Research conducted within the context of the Joint targeted Program in Information and Communication Science and Technology - ICST, supported by CNRS, Inria, and DST, under CEFIPRA&#39;s umbrella. The authors would like to acknowledge the support of students, specifically Rahima Sidiboulenouar, Rishabh Sehgal, and Gorish Aggarwal, towards development of the experimental setup.

Note: The HMI software pipeline was developed based on freely available open-source software and off-the-shelf low-cost video game sensors (details available at: https://team.inria.fr/nphys4nrehab/software/ and https://github.com/NeuroPhys4NeuroRehab/JoVE). The HMI software pipeline is provided for data collection during a modified functional reach task (mFRT)21 in a VR based gaming platform for visuomotor balance therapy (VBT)8.
Figure 2a shows the diagnostic eye tracker setup where the gaze features are extracted offline for the quantification of post-stroke residual function so that the visual feedback in VR can be customized accordingly.
Figure 2b shows the experimental setup for VBT.
<img alt="Figure 2" src="/files/ftp_upload/52394/52394fig2.jpg" /> 
Figure 2: (a) Schematic of the human-machine-interface for the evaluation of post-stroke pursuit eye movements. (b) Schematic of the human-machine-interface where the software interface integrates biosignal sensors and motion capture to record mobile brain/body imaging data with neuromuscular electrical stimulation system (NMES) and sensory electrical stimulation (SES) for post-stroke NMES/SES-assisted visuomotor balance therapy. NMES: Neuromuscular Electrical Stimulation, SES: Sensory Electrical Stimulation, EMG: Electromyogram, EEG: Electroencephalogram, EOG: Electrooculogram, CoP: Center of Pressure, PC: Personal Computer. Reproduced from 8 and 37&#8203;. <a href="https://www.jove.com/files/ftp_upload/52394/52394fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
1. Software Installation for Mobile Brain/Body Imaging During VBT
<ol>
	<li>Install drivers for the Motion Capture (installation procedures provided at https://code.google.com/p/labstreaminglayer/wiki/KinectMocap)
	<ol>
		<li>Download and install Kinect Runtime from http://go.microsoft.com/fwlink/?LinkId=253187 (Motion Capture sensor should not be plugged into any of the USB ports on the computer).</li>
		<li>Plug in the powered Motion Capture Sensor into a USB port via the interface cable. The drivers will load automatically.</li>
	</ol>
	</li>
	<li>Install drivers for the Eye Tracker Sensor (installation procedures provided at <a href="http://github.com/esdalmaijer/EyeTribe-Toolbox-for-Matlab">http://github.com/esdalmaijer/EyeTribe-Toolbox-for-Matlab</a>)
	<ol>
		<li>Download the software from http://theeyetribe.com, launch the application and launch the application to install the software (Eye Tracker sensor should not be plugged into any of the USB ports on the computer).</li>
		<li>Plug in the powered Eye Tracker Sensor and the drivers will load automatically.</li>
	</ol>
	</li>
	<li>Install drivers for the Balance Board (installation procedures provided at (installation procedures provided at http://www.colorado.edu/intphys/neuromechanics/cu_wii.html)
	<ol>
		<li>Download and extract CU_WiiBB.zip from http://www.colorado.edu/intphys/neuromechanics/CU_WiiBB.zip</li>
		<li>Copy the WiiLab folder to Microsoft Window operating system&#39;s standard Program Files directory.</li>
		<li>Open the WiiLab folder in the Program Files directory and run as an administrator the InstallWiiLab.bat file to install the Balance Board.</li>
	</ol>
	</li>
	<li>Install drivers for EEG/EOG (installation procedures provided at http://openvibe.inria.fr/how-to-connect-emotiv-epoc-with-openvibe/ )
	<ol>
		<li>Download and install Emotiv SDK from http://www.emotiv.com/apps/sdk/209/</li>
		<li>Download and install OpenViBE Acquisition Server with labstreaminglayer (LSL) from https://code.google.com/p/labstreaminglayer/downloads/detail?name=OVAS-withLSL-0.14.3-3350-svn.zip for distributed multi-sensor signal transport, time synchronization and data collection system (installation procedures provided at https://code.google.com/p/labstreaminglayer/).</li>
	</ol>
	</li>
	<li>Install the drivers for the commercial NMES stimulator (details at http://www.vivaltis.com/gammes/phenix/phenix-usb-neo-50-554-1.html#content).</li>
</ol>
2. Low-cost Sensor Placement for Mobile Brain/Body Imaging (MoBI): The Open-source HMI Software Pipeline Provides Mobile Brain/Body Imaging (MoBI) 19 with Low-Cost Off-the-Shelf Sensors (Figure 2b) Which Can be Adapted for Other Agility Training Programs.
