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219 results for “Motor Imagery”
A multi-modal human neuroimaging dataset for data integration: simultaneous EEG and fMRI acquisition during a motor imagery neurofeedback task: XP1
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EEG data offline and online during motor imagery for standing and sitting
<p>The experiments were conducted in an acoustically isolated room where only the participant and the experimenter were present. Participants voluntarily signed an informed consent form in accordance with the experimental protocol approved by the ethics committee of the Universidad Antonio Nariño. The participant was seated in a chair in a posture that was comfortable for him/her but did not affect data collection. In front of the participant, a 40-inch TV screen was placed at about 3 m. On this screen, a graphical user interface (GUI) displayed images that guided the participant through the experiment. Each experimental session was divided into two phases: an offline phase and an online phase. </p> <p>The offline experiments consisted of recording participants' EEG signals during motor imagery trials for standing and sitting that were guided by the GUI presented on the TV screen. Six offline runs were conducted in which the participants were standing in three runs and sitting in the other three runs. In each run, the participant had to repeat a block of 30 trials of mental tasks indicated by visual cues continuously presented on the screen in a pseudo-random sequence.</p> <p>The first phase of the experimental session was conducted to construct the offline parts of the dataset: (A) Sit-to-stand and (B) Stand-to-sit. The participant's EEG data were collected from 90 sequences for part A (45 trials of MotorImageryA tasks and 45 trials of IdleStateA tasks) and 90 sequences for part B (45 trials of MotorImageryB tasks and 45 trials of IdleStateB tasks).</p> <p>For each participant, the two machine learning models obtained in the offline phase were used to carry out the online experiment parts of the dataset: (C) Sit-to-stand and (D) Stand-to-sit. Each participant was instructed to select, in no particular order, 30 sequences for part C (15 trials of MotorImageryA tasks and 15 trials of IdleStateA tasks) and 30 other sequences for part D (15 trials of MotorImageryB tasks and 15 trials of IdleStateB tasks). Each trial was unique and was generated pseudo-randomly before the experiment.</p> <p>The database consisted of 32 electroencephalographic files corresponding to the 32 participants. All recordings were collected on channels F3, Fz, F4, FC5, FC1, FC2, FC6, C3, Cz, C4, CP5, CP1, CP2, CP6, P3, Pz, and P4 according to the 10-20 EEG electrode placement standard, grounded to AFz channel and referenced to right mastoid (M2). Each data file contained the data stream in a 2D matrix where rows corresponded to channels and columns corresponded to time samples with a sampling frequency of 250Hz.</p> <p>The following marker numbers encoded information about the execution of the experiment. Marker numbers 200, 201, 202, and 203, indicated the beginning and end of the four steps of the sequence in a trial (resting, fixation, action observation, and imagining). Marker numbers 1, 2, 3, and 4, indicated the figure activated on the screen to the participant perform the task corresponding to 1. actively imagining the sit-to-stand movement (labeled as MotorImageryA), 2. sitting motionless without imagining the sit-to-stand movement (labeled as IdleStateA), 3. standing motionless while actively imagining the stand-to-sit movement (labeled as MotorImageryB), or 4. standing motionless without imagining the stand-to-sit movement (labeled as IdleStateB). Finally, marker numbers 101, 102, 103, and 104, indicated the task detected by the BCI in real time during the online experiment: 101. MotorImageryA, 102. IdleStateA, 103. MotorImageryB, or 104. IdleStateB.</p>
A large EEG database with users' profile information for motor imagery Brain-Computer Interface research
