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259 results for “Motor control”
SFB1451 - key mechanisms of motor control in health and disease
<p>Note: this dataset has been deprecated in favour of <a href="https://github.com/sfb1451/all-datasets" target="_blank" rel="noopener">sfb1451/all-datasets</a>; see <a href="https://doi.org/10.5281/zenodo.11119424">10.5281/zenodo.11119424</a><br><br>This dataset indexes projects of the Collaborative Research Centre 1451 (CRC1451; dt: Sonderforschungsbereich, SFB1451). This collaborative research centre brings together neuroscientists investigating genetic factors, cellular, and synaptic as well as systems/neural network processes underlying motor control in animals and humans, in both health and neuropsychiatric diseases. All investigators are committed to the CRC's multi-faceted, iterative, and integrative agenda with the long-term goal of identifying the essential mechanisms underlying normal and pathological motor control.</p>
SFB1451 - key mechanisms of motor control in health and disease
This is a collection of datasets created within the Collaborative Research Center 1451. The "superdataset" contains information about datasets stored in various locations. This information can be used to obtain programmatic, fine-grained data access to the level of individual files, depending on the level of detail provided by dataset authors, storage location, and access restrictions. The Collaborative Research Center 1451 (CRC1451; dt: Sonderforschungsbereich, SFB1451) brings together neuroscientists investigating genetic factors, cellular, and synaptic as well as systems/neural network processes underlying motor control in animals and humans, in both health and neuropsychiatric diseases. All investigators are committed to the CRC's multi-faceted, iterative, and integrative agenda with the long-term goal of identifying the essential mechanisms underlying normal and pathological motor control.
Visual perturbation of balance suggests impaired motor control but intact visuomotor processing in Parkinson's disease, J Neurophysiol (2021): Data
<p>Data set accompanying the publication:</p> <p>Engel, D., Student, J., Schwenk, J., Morris, A. P., Waldthaler, J., Timmermann, L., & Bremmer, F. (2021). Visual perturbation of balance suggests impaired motor control but intact visuomotor processing in Parkinson's disease. <em>Journal of neurophysiology</em>, <em>126</em>(4), 1076–1089. https://doi.org/10.1152/jn.00183.2021</p>
Current Harmonics Minimization of PMSM Based on Iterative Learning Control and Neural Networks: Motor Data
<p>The provided motor data corresponds to an electrical machine with 24 stator slots and 16 poles. As is common in electrical machines, this motor generates unwanted flux and current harmonics. However, the accompanying paper presents an effective solution to suppress these harmonics through the combined use of Iterative Learning Control (ILC) and Neural Networks (NNs).</p> <p>The ILC method demonstrates proficient compensation for harmonics during operations with constant speed and current reference values. Additionally, Neural Networks are trained with data derived from ILC, proving to be highly effective in suppressing harmonics even during transient operation. The simulation model used in the study is based on flux and torque maps, dependent on dq-currents and the electrical angle. These maps are obtained from Finite Element Method (FEM) simulations of an interior permanent magnet synchronous machine (IPM) and are openly published here, intended to facilitate other researchers in making direct comparisons with their own methodologies.</p> <p>Simulation results presented in the paper confirm that the integration of ILC and NNs leads to superior elimination of current harmonics during transient operations compared to using ILC alone.<br> If you use the provided maps and motor data, kindly cite the associated paper for reference: https://doi.org/10.3390/machines11080784, https://www.mdpi.com/2075-1702/11/8/784</p>
Neural structure of a sensory decoder for motor control
<p>Data associated with 'Neural structure of a sensory decoder for motor control.' Egger, SW and Lisberger, SG. <em>Nature Communications</em>, 2022.</p>
Lower complexity of motor primitives ensures robust control of high-speed human locomotion
