presentation was rendered on an LCD monitor: LG GN 750, 1027x768 pixels, 400 cd/m^2, and 60Hz refresh rate. EEG data and markers were synchronized during recording using Lab Streaming Layer.<br> <br> Subjects were asked to follow cues on a T9 layout to emulate a PIN code typing task (11 classes problem). It was implemented in Python, using the Psychopy toolbox. The 11 stimuli were circles with radius of 150 pixels, with a margin of 250 pixels vertically and horizontally and the figure in the middle.<br> Fifteen 11-trial blocks were recorded for each subject. During a trial, one of the 11 targets was cued for 0.5s, followed by 2.2s of stimulation with a 0.7s inter-trial before next one. The cue sequence for each trial was pseudo-random and different for each block. After each block a pause was observed and subjects had to press space bar to continue.<br> <br> # Pre-processing<br> No pre-processing was applied and data are neither sliced or epoched but it is possible to do it with the help of the markers.</p>
BCI-FES Therapy for Stroke Rehabilitation
ClinicalTrials.gov study NCT04279067. IPD Sharing: NO. Countries: 1. Publications: 1.
Tele BCI-FES for Upper -Limb Stoke Rehabilitation
ClinicalTrials.gov study NCT05215522. IPD Sharing: NO. Countries: 1. Publications: 1.
Stroke Rehabilitation Using Brain-Computer Interface (BCI) Technology
ClinicalTrials.gov study NCT04141774. IPD Sharing: NO. Countries: 1. Publications: 5.
Recruitment limitation for three tree species in BCI, Panama
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Lecture of physiological activity with EEG techniques in two groups of students using a BCI device
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Data from: Speech motor cortex enables BCI cursor control and click
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Data from: BCI training to move a virtual hand reduces phantom limb pain: a randomized crossover trial
<p>Objective: To determine whether training with a brain–computer interface (BCI) to control an image of a phantom hand, which moves based on cortical currents estimated from magnetoencephalographic signals, reduces phantom limb pain.</p> <p>Methods: Twelve patients with chronic phantom limb pain of the upper limb due to amputation or brachial plexus root avulsion participated in a randomized single-blinded crossover trial. Patients were trained to move the virtual hand image controlled by the BCI with a real decoder, which was constructed to classify intact hand movements from motor cortical currents, by moving their phantom hands for 3 days ("real training"). Pain was evaluated using a visual analogue scale (VAS) before and after training, and at follow-up for an additional 16 days. As a control, patients engaged in the training with the same hand image controlled by randomly changing values ("random training"). The two trainings were randomly assigned to the patients. This trial is registered at UMIN-CTR (UMIN000013608).</p> <p>Results: VAS at day 4 was significantly reduced from the baseline after real training (45.3 [24.2] to 30.9 [20.6], 1/100mm, mean [SDs]; P=0.009<0.025), but not after random training (P=0.047>0.025). Compared to VAS at day 1, VAS at days 4 and 8 was significantly reduced by 32% and 36%, respectively, after real training and was significantly lower than VAS after random training (P<0.01).</p> <p>Conclusion: Three-day training to move the hand images controlled by BCI significantly reduced pain for one week.<br> Classification of evidence: This study provides Class &#8546; evidence that BCI reduces phantom limb pain.</p> <p> </p>
Reconfiguring motor circuits for a joint manual and BCI task
<p>Labview experiment details for recordings supporting dual control brain-computer interface.</p>
Reconfiguring motor circuits for a joint manual and BCI task
<p>MEA recordings supporting dual control BCI</p>
Data for Fig 1C of PLOS Biology The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users
<p>The mat file consists of the race completion time data of pilots P1 and P2 and the session date these races took place on.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.