<p><em><strong>Context </strong></em>: <br> We share a large database containing electroencephalographic signals from 87 human participants, with more than 20,800 trials in total representing about 70 hours of recording. It was collected during brain-computer interface (BCI) experiments and organized into 3 datasets (A, B, and C) that were all recorded following the same protocol: right and left hand motor imagery (MI) tasks during one single day session.<br> It includes the performance of the associated BCI users, detailed information about the demographics, personality and cognitive user’s profile, and the experimental instructions and codes (executed in the open-source platform OpenViBE).<br> Such database could prove useful for various studies, including but not limited to: 1) studying the relationships between BCI users' profiles and their BCI performances, 2) studying how EEG signals properties varies for different users' profiles and MI tasks, 3) using the large number of participants to design cross-user BCI machine learning algorithms or 4) incorporating users' profile information into the design of EEG signal classification algorithms.<br> <br> Sixty participants (Dataset A) performed the first experiment, designed in order to investigated the impact of experimenters' and users' gender on MI-BCI user training outcomes, i.e., users performance and experience, (Pillette & al). Twenty one participants (Dataset B) performed the second one, designed to examined the relationship between users' online performance (i.e., classification accuracy) and the characteristics of the chosen user-specific Most Discriminant Frequency Band (MDFB) (Benaroch & al). The only difference between the two experiments lies in the algorithm used to select the MDFB. Dataset C contains 6 additional participants who completed one of the two experiments described above. Physiological signals were measured using a g.USBAmp (g.tec, Austria), sampled at 512 Hz, and processed online using OpenViBE 2.1.0 (Dataset A) & OpenVIBE 2.2.0 (Dataset B). For Dataset C, participants C83 and C85 were collected with OpenViBE 2.1.0 and the remaining 4 participants with OpenViBE 2.2.0. Experiments were recorded at Inria Bordeaux sud-ouest, France.</p> <p><em><strong>Duration</strong> </em>: Each participant's folder is composed of approximately 48 minutes EEG recording. Meaning six 7-minutes runs and a 6-minutes baseline.</p> <p><br> <strong><em>Documents</em></strong><em> </em><br> <em>Instructions</em>: checklist read by experimenters during the experiments.<br> <em>Questionnaires</em>: the Mental Rotation test used, the translation of 4 questionnaires, notably the Demographic and Social information, the Pre and Post-session questionnaires, and the Index of Learning style. English and french version<br> <em>Performance</em>: The online OpenViBE BCI classification performances obtained by each participant are provided for each run, as well as answers to all questionnaires<br> <em>Scenarios/scripts</em> : set of OpenViBE scenarios used to perform each of the steps of the MI-BCI protocol, e.g., acquire training data, calibrate the classifier or run the online MI-BCI</p> <p><strong><em>Database </em></strong>: raw signals<br> Dataset A : N=60 participants<br> Dataset B : N=21 participants<br> Dataset C : N=6 participants<br> <br> The article that expained the database is available here:<br> Dreyer, P., Roc, A., Pillette, L. <em>et al.</em> A large EEG database with users’ profile information for motor imagery brain-computer interface research. <em>Sci Data</em> <strong>10</strong>, 580 (2023).<br> https://doi.org/10.1038/s41597-023-02445-z<br> </p>
fMRI study of a VR-based motor imagery and observation task, a conventional motor imagery task, and a motor execution task