<p>Walking and running are mechanically and energetically different locomotion modes. For selecting one or another, speed is a parameter of paramount importance. Yet, both are likely controlled by similar low-dimensional neuronal networks that reflect in patterned muscle activations called muscle synergies. Here, we investigated how humans synergistically activate muscles during locomotion at different submaximal and maximal speeds. We analysed the duration and complexity (or irregularity) over time of motor primitives, the temporal components of muscle synergies. We found that the challenge imposed by controlling high-speed locomotion forces the central nervous system to produce muscle activation patterns that are wider and less complex relative to the duration of the gait cycle. The motor modules, or time-independent coefficients, were redistributed as locomotion speed changed. These outcomes show that robust locomotion control at challenging speeds is achieved by modulating the relative contribution of muscle activations and producing less complex and wider control signals, whereas slow speeds allow for more irregular control.</p> <p> </p> <p>In this supplementary data set we made available: a) the metadata with anonymized participant information, b) the raw EMG, c) the touchdown and lift-off timings of the recorded limb, d) the filtered and time-normalized EMG, e) the muscle synergies extracted via NMF and f) the code to process the data, including the scripts to calculate the Higuchi's fractal dimension (HFD) of motor primitives. In total, 180 trials from 30 participants are included in the supplementary data set.</p> <p>The file “metadata.dat” is available in ASCII and RData format and contains:</p> <ul> <li>Code: the participant’s code</li> <li>Group: the experimental group in which the participant was involved (G1 = walking and submaximal running; G2 = submaximal and maximal running)</li> <li>Sex: the participant’s sex (M or F)</li> <li>Speeds: the type of locomotion (W for walking or R for running) and speed at which the recordings were conducted in 10*[m/s]</li> <li>Age: the participant’s age in years</li> <li>Height: the participant’s height in [cm]</li> <li>Mass: the participant’s body mass in [kg]</li> <li>PB: 100 m-personal best time (for G2).</li> </ul> <p>The "RAW_DATA.RData" R list consists of elements of S3 class "EMG", each of which is a human locomotion trial containing cycle segmentation timings and raw electromyographic (EMG) data from 13 muscles of the right-side leg. Cycle times are structured as data frames containing two columns that correspond to touchdown (first column) and lift-off (second column). Raw EMG data sets are also structured as data frames with one row for each recorded data point and 14 columns. The first column contains the incremental time in seconds. The remaining 13 columns contain the raw EMG data, named with the following muscle abbreviations: ME = gluteus medius, MA = gluteus maximus, FL = tensor fasciæ latæ, RF = rectus femoris, VM = vastus medialis, VL = vastus lateralis, ST = semitendinosus, BF = biceps femoris, TA = tibialis anterior, PL = peroneus longus, GM = gastrocnemius medialis, GL = gastrocnemius lateralis, SO = soleus. Please note that the following trials include less than 30 gait cycles (the actual number shown between parentheses): P16_R_83 (20), P16_R_95 (25), P17_R_28 (28), P17_R_83 (24), P17_R_95 (13), P18_R_95 (23), P19_R_95 (18), P20_R_28 (25), P20_R_42 (27), P20_R_95 (25), P22_R_28 (23), P23_R_28(29), P24_R_28 (28), P24_R_42 (29), P25_R_28 (29), P25_R_95 (28), P26_R_28 (29), P26_R_95 (28), P27_R_28 (28), P27_R_42 (29), P27_R_95 (24), P28_R_28 (29), P29_R_95 (17). All the other trials consist of 30 gait cycles. Trials are named like “P20_R_20,” where the characters “P20” indicate the participant number (in this example the 20th), the character “R” indicate the locomotion type (W=walking, R=running), and the numbers “20” indicate the locomotion speed in 10*m/s (in this case the speed is 2.0 m/s). The filtered and time-normalized emg data is named, following the same rules, like “FILT_EMG_P03_R_30”.</p> <p><strong>Old versions not compatible with the R package <a href="https://CRAN.R-project.org/package=musclesyneRgies">musclesyneRgies</a></strong></p> <p>The files containing the gait cycle breakdown are available in RData format, in the file named “CYCLE_TIMES.RData”. The files are structured as data frames with as many rows as the available number of gait cycles and two columns. The first column named “touchdown” contains the touchdown incremental times in seconds. The second column named “stance” contains the duration of each stance phase of the right foot in seconds. Each trial is saved as an element of a single R list. Trials are named like “CYCLE_TIMES_P20_R_20,” where the characters “CYCLE_TIMES” indicate that the trial contains the gait cycle breakdown times, the characters “P20” indicate the participant number (in this example the 20th), the character “R” indicate the locomotion type (W=walking, R=running), and the numbers “20” indicate the locomotion speed in 10*m/s (in this case the speed is 2.0 m/s). Please note that the following trials include less than 30 gait cycles (the actual number shown between parentheses): P16_R_83 (20), P16_R_95 (25), P17_R_28 (28), P17_R_83 (24), P17_R_95 (13), P18_R_95 (23), P19_R_95 (18), P20_R_28 (25), P20_R_42 (27), P20_R_95 (25), P22_R_28 (23), P23_R_28(29), P24_R_28 (28), P24_R_42 (29), P25_R_28 (29), P25_R_95 (28), P26_R_28 (29), P26_R_95 (28), P27_R_28 (28), P27_R_42 (29), P27_R_95 (24), P28_R_28 (29), P29_R_95 (17).