<ol>
	<li>Visual Feedback for MoBI:
	<ol>
		<li>Begin by obtaining a projection screen to display the visual biofeedback at the one end of the room (recommended distance from subject 0.6 m).</li>
		<li>Adjust the height so that the center of the screen will be at the subjects&#39; eye-level.</li>
	</ol>
	</li>
	<li>Motion Capture for MoBI:
	<ol>
		<li>Place the motion capture sensor in front of the projection screen, and aim it at the volume of motion capture.</li>
		<li>Confirm that the volume of motion capture is 1.5 m to 2.5 m in front of the motion capture sensor.</li>
	</ol>
	</li>
	<li>Balance Board Placement for MoBI:
	<ol>
		<li>Place the Balance Board on the floor, about 2.0 m away from the motion capture sensor.</li>
		<li>Leave enough room around the Balance Board to ensure full-body movement (i.e., during modified functional reach task21).</li>
	</ol>
	</li>
	<li>EEG/EMG/EOG Sensor Placement for MoBI
	<ol>
		<li>Ask the subject to sit on a chair facing the Motion Capture and with their feet on the Balance Board.</li>
		<li>Place the recording (EMG) cum stimulation (NMES/SES) electrodes bilaterally on the Medial Gastrocnemius (MG) and Tibialis Anterior (TA) muscles of the subject. Then, connect them to the wireless electrical stimulator (NMES/SES) system.</li>
		<li>Place the electroencephalogram (EEG) cap on the subjects head following the International 10 - 20 system. Then, place the EEG electrodes with conductive paste at -Fz, C3, Cz, C4, P3, Pz, P4, PO7, Oz, PO8 - before connecting them to the wireless EEG headset.</li>
		<li>Place two EEG electrodes with conductive paste above and below one of eyes for vertical EOG and put two electrodes with conductive paste at the outer canthus of each eye for horizontal EOG. (Note: In case Eye Tracker sensor is not used in the post-stroke subject then bilateral EOG is preferable).</li>
		<li>Place two EEG electrodes on earlobes as reference electrodes.</li>
	</ol>
	</li>
</ol>
3. Eye Tracker Based Evaluation of Post-stroke Pursuit Eye Movements
<ol>
	<li>Ask the subject to sit with the chin resting comfortably on the height-adjustable Chin-Rest. Then, raise the computer monitor to a convenient height such that the eyes are roughly facing the center of the computer monitor (Figure 2a).</li>
	<li>Place the Eye Tracker roughly 50 cm from the Chin-Rest and ask the subject to look straight at the computer monitor for visual cues.</li>
	<li>Run EyeTribeWinUI.exe in the &#39;SmartEye&#39; folder to calibrate the Eye Tracker sensor. The subject will be asked to look at various targets on the PC monitor for roughly 2 sec each. A typical user calibration process takes approximately 20 sec to complete. The (x, y) coordinates of the subject&#39;s gaze point are recorded for different cued targets for calibration.</li>
	<li>Run &#39;Visual_Stimulus.exe&#39; in the SmartEye folder to execute the virtual reality based interface. Subsequently run the &#39;SmartEye.exe&#39; program present in the &#39;SmartEye&#39; folder to acquire the subjects&#39; eye gaze data that is synchronized with the virtual reality based task. This data will be used for the evaluation of post-stroke pursuit eye movement.</li>
</ol>
<img alt="Figure 3" src="/files/ftp_upload/52394/52394fig3.jpg" /> 
Figure 3: (a) Cursor representing the center of pressure (CoP) which needs to be volitionally driven to the cued target during visuomotor balance therapy , (b) Visuomotor balance therapy protocol where the subject steers the computer cursor to a peripheral target driven by volitionally generated CoP excursions. The Reset can be assisted with Neuromuscular Electrical Stimulation (NMES) and sensory electrical stimulation (SES), (c) Experimental setup for visually-cued visuomotor balance therapy. Reproduced from 8 and 37&#8203;.&#160;<a href="https://www.jove.com/files/ftp_upload/52394/52394fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
4. NMES/SES-Assisted Visuomotor Balance Therapy (VBT) under MoBI
<ol>
	<li>Connect the eye-tracker and balance board sensors to the visual feedback computer (Figure 2).
