We thank Mr. Zhengyang Wang for reshaping the scripts, shooting and editing the video, and Mr. Ziyuan Ye for editing the video. This study got support from CIT Endowment &#38; National Science Foundation (to JCD),&#160;Boswell fellowship (to QL) and High-level University Fund (No. G02386301, G02386401), Guangdong Natural Science Foundation Joint Fund (No. 2019A1515111038).&#160;

The development and application of the protocol were approved by the California Institute of Technology Institutional Review Board (IRB) and the Southern University of Science and Technology IRB. The subject provided informed consent prior to any procedures being performed.
1. System preparation and setup
<ol>
	<li>The recommended basic hardware is a&#160;2 GHz dual-core processor and 4 GB of system memory.</li>
	<li>Build the gaming platform under the Unity platform, while using C# programing language. The Logitech gaming wheel driver and Logitech Steering Wheel SDK are needed for gaming platform development.</li>
	<li>The gaming platform executable files only support Windows 10 Operating System (OS). Therefore, on a PC running Windows 10, download and install the corresponding racing wheel driver. Then download the compressed WheelCon software (https://github.com/Doyle-Lab/WheelCon/archive/master.zip) and extract the files to the local hard drive.</li>
	<li>Mount the racing wheel securely at the sitting level in front of a monitor, and then connect the wheel&#39;s USB cable to the PC and the power adapter to an outlet.</li>
	<li>Start the driver GUI to test for correct input readout and force feedback. Importantly, keep the driver GUI running in the background during the test.</li>
	<li>To start the program, double-click on WheelCon.exe in the &#39;\WheelCon-master\Executable &#38; Output Files\&#39; directory.</li>
	<li>On the configuration screen, choose settings for monitor and click Play! (Figure 2a). The main menu will appear. Make sure the display size and location are as specified. 
	NOTE: The &#39;Wheel Sensitivity&#39; value, defining cursor speed, ranges from 0 to 1, and defaults to 0.5. In case the range of motion afforded by the racing wheel does not suit specific task parameters, adjust this value. For example, decrease the sensitivity for the aging population. However, for comparing between tasks, it is necessary to keep this value constant for the battery and across groups.</li>
</ol>
2. Task implementation
<ol>
	<li>Fitts&#39; law reaching game 
	NOTE: The Fitts&#8217; law reaching game simulates the reaching process. The subject requires to turn the wheel to place the vertical line into the desired region (Figure 2d).
	<ol>
		<li>Seat the subject comfortably behind the wheels. Adjust the wheel height if necessary.</li>
		<li>On the main menu, click Fitts&#39; Law Task (Figure 2b) and type in a name for the output file indicating subject identification and task information on the textbox.</li>
		<li>Click on Select File, choose&#160;Fitt&#39;s Law.txt in the &#39;\WheelCon-master\ Demo Input Files\&#39; directory, and then click Begin Game.</li>
		<li>Instruct the subject to move the green vertical line with the wheel to place it within the gray zone. This task serves to familiarize the subject with maneuvering the wheel, as well as with the color convention used throughout different tasks.</li>
	</ol>
	</li>
	<li>Mountain bike tasks 
	NOTE: The mountain bike task is a combination of pursuit and compensatory tracking task. It simulates riding a mountain bike down a steep, twisted and bumpy trail. The subject can see the trail and turn the wheel to track it, while a motor can torque the wheel to mimic invisible bumps in the trial (Figure 2e).
	<ol>
		<li>Game 1: Testing the effect of the visual delay 
		NOTE: In this game, the length of the look-ahead window (advanced warning vs. delay) is manipulated.
		<ol>
			<li>On the main menu, click Mountain Bike Task (Figure 2c) and type in a name for the output file indicating subject identification and task information on the textbox.</li>
			<li>Click on Select File, choose Vision_Delay.txt in the &#39;\WheelCon-master\ Demo Input Files\&#39; directory, and then click on Begin Game.</li>