</p> <p>The files containing the raw, filtered and the normalized EMG data are available in RData format, in the files named “RAW_EMG.RData” and “FILT_EMG.RData”. The raw EMG files are structured as data frames with as many rows as the amount of recorded data points and 13 columns. The first column named “time” contains the incremental time in seconds. The remaining 12 columns contain the raw EMG data, named with muscle abbreviations that follow those reported above. Each trial is saved as an element of a single R list. Trials are named like “RAW_EMG_P03_R_30”, where the characters “RAW_EMG” indicate that the trial contains raw emg data, the characters “P03” indicate the participant number (in this example the 3rd), the character “R” indicate the locomotion type (see above), and the numbers “30” indicate the locomotion speed (see above). The filtered and time-normalized emg data is named, following the same rules, like “FILT_EMG_P03_R_30”.</p> <p>The files containing the muscle synergies extracted from the filtered and normalized EMG data are available in RData format, in the files named “SYNS_H.RData” and “SYNS_W.RData”. The muscle synergies files are divided in motor primitives and motor modules and are presented as direct output of the factorisation and not in any functional order. Motor primitives are data frames with 6000 rows and a number of columns equal to the number of synergies (which might differ from trial to trial) plus one. The rows contain the time-dependent coefficients (motor primitives), one column for each synergy plus the time points (columns are named e.g. “time, Syn1, Syn2, Syn3”, where “Syn” is the abbreviation for “synergy”). Each gait cycle contains 200 data points, 100 for the stance and 100 for the swing phase which, multiplied by the 30 recorded cycles, result in 6000 data points distributed in as many rows. This output is transposed as compared to the one discussed in the methods section to improve user readability. Each set of motor primitives is saved as an element of a single R list. Trials are named like “SYNS_H_P12_W_07”, where the characters “SYNS_H” indicate that the trial contains motor primitive data, the characters “P12” indicate the participant number (in this example the 12th), the character “W” indicate the locomotion type (see above), and the numbers “07” indicate the speed (see above). Motor modules are data frames with 12 rows (number of recorded muscles) and a number of columns equal to the number of synergies (which might differ from trial to trial). The rows, named with muscle abbreviations that follow those reported above, contain the time-independent coefficients (motor modules), one for each synergy and for each muscle. Each set of motor modules relative to one synergy is saved as an element of a single R list. Trials are named like “SYNS_W_P22_R_20”, where the characters “SYNS_W” indicate that the trial contains motor module data, the characters “P22” indicate the participant number (in this example the 22nd), the character “W” indicates the locomotion type (see above), and the numbers “20” indicate the speed (see above). Given the nature of the NMF algorithm for the extraction of muscle synergies, the supplementary data set might show non-significant differences as compared to the one used for obtaining the results of this paper.</p> <p>The files containing the HFD calculated from motor primitives are available in RData format, in the file named “HFD.RData”. HFD results are presented in a list of lists containing, for each trial, 1) the HFD, and 2) the interval time <em>k</em> used for the calculations. HFDs are presented as one number (mean HFD of the primitives for that trial), as are the interval times <em>k</em>. Trials are named like “HFD_P01_R_95”, where the characters “HFD” indicate that the trial contains HFD data, the characters “P01” indicate the participant number (in this example the 1st), the character “R” indicates the locomotion type (see above), and the numbers “95” indicate the speed (see above).</p> <p>All the code used for the pre-processing of EMG data, the extraction of muscle synergies and the calculation of HFD is available in R format. Explanatory comments are profusely present throughout the script “muscle_synergies.R”.</p>
Data from: Sensing, feeling, and regulating: Investigating the association of focal brain damage with voluntary respiratory and motor control