	<ol>
		<li>Make sure that the Eye Tracker sensor is powered on, connected to computer, and that it has fully booted. Start the &#39;EyeTribe server.exe&#39; and &#39;EyeTribeWinUI.exe&#39; available in the &#39;VBT&#39; folder (see steps 1.3).</li>
		<li>Make sure that the Balance Board sensor is powered on. Then, press the button on the Balance Board sensor to make the remote discoverable in the menu. Then, click on the show or hide icon in the system&#39;s taskbar and click on Bluetooth device icon. Then, click on the &#39;Add a Device&#39; option and pair the Balance Board sensor as a Bluetooth device without using the code to the visual feedback computer. Once the Balance Board sensor is connected to the visual feedback computer, open the &#39;VBT&#39; folder and run the WiiBBinterface.m file to establish Matlab- Balance Board sensor interface (see steps 1.6).</li>
		<li>Make sure that the Motion Capture sensor is powered on, connected to the computer and that it has fully booted (there is a green LED on the front). Open the LSL folder and start &#39;Mocap&#39; software to begin streaming of the motion capture sensor data (see steps 1.6).</li>
		<li>Make sure that the EEG/EOG data acquisition systems are powered on. Then, double-click on the openvibe-acquisition-server-withlsl.cmd available in the LSL folder (see steps 1.6). From the menu, select the respective sensor hardware (i.e., &#39;Emotiv EPOC&#39;) and configure the module, if necessary, by clicking on the &#39;Driver Properties&#39;. Then, click on &#39;Connect&#39;, and then click on &#39;Play&#39; to start the acquisition server.</li>
	</ol>
	</li>
	<li>Calibrate the Sensors for VBT
	<ol>
		<li>Ask the post-stroke subject to stand on the Balance Board with safety harness (and partial body weight support, if necessary).</li>
		<li>Set a minimum baseline NMES level (pulse-width and current level) necessary for upright standing according to clinical observation (i.e., zero body weight support)22. For setting the minimum baseline NMES level, one can set the stimulation frequency at 20 Hz and then increase the pulse-width and/or current level until upright standing is achieved. Here, NMES of knee extensors is required to generate enough torque to prevent knee buckling.</li>
		<li>Ask the subject to perform various reach movements that affects CoM and CoP location.</li>
		<li>Run the &#39;CalibSensors.m&#39; program available in the &#39;DataCollect&#39; folder in order to collect multi-sensor calibration data while the subject performs various self-initiated maximal reach movements in different directions that affect center of mass (CoM) and center of pressure (CoP) location on the visual feedback.</li>
	</ol>
	</li>
</ol>
5. Multi-sensor Data Collection from Low-cost Sensors During VBT (Figure 2b)
<ol>
	<li>Run the &#39;CollectBaseline.m&#39; program in the &#39;DataCollect&#39; folder to collect baseline resting-state, eyes-open, multi-sensor data by asking the subject to stand still for 2 min while looking straight at the CoP target on the PC monitor (Figure 3a).</li>
	<li>Connect the visual feedback computer&#39;s video output to the projection screen and run the SmartEyeVRTasks.exe file in the &#39;VBT&#39; folder in the visual feedback computer to launch the SmartEyeVRTasks GUI. Also, run &#39;CollectVBT.m&#39; program in the &#39;DataCollect&#39; folder to collect sensor data during VBT.