			<li>Instruct the subject to move the green vertical line with the wheel in order to track the part of the gray trail that intersects the purple horizontal line.</li>
		</ol>
		</li>
		<li>Game 2: Testing the effect of action delay 
		NOTE: In this game, a delay of various lengths is added between wheel movement and action output.
		<ol>
			<li>On the main menu, click on Mountain Bike Task and type in a name for the output file indicating subject identification and task information on the textbox.</li>
			<li>Click on Select File, choose Action_Delay.txt in the &#39;\WheelCon-master\ Demo Input Files\&#39; directory, and then click Begin Game.</li>
			<li>Instruct the subject to move the green vertical line with the wheel in order to track the part of the gray trail that intersects the purple horizontal line.</li>
		</ol>
		</li>
		<li>Game 3: Testing the effect of visual quantization 
		NOTE: In this game, visual input is quantized to limit the data rate.
		<ol>
			<li>On the main menu, click on Mountain Bike Task and type in a name for the output file indicating subject identification and task information on the textbox.</li>
			<li>Click on Select File, choose Vision Quantization.txt in the &#39;\WheelCon-master\ Demo Input Files\&#39; directory, and then click Begin Game.</li>
			<li>Instruct the subject to move the green vertical line with the wheel in order to track the part of the gray trail that intersects the purple horizontal line.</li>
		</ol>
		</li>
		<li>Game 4: Testing the effect of action quantization 
		NOTE: In this game, action output is quantized to limit the data rate.
		<ol>
			<li>On the main menu, click on Mountain Bike Task and type in a name for the output file indicating subject identification and task information on the textbox.</li>
			<li>Click on Select File, choose Action Quantization.txt in the &#39;\WheelCon-master\ Demo Input Files\&#39; directory, and then click Begin Game.</li>
			<li>Instruct the subject to move the green vertical line with the wheel in order to track the part of the gray trail that intersects the purple horizontal line.</li>
		</ol>
		</li>
		<li>Game 5: Testing the effect of bump and trail disturbance 
		NOTE: This task consists of three scenarios: 
		a) &#34;Bumps&#34;, tracking a constant trail subject despite torque disturbances on the wheel that mimic hitting bumps when riding a mountain bike; 
		b) &#34;Trail&#34;, tracking a moving trail with random turns but without bumps; 
		c) &#34;Trail with Bumps&#34;, tracking a moving trail with random turns and bumps.
		<ol>
			<li>On the main menu, click on Mountain Bike Task and type in a name for the output file indicating subject identification and task information on the textbox.</li>
			<li>Click on Select File, choose Bump &#38; Trail.txt in the &#39;\WheelCon-master\ Demo Input Files\&#39; directory, and then click Begin Game.</li>
			<li>Instruct the subject to move the green vertical line with the wheel in order to track the part of the gray trail that intersects the purple horizontal line.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
3. Data output
<ol>
	<li>Locate the TXT output file in the &#39;\WheelCon-master\Executable &#38; Output Files\MountainBikeData\&#39; directory, and then open with Matlab&#39; WheelCon Data Analysis Code.m&#39; in the &#39;\WheelCon-master\Source Code&#39; directory.</li>
	<li>Specify in the MATLAB script the folder and file_names variables according to the output file directory, and then run the script (Ctrl + Enter), and the output variables will be saved as column vectors to the Workspace. The error and control policy will be exported for each sampling time. See Table 2 for the detailed description.</li>
</ol>
4. Input file development
<ol>
	<li>Open &#39;WheelCon Mntn Bike Trail Design Code.m&#39; in the &#39;\WheelCon-master\Source Code\&#39; directory.</li>
	<li>Uncomment (Ctrl + T) the section for the desired game parameters and run the script (Ctrl + Enter). The input file will be saved in the &#39;\WheelCon-master\Source Code\&#39; directory&#39; in .txt format. Each column in the input files is one control variable. Refer to Table 1 for the list of control variables.</li>
</ol>