<p>We used functional magnetic resonance imaging (fMRI) to map brain activation during: i) a VR-based motor imagery and observation task called NeuRow; ii) a conventional non-VR, motor imagery task based on the Graz paradigm; and iii) a motor execution task. Data were collected from two groups of healthy right-handed participants: 11 young adults (mean 58 age 27 ± 4 years) and 10 older adults (mean age 51 ± 6 years). The experimental protocol was designed in collaboration with the local healthcare system of Madeira, Portugal (SESARAM), in accordance with the 1964 Declaration of Helsinki, and approved by the scientific and ethic committees of the Central Hospital of Funchal with approval reference number: 21/2019. A written informed consent was obtained from each participant upon recruitment.</p> <p>Imaging was carried out on a 3T GE Signa HDxt MRI scanner (General Electrics Healthcare, Little Chalfont, United Kingdom) using a 12-channel head coil. fMRI data were acquired using a multi-slice 2D gradient-echo EPI sequence (TR/TE = 2500/30 ms, voxel size = 3.75x3.75x3.00 mm3, flip angle = 90, and FoV = 240x240 mm2). For co-registration purposes, whole-brain structural images were also acquired using a T1-weighted 3D Fast Spoiled Gradient-Echo (FSPGR) sequence (TR/TE = 7.8/3.0 ms, voxel size = 1.00x1.00x0.60 mm3).</p> <p>The following three tasks were performed for left and right arm movement separately, yielding a total of six fMRI runs (pseudo-randomized order): (a) NeuRow task: motor imagery and observation task through VR scenario (NeuRow); (b) Graz task: motor imagery only with abstract instructions (Graz), (c) and motor execution (ME): finger-tapping. Each fMRI run consisted of 8 trials, each with 20 s of baseline followed by 20 s of task (total run duration 5.33 min).</p>
Physiological Signals During Motor Imagery Brain-Computer Interface Training Using Virtual Reality and Haptics
<p><strong>Participant demographics:</strong></p> <p>The sample is consisted by 20 healthy volunteers with a mean age of 24.79 years (SD = 3.54 years). The cohort was 68% male and 32% female. In terms of education, 16% had attended only high school, while 32% had a bachelor's degree, 42% a master's degree, and 11% a doctorate. 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>The experiment consisted in having the subjects perform motor imagery of a bimanual rowing task with two individual paddles, one in each hand, under five experimental conditions. Four of these conditions used NeuRow (<a href="https://link.springer.com/chapter/10.1007/978-3-030-27950-9_1"><strong>Vourvopoulos et al. (2016-2019</strong>))</a>—a VR environment that renders virtual arms from a first-person perspective—while the other conditions used abstract feedback based on the BCI-Graz paradigm<a href="https://ieeexplore.ieee.org/abstract/document/1214714"> (<strong>Pfurtscheller et al. (2003))</strong></a>. All six conditions and their acronyms are described below:</p> <ol> <li><strong>Motor Imagery(MI)</strong>: The standard motor imagery training, with a fixation cross and directional arrows on a black background guiding the subjects through the experiment.</li> <li><strong>Motor Imagery/Motor Observation (MIMO):</strong> A motor imagery training paradigm using NeuRow, with a fixation cross and directional arrows overlaid on the VR environment, which was displayed through a monitor.</li> <li><strong>Motor Imagery/Motor Observation with Haptics (MIMOHP): </strong>A motor imagery training paradigm using NeuRow, with a fixation cross and directional arrows overlaid on the VR environment, which was displayed through a monitor. Hand controllers also provided haptic feedback through vibrotactile stimulation.</li> <li><strong>Motor Imagery/Motor Observation with VR HMD (MIMOVR):</strong> A motor imagery training paradigm using NeuRow, with a fixation cross and directional arrows overlaid on the VR environment, which was displayed through a VR HMD.</li> <li><strong>Motor Imagery/Motor Observation with VR HMD and Haptics (MIMOVRHP):</strong> A motor imagery training paradigm using NeuRow, with a fixation cross and directional arrows overlaid on the VR environment, which was displayed through a VR HMD. Hand controllers also provided haptic feedback through vibrotactile stimulation.</li> <li><strong>Motor Execution (ME):</strong> A fixation cross and directional arrows were displayed on a black background through a monitor (same as in MI), and guided the subjects through the experiment by having them tap their fingers accordingly. Data from this condition was available only after S07, so only 10 subjects<br> have performed ME.</li> </ol> <p>Finally, this experiment followed a within-subject design, in a randomized order of the conditions to minimize any order effects, while MI and ME conditions acted as control.