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Control strategies and electric drive design of induction and synchronous reluctance motors for e-mobility
<p>Second webinar of ReFreeDrive's webinar series. This webinar presents the control algorithms developed for both technologies, as well as the electric drive design (power electronics).</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>
Effects of Attentional Focus on Motor Performance and Physiology in a Slow-Motion Violin Bow-Control Task: Evidence for the Constrained Action Hypothesis in Bowed String Technique
<p>The constrained action hypothesis states that focusing attention on action outcomes rather than body movement improves motor performance. Dexterity of motor control is key to successful music performance, making this a highly relevant topic to music education. We investigated effects of focus of attention (FOA) on motor skill performance and EMG muscle activity in a violin bowing task among experienced and novice upper strings players. Following a pedagogically informed exercise, participants attempted to produce single oscillations of the string at a time under three FOA: internal (on arm movement), external (on sound produced), and somatic (on string resistance). Experienced players’ number of bow slips was significantly reduced under somatic focus relative to internal, although number of successful oscillations was not affected. Triceps electromyographic activity was also significantly lower in somatic compared to internal foci for both expertise groups, consistent with physiological understandings of FOA effects. Participants’ reported thoughts during the experiment provided insight into whether aspects of constrained action may be evident in performers’ conscious thinking. These results provide novel support for the constrained action hypothesis in violin bow control, suggesting a somatic FOA as a promising performance-enhancing strategy for bowed string technique.</p>
Data for the project 'Feasibility and Effectiveness of a Personalized Home-Based Motor-Cognitive Training Program in Community-Dwelling Older Adults: a Pragmatic Pilot Randomized Controlled Trial'
<p>Data for the project 'Feasibility and Effectiveness of a Personalized Home-Based Motor-Cognitive Training Program in Community-Dwelling Older Adults: a Pragmatic Pilot Randomized Controlled Trial' including four complete datasets with all variables collected in this project and a corresponding README file ('README-File_Data_Feasibility and Effectiveness of a Personalized Home-Based Motor-Cognitive Training Program in Community-Dwelling Older Adults: a Pragmatic Pilot Randomized Controlled Trial.txt'). The latter provides (1) general information, (2) sharing and access information, (3) data and file overview, (4) methodological information, and (5) data-specific information.</p>
Multi-objective control in human walking: insight gained through simultaneous degradation of energetic and motor regulation systems
<p>See ReadMe.txt</p>
Motor sources of dual-task interference: Evidence for effector-based prioritization in dual-task control
<p>Data of "Motor sources of dual-task interference: Evidence for effector-based prioritization in dual-task control", Hoffmann, Pieczykolan, Koch, & Huestegge.</p> <p>Dual-task costs in error rates and reaction times in six pairwise combination groups of effector systems.</p>
Effects of Coordinative Exercise on Physical Fitness, Motor Competence, and Inhibitory Control in Preschoolers
ClinicalTrials.gov study NCT06631248. IPD Sharing: YES. Countries: 1. Publications: 3.
Effectiveness of Intelligent Rehabilitation Robot Training System Combined With Repetitive Facilitative Exercise on Upper Limb Motor Function After Stroke: a Randomized Control Trial.
ClinicalTrials.gov study NCT06435624. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Placebo-controlled Study in Patients With Parkinson's Disease to Evaluate the Effect of Rotigotine on Non-motor Symptoms
ClinicalTrials.gov study NCT01300819. IPD Sharing: Not stated. Countries: 12. Publications: 1.
The Effects of Targeted Phantom Motor Execution on Phantom Limb Control
ClinicalTrials.gov study NCT05247827. IPD Sharing: NO. Countries: 1. Publications: 22.
Impact of Robotic Glove Use on Quality of Life, Grip Strength and Fine Motor Control in ALS
ClinicalTrials.gov study NCT07298486. IPD Sharing: YES. Countries: 1. Publications: 4.
Miniature linear and split-belt treadmills reveal mechanisms of adaptive motor control in walking Drosophila
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Data from: Speech motor cortex enables BCI cursor control and click
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.