<ol>
	<li>Franklin, D. W., Wolpert, D. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Computational+Mechanisms+of+Sensorimotor+Control.">Computational Mechanisms of Sensorimotor Control.</a> Neuron. 72 (3), 425-442 (2011).</li><li>Bays, P. M., Wolpert, D. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Computational+principles+of+sensorimotor+control+that+minimize+uncertainty+and+variability.">Computational principles of sensorimotor control that minimize uncertainty and variability.</a> The Journal of Physiology. 578 (2), 387-396 (2007).</li><li>Desmurget, M., Grafton, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Forward+modeling+allows+feedback+control+for+fast+reaching+movements.">Forward modeling allows feedback control for fast reaching movements.</a> Trends in Cognitive Sciences. 4 (11), 423-431 (2000).</li><li>Sanger, T. D., Merzenich, M. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Computational+Model+of+the+Role+of+Sensory+Disorganization+in+Focal+Task-Specific+Dystonia.">Computational Model of the Role of Sensory Disorganization in Focal Task-Specific Dystonia.</a> Journal of Neurophysiology. 84 (5), 2458-2464 (2000).</li><li>Mohler, H., Okada, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Benzodiazepine+receptor:+demonstration+in+the+central+nervous+system.">Benzodiazepine receptor: demonstration in the central nervous system.</a> Science. 198 (4319), 849-851 (1977).</li><li>Muller, L., Chavane, F., Reynolds, J., Sejnowski, T. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cortical+travelling+waves:+mechanisms+and+computational+principles.">Cortical travelling waves: mechanisms and computational principles.</a> Nature Reviews Neuroscience. 19 (5), 255-268 (2018).</li><li>Zhang, H., Watrous, A. J., Patel, A., Jacobs, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Theta+and+Alpha+Oscillations+Are+Traveling+Waves+in+the+Human+Neocortex.">Theta and Alpha Oscillations Are Traveling Waves in the Human Neocortex.</a> Neuron. 98 (6), 1269-1281 (2018).</li><li>Blinks, J. R., R&#252;del, R., Taylor, S. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Calcium+transients+in+isolated+amphibian+skeletal+muscle+fibres:+detection+with+aequorin.">Calcium transients in isolated amphibian skeletal muscle fibres: detection with aequorin.</a> The Journal of Physiology. 277 (1), 291-323 (1978).</li><li>Gallivan, J. P., Chapman, C. S., Wolpert, D. M., Flanagan, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Decision-making+in+sensorimotor+control.">Decision-making in sensorimotor control.</a> Nature Reviews Neuroscience. 19 (9), 519-534 (2018).</li><li>Sanger, T. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Basic+and+Translational+Neuroscience+of+Childhood-Onset+Dystonia:+A+Control-Theory+Perspective.">Basic and Translational Neuroscience of Childhood-Onset Dystonia: A Control-Theory Perspective.</a> Annual Review of Neuroscience. 41 (1), 41-59 (2018).</li><li>Todorov, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optimality+principles+in+sensorimotor+control.">Optimality principles in sensorimotor control.</a> Nature Neuroscience. 7 (9), 907-915 (2004).</li><li>Todorov, E., Jordan, M. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optimal+feedback+control+as+a+theory+of+motor+coordination.">Optimal feedback control as a theory of motor coordination.</a> Nature Neuroscience. 5 (11), 1226-1235 (2002).</li><li>Wolpert, D. M., Flanagan, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Computations+underlying+sensorimotor+learning.">Computations underlying sensorimotor learning.</a> Current Opinion in Neurobiology. 37, 7-11 (2016).</li><li>Kiper, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Computational+models+and+motor+learning+paradigms:+Could+they+provide+insights+for+neuroplasticity+after+stroke?+An+overview.">Computational models and motor learning paradigms: Could they provide insights for neuroplasticity after stroke? An overview.</a> Journal of the Neurological Sciences. 369, 141-148 (2016).</li><li>Cluff, T., Crevecoeur, F., Scott, S. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tradeoffs+in+optimal+control+capture+patterns+of+human+sensorimotor+control+and+adaptation.">Tradeoffs in optimal control capture patterns of human sensorimotor control and adaptation.</a> bioRxiv. , 730713 (2019).</li><li>Nakahira, Y., Liu, Q., Bernat, N., Sejnowski, T., Doyle, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Theoretical+foundations+for+layered+architectures+and+speed-accuracy+tradeoffs+in+sensorimotor+control.">Theoretical foundations for layered architectures and speed-accuracy tradeoffs in sensorimotor control.</a> 2019 American Control Conference (ACC). , 809-814 (2019).</li><li>K&#246;rding, K. P., Wolpert, D. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bayesian+integration+in+sensorimotor+learning.">Bayesian integration in sensorimotor learning.</a> Nature. 427 (6971), 244-247 (2004).</li><li>Chambers, C., Sokhey, T., Gaebler-Spira, D., Kording, K. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+development+of+Bayesian+integration+in+sensorimotor+estimation.">The development of Bayesian integration in sensorimotor estimation.</a> Journal of Vision. 18 (12), 8 (2018).</li><li>Karmali, F., Whitman, G. T., Lewis, R. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bayesian+optimal+adaptation+explains+age-related+human+sensorimotor+changes.">Bayesian optimal adaptation explains age-related human sensorimotor changes.</a> Journal of Neurophysiology. 119 (2), 509-520 (2017).</li><li>Gori, J., Rioul, O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Information-Theoretic+Analysis+of+the+Speed-Accuracy+Tradeoff+with+Feedback.">Information-Theoretic Analysis of the Speed-Accuracy Tradeoff with Feedback.</a> 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). , 3452-3457 (2018).</li><li>Trendafilov, D., Polani, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Information-theoretic+Sensorimotor+Foundations+of+Fitts'+Law.">Information-theoretic Sensorimotor Foundations of Fitts' Law.</a> Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems. , 1-6 (2019).</li><li>Fitts, P. M., Peterson, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Information+capacity+of+discrete+motor+responses.">Information capacity of discrete motor responses.</a> Journal of Experimental Psychology. 67 (2), 103-112 (1964).</li><li>Fitts, P. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+information+capacity+of+the+human+motor+system+in+controlling+the+amplitude+of+movement.">The information capacity of the human motor system in controlling the amplitude of movement.