</p> <p><strong>Equipment:</strong></p> <p>A wireless EEG amplifier (LiveAmp; Brain Products GmbH, Gilching, Germany) was used, with 32 active electrodes(+3 ACC) with a sampling rate of 500Hz. In addition, <strong>ECG, PPG</strong> and <strong>Respiration</strong> signals 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 a monitor in all conditions except in MIMOVR and MIMOVRHP, in which an Oculus Rift CV1 headset (Reality Labs, formerly Facebook, Inc., CA, USA) was used instead. Haptic feedback was provided through the Oculus Rift hand controllers.<br> </p> <p><strong>Channel Indices:</strong></p> <p><strong>EEG</strong>: 1-32<br> <strong>PPG</strong> (AUX1): 33<br> <strong>Resp</strong>. (AUX2): 34<br> <strong>ECG</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> +--- USER #<br> | +---SESSION #<br> | | +---TASK #<br> | | | +---MI<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk<br> | | | +---MIMO<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk<br> | | | +---MIMOHP<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk<br> | | | +---MIMOVR<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk<br> | | | +---MIMOHPVR<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk<br> | | | +---ME<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk</p> <p> </p> <p><strong>Note: </strong>The first three datasets are from pilot sessions: sub-p01 to p03. From sub-01 to 19, subjects 10 and 11 have been removed due to the lack of markers. Subject sub-13, task MIMOVRHP is missing.</p> <p> </p>
EEGlass motor-imagery and resting-state data
<p>Pilot acquisition of EEG data during motor-imagery and resting state (eyes-closed) from <a href="https://dl.acm.org/doi/10.1145/3341162.3348383">EEGlass eyeware prototype for ubiquitous brain-computer interaction.</a></p> <p>There are two types of EEG data: (1) motor imagery and (2) resting state during closed eyes from two EEG devices: (1) EEGlass through the OpenBCI board, and (2) Enobio 8 from Neuroelectrics. In addition, the EOG activity from four eye movements (up,down;left;right) from EEGlass are included. All datasets have been pre-processessed in EEGlab and exported as .set files.</p> <p><strong>Datasets:</strong></p> <ul> <li>Motor Imagery <ul> <li>EEGlass (data: MI_EEGlass.set; header: MI_EEGlass.fdt)</li> <li>Enobio (data: MI_Enobio.set; header: MI_Enobio.fdt)</li> </ul> </li> <li>Resting State (eyes-closed) <ul> <li>EEGlass (data: EC_EEGlass.set; header: EC_EEGlass.fdt)</li> <li>Enobio (data: EC_Enobio.set; header: EC_Enobio.fdt)</li> </ul> </li> <li>EOG <ul> <li>EEGlass <ul> <li> <p>EOG Up (EOG_U_EEGlass.set, .fdt)</p> </li> <li> <p>EOG Down (EOG_U_EEGlass.set, .fdt)</p> </li> <li> <p>EOG Left (EOG_U_EEGlass.set, .fdt)</p> </li> <li> <p>EOG Right (EOG_U_EEGlass.set, .fdt)</p> </li> </ul> </li> </ul> </li> </ul> <p><strong>Pre-processing:</strong></p> <ol> <li>Bandpass filtering: FIR 1-40 Hz</li> <li>Re-referencing: Common average reference (CAR)</li> <li>Channel locations <ul> <li>EEGlass [1:Nz; 2:TP9; 3:TP10]</li> <li>Enobio [1:Fpz ; 2:C3; 3:C4; 4:Pz]</li> </ul> </li> </ol> <p> </p> <p>Details from the pilot study can be found below:</p> <blockquote> <p>A. Vourvopoulos, E. Niforatos, M. Giannakos, 2019. EEGlass: an EEG-eyeware prototype for ubiquitous brain-computer interaction. In Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers(UbiComp/ISWC '19 Adjunct). Association for Computing Machinery, New York, NY, USA, 647–652. DOI: https://doi.org/10.1145/3341162.3348383</p> </blockquote>
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>
Neurophysiologically meaningful motor imagery EEG simulated data