</a> Journal of Experimental Psychology. 47 (6), 381-391 (1954).</li><li>Nakahira, Y., Matni, N., Doyle, J. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hard+limits+on+robust+control+over+delayed+and+quantized+communication+channels+with+applications+to+sensorimotor+control.">Hard limits on robust control over delayed and quantized communication channels with applications to sensorimotor control.</a> 2015 54th IEEE Conference on Decision and Control (CDC). , 7522-7529 (2015).</li><li>. Neural Correlates of Sensorimotor Control in Human Cortex: State Estimates and Reference Frames Available from: <a target="_blank" href="https://resolver.caltech.edu/CaltechTHESIS:05302019-163325527">https://resolver.caltech.edu/CaltechTHESIS:05302019-163325527</a> (2019)</li><li>Miall, R. C., Wolpert, D. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Forward+Models+for+Physiological+Motor+Control.">Forward Models for Physiological Motor Control.</a> Neural Networks. 9 (8), 1265-1279 (1996).</li><li>Battaglia-Mayer, A., Caminiti, R., Lacquaniti, F., Zago, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multiple+Levels+of+Representation+of+Reaching+in+the+Parieto-frontal+Network.">Multiple Levels of Representation of Reaching in the Parieto-frontal Network.</a> Cerebral Cortex. 13 (10), 1009-1022 (2003).</li><li>Scott, S. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optimal+feedback+control+and+the+neural+basis+of+volitional+motor+control.">Optimal feedback control and the neural basis of volitional motor control.</a> Nature Reviews Neuroscience. 5 (7), 532-545 (2004).</li><li>Saunders, J. A., Knill, D. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Humans+use+continuous+visual+feedback+from+the+hand+to+control+fast+reaching+movements.">Humans use continuous visual feedback from the hand to control fast reaching movements.</a> Experimental Brain Research. 152 (3), 341-352 (2003).</li><li>Insperger, T., Milton, J., St&#233;p&#225;n, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Acceleration+feedback+improves+balancing+against+reflex+delay.">Acceleration feedback improves balancing against reflex delay.</a> Journal of The Royal Society Interface. 10 (79), 20120763 (2013).</li><li>Birbaumer, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Breaking+the+silence:+Brain-computer+interfaces+(BCI)+for+communication+and+motor+control.">Breaking the silence: Brain-computer interfaces (BCI) for communication and motor control.</a> Psychophysiology. 43 (6), 517-532 (2006).</li><li>Liu, Q., Farahibozorg, S., Porcaro, C., Wenderoth, N., Mantini, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Detecting+large-scale+networks+in+the+human+brain+using+high-density+electroencephalography.">Detecting large-scale networks in the human brain using high-density electroencephalography.</a> Human Brain Mapping. 38 (9), 4631-4643 (2017).</li><li>Nicolelis, M. A. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Brain-machine+interfaces+to+restore+motor+function+and+probe+neural+circuits.">Brain-machine interfaces to restore motor function and probe neural circuits.</a> Nature Reviews Neuroscience. 4 (5), 417-422 (2003).</li><li>Nordin, A. D., Hairston, W. D., Ferris, D. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Faster+Gait+Speeds+Reduce+Alpha+and+Beta+EEG+Spectral+Power+From+Human+Sensorimotor+Cortex.">Faster Gait Speeds Reduce Alpha and Beta EEG Spectral Power From Human Sensorimotor Cortex.</a> IEEE Transactions on Biomedical Engineering. 67 (3), 842-853 (2020).</li><li>Hallett, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Transcranial+magnetic+stimulation+and+the+human+brain.">Transcranial magnetic stimulation and the human brain.</a> Nature. 406 (6792), 147-150 (2000).</li><li>Madhavan, S., Weber, K. A., Stinear, J. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Non-invasive+brain+stimulation+enhances+fine+motor+control+of+the+hemiparetic+ankle:+implications+for+rehabilitation.">Non-invasive brain stimulation enhances fine motor control of the hemiparetic ankle: implications for rehabilitation.</a> Experimental Brain Research. 209 (1), 9-17 (2011).</li><li>Denoyelle, N., Pouget, F., Vi&#233;ville, T., Alexandre, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> VirtualEnaction: A Platform for Systemic Neuroscience Simulation. , (2014).</li><li>Butti, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Virtual+Reality+Social+Prediction+Improvement+and+Rehabilitation+Intensive+Training+(VR-SPIRIT)+for+paediatric+patients+with+congenital+cerebellar+diseases:+study+protocol+of+a+randomised+controlled+trial.">Virtual Reality Social Prediction Improvement and Rehabilitation Intensive Training (VR-SPIRIT) for paediatric patients with congenital cerebellar diseases: study protocol of a randomised controlled trial.</a> Trials. 21 (1), 82 (2020).</li><li>McCall, J. V., Ludovice, M. C., Blaylock, J. A., Kamper, D. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Platform+for+Rehabilitation+of+Finger+Individuation+in+Children+with+Hemiplegic+Cerebral+Palsy.">A Platform for Rehabilitation of Finger Individuation in Children with Hemiplegic Cerebral Palsy.</a> 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR). , 343-348 (2019).</li><li>Demers, M., Levin, M. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Kinematic+Validity+of+Reaching+in+a+2D+Virtual+Environment+for+Arm+Rehabilitation+After+Stroke.">Kinematic Validity of Reaching in a 2D Virtual Environment for Arm Rehabilitation After Stroke.</a> IEEE Transactions on Neural Systems and Rehabilitation Engineering. 28 (3), 679-686 (2020).</li><li>Zbytniewska, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Design+and+Characterization+of+a+Robotic+Device+for+the+Assessment+of+Hand+Proprioceptive,+Motor,+and+Sensorimotor+Impairments.">Design and Characterization of a Robotic Device for the Assessment of Hand Proprioceptive, Motor, and Sensorimotor Impairments.</a> 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR). , 441-446 (2019).</li><li>Delp, S. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=OpenSim:+Open-Source+Software+to+Create+and+Analyze+Dynamic+Simulations+of+Movement.">OpenSim: Open-Source Software to Create and Analyze Dynamic Simulations of Movement.</a> IEEE Transactions on Biomedical Engineering. 54 (11), 1940-1950 (2007).</li><li>Gray, W. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Plateaus+and+Asymptotes:+Spurious+and+Real+Limits+in+Human+Performance.">Plateaus and Asymptotes: Spurious and Real Limits in Human Performance.</a> Current Directions in Psychological Science. 26 (1), 59-67 (2017).</li></ol>