<p><strong>Description</strong></p> <p>This dataset contains different sessions of artificially generated motor imagery electroencephalographic (MI-EEG)-like signals. The data was generated based on PySimMIBCI [1], a framework for generating realistic MI-EEG signals by integrating neurophysiologically meaningful activity into biophysical forward models.</p> <p>Specifically, MI was modeled by simulating an Event-Related Desynchronization (ERD) in the α band (9-11 Hz) over the contralateral motor area, representing the well-known brain dynamic during hand MI tasks. Right hand MI was differentiated from rest activity by a reduction in amplitude (ERD%) on the left hemisphere. For rest trials, idle α activity was simulated without ERD. The simulation includes three types of noise and artifacts: background noise, blink artifacts, and eye movement artifacts. For more information about the simulation process, please refer to [1].</p> <p>Different sessions were simulated, with each session representing different simulated scenarios:</p> <ol> <li> <p><strong>Self-Regulation Capabilities:</strong> To evaluate MI-BCI performance under different self-regulation capabilities, nine sessions were simulated with varying levels of ERD%, ranging from 50% to 10% in steps of 5%. These sessions reflect different user abilities to modulate α-band activity during MI, with higher ERD% indicating stronger MI modulation. The name of each session indicates the ERD% level, which was constant for all the trials of the session. For example, session <em>S_30</em> represents an ERD% of 30%. It is important to mention that in all the simulated sessions, the subject performed the task correctly in every trial, meaning no failed trials were included.</p> </li> <li> <p><strong>Varying MI Modulation:</strong> An MI vs. Rest session was generated where MI trials were simulated with an ERD% sampled from a uniform distribution from 10% to 50%. This session simulates trials with varying levels of MI modulation within the same BCI session, providing a more continuous representation of user performance fluctuations during MI-BCI tasks. The name of this session is <em>S_mix</em>.<strong> </strong>A list of the ERD% corresponding to each trial is included at the <em>S_mix_ERD_list.npy</em> file.</p> </li> <li> <p><strong>Fatigue-Related Artifacts: </strong>An additional MI vs. Rest session that shows the impact of non-MI-related cortical activity, specifically fatigue-related effects. Fatigue was simulated by increasing frontal θ and parietal α power across the course of the session, as described in [1]. The first half of the trials were fatigue-free, while the second half gradually incorporated increasing fatigue levels. The MI trials in this session were simulated with a fixed ERD% of 50% ensuring consistent modulation ability throughout the session. The name of this session is <em>S_fatigue</em>.</p> </li> </ol> <p>Each session contains 100 trials per class (MI vs. Rest), of 4s duration each, simulated at 41 electrodes following the 10-5 electrode system, with a sampling frequency of 1000 Hz. It is important to mention that in all simulated sessions, the task wask correctly performed in the 100% of the trials, meaning no failed trials were included.</p> <p><strong>File Format:</strong></p> <p>Epoched data are provided as <em>.fif </em>files. This data can be easily read by <em>mne.read_epochs</em> function from the MNE-Python library.</p> <p><strong>Potential Use Cases: </strong></p> <p>This dataset is suitable for testing hypothesis during MI-BCI algorithms development, specially to study the impact of subject self-regulation capabilities and non-MI-related cortical activity on BCI efficacy.</p> <p><strong>References:</strong></p> <p>[1] C. M. Galván, R. D. Spies, D. H. Milone and V. Peterson, "Neurophysiologically Meaningful Motor Imagery EEG Simulation With Applications to Data Augmentation," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 32, pp. 2346-2355, 2024, doi: 10.1109/TNSRE.2024.3417311.</p>
EEG Motor Imagery Dataset from the PhD Thesis "Commande robuste d'un effecteur par une interface cerveau machine EEG asynchrone"
<p>This Dataset contains EEG recordings from 8 subjects, performing 2 task of motor imagination (right hand, feet or rest). Data have been recorded at 512Hz with 16 wet electrodes (Fpz, F7, F3, Fz, F4, F8, T7, C3, Cz, C4, T8, P7, P3, Pz, P4, P8) with a g.tec g.USBamp EEG amplifier.</p> <p>File are provided in MNE raw file format. A stimulation channel encoding the timing of the motor imagination. The start of a trial is encoded as 1, then the actual start of the motor imagination is encoded with 2 for imagination of a right hand movement, 3 for imagination of both feet movement and 4 with a rest trial.</p> <p>The duration of each trial is 3 second. There is 20 trial of each class.</p>
Motor-Imagery EEG Dataset During Robot-Arm Control
<p><strong>Experiment Description:</strong></p> <p>This experiment involved <strong>12 healthy subjects</strong> with no prior experience on neurofeedback or BCI, and without any known neurological disorders. All participants are right-handed, except one ambidextrous (participant #5). All participants have provided their signed informed consent for participating in the study in accordance with the 1964 Declaration of Helsinki.</p> <p>The experiment had been conducted in a laboratory environment under controlled conditions. The subjects went through <strong>three sessions</strong> lasting maximum two hours, during three consecutive days and each day at approximately at the same hour.</p> <p>During each session, participants underwent <strong>three different conditions</strong>. The first condition was always the ”<em>resting-state</em>”: the user was asked to keep the eyes open for two minutes staring at a screen with a green cross and a red arrow pointing up, and then closed for the other two minutes. After this, two more conditions followed related to a Motor Imagery (MI) task performed in a randomized order between left|right-hand movement. The two MI conditions consisted of<strong> two phases</strong> each: a training phase and a test phase. The general experimental routine for both of them was the same: each trial lasted 6 seconds (2 seconds baseline and 4 seconds MI), forewarned by the appearance of a green cross on the screen and a concomitant beep-sound a second before the onset of the task.</p> <p>Then, an arrow was appearing pointing left or right, and the subject had to imagine the movement of the corresponding arm reaching an object in front of the Baxter Robot (Rethink Robotics, Bochum, Germany). For both phases, 20 trials from left and 20 trials for right MI were generated in a randomized order, for a total of 40 trials. Finally, there was an inter-trial interval that extended randomly between 1.5 and 3.5 seconds.</p> <p>Overall, this study resulted into <strong>180 EEG </strong>datasets.</p> <p> </p> <p><strong>Data Description:</strong></p> <table> <tbody> <tr> <td><strong>Data Format</strong></td> <td>General Data Format (GDF)</td> </tr> <tr> <td><strong>Sampling Rate</strong></td> <td>250 Hz</td> </tr> <tr> <td><strong>Channels</strong></td> <td>32 EEG + 3 ACC.</td> </tr> <tr> <td><strong>EEG system</strong></td> <td>LiveAmp 32 with active electrodes actiCAP (Brain Products GmbH, Gilching, Germany)</td> </tr> </tbody> </table> <p> </p> <p><strong>Events:</strong></p> <table> <caption> </caption> <tbody> <tr> <td><strong>Code</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>32775</td> <td>Baseline Start</td> </tr> <tr> <td>32776</td> <td>Baseline Stop</td> </tr> <tr> <td>768</td> <td>Start of Trial, Trigger at t=0s</td> </tr> <tr> <td>786</td> <td>Cross on screen (BCI experiment)</td> </tr> <tr> <td>33282</td> <td>Beep</td> </tr> <tr> <td>769</td> <td>class1, Left hand - cue onset</td> </tr> <tr> <td>770</td> <td>class2, Right hand - cue onset</td> </tr> <tr> <td>781</td> <td>Feedback (continuous) - onset</td> </tr> <tr> <td>800</td> <td>End Of Trial</td> </tr> <tr> <td>1010</td> <td>End Of Session</td> </tr> <tr> <td>33281</td> <td>Train</td> </tr> <tr> <td>32770</td> <td>Experiment Stop</td> </tr> </tbody> </table> <p> </p> <p><strong>Directory Tree:</strong></p> <p>ROOT<br> | chanlocs.locs<br> |<br> |<br> +--- USER #<br> | +---SESSION #<br> | | +---CONDITION #<br> | | | \---RESTING_STATE<br> | | | +---1st_PERSON<br> | | | | TRAINING<br> | | | | ONLINE<br> | | | +---3rd_PERSON<br> | | | | TRAINING<br> | | | | ONLINE</p>
The Effect of Motor Imagery on Preventing Volleyball Players During Jumping and Landing
ClinicalTrials.gov study NCT07009080. IPD Sharing: NO. Countries: 1. Publications: 15.
Effect of Structured Progressive Task-Oriented Circuit Class Training With Motor Imagery on Gait in Stroke
ClinicalTrials.gov study NCT03436810. IPD Sharing: NO. Countries: 1. Publications: 18.
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OpenNeuro
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