The authors disclose that they have no conflicts of interest.

In this paper, we have presented a free, open-source gaming platform, WheelCon, for studying the effects of delay, quantization, disturbance, and layered feedback loops in human sensorimotor control. We have shown the hardware, the software, and the GUI. The settings of a single sensorimotor control loop with delay and quantization have been implemented, which allows us to measure the effects of delay, quantization, and disturbance in sensorimotor control. The experimental results are well in line with the prediction fro...

The human sensorimotor control system is extremely robust1, although the sensing is distributed, variable, sparse, quantized, noisy and delayed2,3,4; the computing in the central nervous system is slow5,6,7; and the muscle actuation fatigues and saturates8. Many computational theoretical models have been proposed to explain the complicated human sensorimotor control process4,9,10,11,12,13,14, which is a tradeoff process in human reach and response15,16. For example, feedback control theory predicts the optimal control policy12, Bayesian theory models sensorimotor learning17,18,19 and information theory sensorimotor foundation20,21. In contrast to the abundance of theoretical models, experimental platforms capable of manipulating important components of multiple feedback loops lack development. This is in part due to the fact that designing a platform to bridge and test these aspects of sensorimotor control requires a diverse range of expertise, extending from motor control theory, signal processing, and interaction, all the way to computer graphics and programming. Researchers often develop their own custom hardware/software systems to characterize human sensorimotor control performance, which can limit the ability to compare/contrast and integrate datasets across research groups. The development of an easy-to-use and validated system could broaden the quantitative characterization of sensorimotor control.
In this paper, we present the WheelCon platform, a novel, free and open-source platform to design video games for a virtual environment that noninvasively simulates a Fitts&#8217; Law reaching game and a mountain bike task with downing a steep, twisting and bumpy trail. The Fitts&#8217; law for reaching task quantifies the tradeoff between speed and accuracy in which the time required for reaching a target of width at distance scales as22,23. The &#39;mountain-bike task&#39; is a combination of a pursuit and compensatory tracking task, which are two classic components of research on human sensorimotor performance, especially in terms of studying feedback loops.
WheelCon contains the highly demanded basic components presented in each theory: delay, quantization, noise, disturbance, and multiple feedback loops. It is a potential tool for studying the following diverse questions in human sensorimotor control:
&#8226; How the human sensorimotor system deals with the delay and quantization in neural signaling, which is fundamentally constrained by the limited resources (such as the space and metabolic costs) in the brain24,25; &#8226; How neural correlation in the human cortex with sensorimotor control26; &#8226; How humans handle the unpredictable, external disturbances in sensorimotor control27; &#8226; How the hierarchical control loops layered and integrated within human sensorimotor system16,28,29; &#8226; The consequence of the delay and quantization in human visual feedback30 and reflex feedback31 in sensorimotor control; &#8226; The optimal policy and strategy for sensorimotor learning under delay and quantization16,17,24,29.
WheelCon integrates with a steering wheel and can simulate game conditions that manipulate the variables in these questions, such as signaling delay, quantization, noise, and disturbance, while recording the dynamic control policy and system errors. It also allows researchers to study the layered architecture in sensorimotor control. In the example of riding a mountain bike, two control layers are involved in this task: the high-layer plan and the low-layer reflex. For visible disturbances (i.e., the trail), we plan before the disturbance arrives. For disturbances unknown in advance (i.e., small bumps), the control relies on delayed reflexes. Feedback control theory proposes that effective layered architectures can integrate the higher layers&#39; goals, plans, decisions with the lower layers&#39; sensing, reflex, and action24. WheelCon provides experimental tools to induce distinctive disturbances in the plan and reflex layers separately for testing such a layered architecture (Figure 1).
We provide a cheap, easy to use and flexible to program platform, WheelCon that bridges the gap between theoretical and experimental studies on neuroscience. To be specific, it can be used for examining the effects of delay, quantization, disturbance, potentially speed-accuracy tradeoffs. The variables that can be manipulated in control loops are shown in Table 1. It can also be applied for studying decision making and multiplexing ability across different control layers in human sensorimotor control. Moreover, WheelCon is compatible with noninvasive neural recordings, such as electroencephalography (EEG), to measure the neural response during sensorimotor control32,33,34,35, and the non-invasive brain stimulation techniques, such as Transcranial Electrical Stimulation (tES) and Transcranial Magnetic Stimulation (TMS), to manipulate the neural activity36,37.

<table><tbody><tr><td>Gaming Wheel</td><td>Logitech</td><td></td><td></td></tr><tr><td>Windows 10 OS</td><td>Microsoft</td><td></td><td></td></tr></tbody></table>

wheelcon: a wheel control-based gaming platform for studying human sensorimotor control

Feedback control theory has been extensively implemented to theoretically model human sensorimotor control. However, experimental platforms capable of manipulating important components of multiple feedback loops lack development. This paper describes WheelCon, an open-source platform aimed at resolving such insufficiencies. Using only a computer, a standard display, and inexpensive gaming steering wheel equipped with a force feedback motor, WheelCon safely simulates the canonical sensorimotor task of riding a mountain bike down a steep, twisting, bumpy trail. The platform provides flexibility, as will be demonstrated in the demos provided, so that researchers may manipulate the disturbances, delay, and quantization (data rate) in the layered feedback loops, including a high-level advanced plan layer and a low-level delayed reflex layer. In this paper, we illustrate WheelCon&#39;s graphical user interface (GUI), the input and output of existing demos, and how to design new games. In addition, we present the basic feedback model and the experimental results from the demo games, which align well with the model&#39;s prediction. The WheelCon platform can be downloaded at https://github.com/Doyle-Lab/WheelCon. In short, the platform is featured to be cheap, simple to use, and flexible to program for effective sensorimotor neuroscience research and control engineering education.

Modelling Feedback Control
We show a simplified feedback control model shown in Figure 1. The system dynamics is given by:
<img alt="Equation 1" src="/files/ftp_upload/61092/61092equ01.jpg" />
where x(t) is the error at time t, r(t) is the trail disturbance w(t), is the bump disturbance, and u(...

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WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

WheelCon is a novel, free and open-source platform to design video games that noninvasively simulates mountain biking down a steep, twisting, bumpy trail. It contains components presenting in human sensorimotor control (delay, quantization, noise, disturbance, and multiple feedback loops) and allows researchers to study the layered architecture in sensorimotor control.

Feedback control theory has been extensively implemented to theoretically model human sensorimotor control. However, experimental platforms capable of ...

wheelcon-wheel-control-based-gaming-platform-for-studying-human

Research

JoVE Journal

Neuroscience

4.9K Views.  Southern University of Science and Technology. Menselijke sensorische motor controle systeem is zeer robuust, hoewel de detectie is verdeeld, schaars, gekwantificeerd, lawaaierig, en vertraagd. Het computergebruik in het centrale zenuwstelsel is traag, de spieractuatie vermoeien en verzadigt. Om deze oorzakelijke factoren en de gevolgen daarvan voor de systeemprestaties van sensorische motorbesturing te bestuderen, is het belangrijk om een gebruiksvriendelijk experimenteel platform te ontwikkelen dat deze factoren manipuleert en de presta...

Video: WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

The non-stationary nature and variability of neuronal signals is a fundamental problem in brain-machine interfacing. We developed a brain-machine interface to assess the robustness of different control-laws applied to a closed-loop image stabilization task. Taking advantage of the well-characterized fly visuomotor pathway we record the electrical activity from an identified, motion-sensitive neuron, H1, to control the yaw rotation of a two-wheeled robot. The robot is equipped with 2 high-speed video cameras providing visual motion input to a fly placed in front of 2 CRT computer monitors. The activity of the H1 neuron indicates the direction and relative speed of the robot's rotation. The neural activity is filtered and fed back into the steering system of the robot by means of proportional and proportional/adaptive control. Our goal is to test and optimize the performance of various control laws under closed-loop conditions for a broader application also in other brain machine interfaces.

We use a closed-loop fly-machine interface to investigate general principles in neuronal control.

The non-stationary nature and variability of neuronal signals is a fundamental problem in brain-machine interfacing. We developed a brain-machine ...

A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments

Rodents have been traditionally used as a standard animal model in laboratory experiments involving a myriad of sensory, cognitive, and motor tasks. Higher cognitive functions that require precise control over sensorimotor responses such as decision-making and attentional modulation, however, are typically assessed in nonhuman primates. Despite the richness of primate behavior that allows multiple variants of these functions to be studied, the rodent model remains an attractive, cost-effective alternative to primate models. Furthermore, the ability to fully automate operant conditioning in rodents adds unique advantages over the labor intensive training of nonhuman primates while studying a broad range of these complex functions.
Here, we introduce a protocol for operantly conditioning rats on performing working memory tasks. During critical epochs of the task, the protocol ensures that the animal's overt movement is minimized by requiring the animal to 'fixate' until a Go cue is delivered, akin to nonhuman primate experimental design. A simple two alternative forced choice task is implemented to demonstrate the performance. We discuss the application of this paradigm to other tasks.

A fully automated protocol for rodent operant conditioning is proposed. The protocol relies on precise temporal control of behavioral events to investigate the extent to which this control influences neural activity underlying sensorimotor integration and cognitive control experiments.

Rodents have been traditionally used as a standard animal model in laboratory experiments involving a myriad of sensory, cognitive, and motor tasks. ...

Behavior

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

Advances in robotic systems have resulted in prostheses for the upper limb that can produce multifunctional movements. However, these sophisticated systems require upper limb amputees to learn complex control schemes. Humans have the ability to learn new movements through imitation and other learning strategies. This protocol describes a structured rehabilitation method, which includes imitation, repetition, and reinforcement learning, and aims to assess if this method can improve multifunctional prosthetic control. A left below elbow amputee, with 4 years of experience in prosthetic use, took part in this case study. The prosthesis used was a Michelangelo hand with wrist rotation, and the added features of wrist flexion and extension, which allowed more combinations of hand movements. The participant&#8217;s Southampton Hand Assessment Procedure score improved from 58 to 71 following structured training. This suggests that a structured training protocol of imitation, repetition and reinforcement may have a role in learning to control a new prosthetic hand. A larger clinical study is however required to support these findings.

As prosthetic development moves towards the goal of natural control, harnessing amputees&#8217; inherent ability to learn new motor skills may enable proficiency. This manuscript describes a structured rehabilitation protocol, which includes imitation, repetition, and reinforcement learning strategies, for improved multifunctional prosthetic control.

Advances in robotic systems have resulted in prostheses for the upper limb that can produce multifunctional movements. However, these sophisticated ...

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Skilled reaching tasks are commonly used in studies of motor skill learning and motor function under healthy and pathological conditions, but can be time-intensive and ambiguous to quantify beyond simple success rates. Here, we describe the training procedure for reach-and-pull tasks with ETH Pattus, a robotic platform for automated forelimb reaching training that records pulling and hand rotation movements in rats. Kinematic quantification of the performed pulling attempts reveals the presence of distinct temporal profiles of movement parameters such as pulling velocity, spatial variability of the pulling trajectory, deviation from midline, as well as pulling success. We show how minor adjustments in the training paradigm result in alterations in these parameters, revealing their relation to task difficulty, general motor function or skilled task execution. Combined with electrophysiological, pharmacological and optogenetic techniques, this paradigm can be used to explore the mechanisms underlying motor learning and memory formation, as well as loss and recovery of function (e.g. after stroke).

A paradigm is presented for training and analysis of an automated skilled reaching task in rats. Analysis of pulling attempts reveals distinct subprocesses of motor learning.

Skilled reaching tasks are commonly used in studies of motor skill learning and motor function under healthy and pathological conditions, but can be ...

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis

To enable intuitive operation of powered artificial legs, an interface between user and prosthesis that can recognize the user's movement intent is desired. A novel neural-machine interface (NMI) based on neuromuscular-mechanical fusion developed in our previous study has demonstrated a great potential to accurately identify the intended movement of transfemoral amputees. However, this interface has not yet been integrated with a powered prosthetic leg for true neural control. This study aimed to report (1) a flexible platform to implement and optimize neural control of powered lower limb prosthesis and (2) an experimental setup and protocol to evaluate neural prosthesis control on patients with lower limb amputations. First a platform based on a PC and a visual programming environment were developed to implement the prosthesis control algorithms, including NMI training algorithm, NMI online testing algorithm, and intrinsic control algorithm. To demonstrate the function of this platform, in this study the NMI based on neuromuscular-mechanical fusion was hierarchically integrated with intrinsic control of a prototypical transfemoral prosthesis. One patient with a unilateral transfemoral amputation was recruited to evaluate our implemented neural controller when performing activities, such as standing, level-ground walking, ramp ascent, and ramp descent continuously in the laboratory. A novel experimental setup and protocol were developed in order to test the new prosthesis control safely and efficiently. The presented proof-of-concept platform and experimental setup and protocol could aid the future development and application of neurally-controlled powered artificial legs.

Neural-machine interfaces (NMI) have been developed to identify the user's locomotion mode. These NMIs are potentially useful for neural control of powered artificial legs, but have not been fully demonstrated. This paper presented (1) our designed engineering platform for easy implementation and development of neural control for powered lower limb prostheses and (2) an experimental setup and protocol in a laboratory environment to evaluate neurally-controlled artificial legs on patients with lower limb amputations safely and efficiently.

To enable intuitive operation of powered artificial legs, an interface between user and prosthesis that can recognize the user's movement intent is ...

Bioengineering

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

The fields that develop methods for sensory substitution and sensory augmentation have aimed to control external goals using signals from the central nervous systems (CNS). Less frequent however, are protocols that update external signals self-generated by interactive bodies in motion. There is a paucity of methods that combine the body-heart-brain biorhythms of one moving agent to steer those of another moving agent during dyadic exchange. Part of the challenge to accomplish such a feat has been the complexity of the setup using multimodal bio-signals with different physical units, disparate time scales and variable sampling frequencies.
In recent years, the advent of wearable bio-sensors that can non-invasively harness multiple signals in tandem, has opened the possibility to re-parameterize and update the peripheral signals of interacting dyads, in addition to improving brain- and/or body-machine interfaces. Here we present a co-adaptive interface that updates efferent somatic-motor output (including kinematics and heart rate) using biosensors; parameterizes the stochastic bio-signals, sonifies this output, and feeds it back in re-parameterized form as visuo/audio-kinesthetic reafferent input. We illustrate the methods using two types of interactions, one involving two humans and another involving a human and its avatar interacting in near real time. We discuss the new methods in the context of possible new ways to measure the influences of external input on internal somatic-sensory-motor control.

We present protocols and methods of analyses to build co-adaptive interfaces that stream, parameterize, analyze, and modify human body and heart signals in close-loop. This setup interfaces signals derived from the peripheral and central nervous systems of the person with external sensory inputs to help track biophysical change.

The fields that develop methods for sensory substitution and sensory augmentation have aimed to control external goals using signals from the central ...

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Information coding in the Central Nervous System (CNS) remains unexplored. There is mounting evidence that, even at a very low level, the representation of a given stimulus might be dependent on context and history. If this is actually the case, bi-directional interactions between the brain (or if need be a reduced model of it) and sensory-motor system can shed a light on how encoding and decoding of information is performed. Here an experimental system is introduced and described in which the activity of a neuronal element (i.e., a network of neurons extracted from embryonic mammalian hippocampi) is given context and used to control the movement of an artificial agent, while environmental information is fed back to the culture as a sequence of electrical stimuli. This architecture allows a quick selection of diverse encoding, decoding, and learning algorithms to test different hypotheses on the computational properties of neuronal networks.

In this paper, an experimental framework to perform closed-loop experiments is presented, in which information processing (i.e., coding and decoding) and learning of neuronal assemblies are studied during the continuous interaction with a robotic body.

Information coding in the Central Nervous System (CNS) remains unexplored. There is mounting evidence that, even at a very low level, the representation ...

Isokinetic Robotic Device to Improve Test-Retest and Inter-Rater Reliability for Stretch Reflex Measurements in Stroke Patients with Spasticity

Measuring spasticity is important in treatment planning and determining efficacy after treatment. However, the current tool used in clinical settings has been shown to be limited in inter-rater reliability. One factor in this poor inter-rater reliability is the variability of passive motion while measuring the angle of catch (AoC) measurements. Therefore, an isokinetic device has been proposed to standardize the manual joint motion; however, the benefits of isokinetic motion for AoC measurements has not been tested in a standardized manner. This protocol investigates whether isokinetic motion itself can improve inter-rater reliability for AoC measurements. For this purpose, a robotic isokinetic device was developed that is combined with surface electromyography (EMG). Two conditions, manual and isokinetic motions, are compared with the standardized method to measure the angle and subjective feeling of catch. It is shown that in 17 stroke patients with mild elbow flexor spasticity, isokinetic motion improved the intraclass correlation coefficient (ICC) for inter-rater reliability of AoC measurements to 0.890 [95% confidence interval (CI): 0.685&#8211;0.961] by the EMG criteria, and 0.931 (95% CI: 0.791&#8211;0.978) by the torque criteria, from 0.788 (95% CI: 0.493&#8211;0.920) by manual motion. In conclusion, isokinetic motion itself can improve inter-rater reliability of AoC measurements in stroke patients with mild spasticity. Given that this system may provide greater standardized angle measurements and catch of feeling, it may be a good option for the evaluation of spasticity in a clinical setting.

Using a robotic isokinetic device with electromyography (EMG) measurements, this protocol illustrates that isokinetic motion itself can improve inter-rater reliability for the angle of catch measurements in stroke patients with mild elbow flexor spasticity.

Measuring spasticity is important in treatment planning and determining efficacy after treatment. However, the current tool used in clinical settings has ...

Experimental Methods to Study Human Postural Control

Many components of the nervous and musculoskeletal systems act in concert to achieve the stable, upright human posture. Controlled experiments accompanied by appropriate mathematical methods are needed to understand the role of the different sub-systems involved in human postural control. This article describes a protocol for performing perturbed standing experiments, acquiring experimental data, and carrying out the subsequent mathematical analysis, with the aim of understanding the role of musculoskeletal system and central control in human upright posture. The results generated by these methods are important, because they provide insight into the healthy balance control, form the basis for understanding the etiology of impaired balance in patients and the elderly, and aid in the design of interventions to improve postural control and stability. These methods can be used to study the role of somatosensory system, intrinsic stiffness of ankle joint, and visual system in postural control, and may also be extended to investigate the role of vestibular system. The methods are to be used in the case of an ankle strategy, where the body moves primarily about the ankle joint and is considered a single-link inverted pendulum.

This article presents an experimental/analytic framework to study human postural control. The protocol provides step-by-step procedures for performing standing experiments, measuring body kinematics and kinetics signals, and analyzing the results to provide insight into the mechanisms underlying human postural control.

Many components of the nervous and musculoskeletal systems act in concert to achieve the stable, upright human posture. Controlled experiments accompanied ...

A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning

Climbing fiber inputs to Purkinje cells provide instructive signals critical for cerebellum-dependent associative learning. Studying these signals in head-fixed mice facilitates the use of imaging, electrophysiological, and optogenetic methods. Here, a low-cost behavioral platform (~$1000) was developed that allows tracking of associative learning in head-fixed mice that locomote freely on a running wheel. The platform incorporates two common associative learning paradigms: eyeblink conditioning and delayed tactile startle conditioning. Behavior is tracked using a camera and the wheel movement by a detector. We describe the components and setup and provide a detailed protocol for training and data analysis. This platform allows the incorporation of optogenetic stimulation and fluorescence imaging. The design allows a single host computer to control multiple platforms for training multiple animals simultaneously.

We have developed a single platform to track animal behavior during two climbing fiber-dependent associative learning tasks. The low-cost design allows integration with optogenetic or imaging experiments directed towards climbing fiber-associated cerebellar activity.

Climbing fiber inputs to Purkinje cells provide instructive signals critical for cerebellum-dependent associative learning. Studying these signals in ...

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

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