	<ol>
		<li>From upright standing, called the &#39;Central hold&#39; phase, ask the subject to steer the cursor, driven by the CoP, as fast as possible towards randomly presented peripheral target as cued by visual feedback (Figure 3b).</li>
		<li>Following this &#39;Move&#39; phase, ask the subject to hold the cursor at the target location for 1 sec during the &#39;Peripheral hold&#39; phase.</li>
		<li>Following the &#39;Peripheral hold&#39; phase, the cursor will &#39;Reset&#39; back to the center when the subject needs to return back to upright standing - the &#39;Central hold&#39; position. NMES/SES is triggered for the muscle when its EMG level goes above a set threshold to assist the volitional effort required to return the CoP to the &#39;Central hold&#39; position. 
		Note: The difficulty of the mFRT can be increased by decreasing the gain, <img alt="Equation 1" src="/files/ftp_upload/52394/52394eq1.jpg" />, or increasing the noise variance,<img alt="Equation 2" src="/files/ftp_upload/52394/52394eq2.jpg" />, within subject-specific feasible range: 
		<img alt="Equation 3" src="/files/ftp_upload/52394/52394eq3.jpg" /> 
		where the CoP excursions, <img alt="Equation 4" src="/files/ftp_upload/52394/52394eq4.jpg" />, drive the computer cursor, <img alt="Equation 5" src="/files/ftp_upload/52394/52394eq5.jpg" />, in discretized time, <img alt="Equation 6" src="/files/ftp_upload/52394/52394eq6.jpg" />, with time-step, <img alt="Equation 7" src="/files/ftp_upload/52394/52394eq7.jpg" />.</li>
	</ol>
	</li>
</ol>

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<li>Carl, J. R., Gellman, R. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Human+smooth+pursuit%3A+stimulus-dependent+responses%5BTitle%5D)+AND+%22Journal+of+Neurophysiology%22%5BJournal%5D)">Human smooth pursuit: stimulus-dependent responses.</a> Journal of Neurophysiology. 57, 1446-1463 (1987).</li>
<li>Clark, R. A., Bryant, A. L., Pua, Y., McCrory, P., Bennell, K., Hunt, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Validity+and+reliability+of+the+Nintendo+Wii+Balance+Board+for+assessment+of+standing+balance%5BTitle%5D)+AND+%22Gait+%26+posture%22%5BJournal%5D)">Validity and reliability of the Nintendo Wii Balance Board for assessment of standing balance.</a> Gait & posture. 31, 307-310 (2010).</li>
<li>Clark, R. A., Pua, Y. -H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Validity+of+the+Microsoft+Kinect+for+assessment+of+postural+control%5BTitle%5D)+AND+%22Gait+%26+posture%22%5BJournal%5D)">Validity of the Microsoft Kinect for assessment of postural control.</a> Gait & posture. 36, 372-377 (2012).</li>
<li>Khattar, B., Banerjee, A., Reddi, R., Dutta, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Feasibility+of+Functional+Electrical+Stimulation-Assisted+Neurorehabilitation+following+Stroke+in+India%3A+A+Case+Series%5BTitle%5D)+AND+%22Case+Reports+in+Neurological+Medicine%22%5BJournal%5D)">Feasibility of Functional Electrical Stimulation-Assisted Neurorehabilitation following Stroke in India: A Case Series.</a> Case Reports in Neurological Medicine. 2012, e830873(2012).</li>
<li>Sailer, U., Flanagan, J. R., Johansson, R. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Eye-hand+coordination+during+learning+of+a+novel+visuomotor+task%5BTitle%5D)+AND+%22The+Journal+of+neuroscience%3A+the+official+journal+of+the+Society+for+Neuroscience%22%5BJournal%5D)">Eye-hand coordination during learning of a novel visuomotor task.</a> The Journal of neuroscience: the official journal of the Society for Neuroscience. 25, 8833-8842 (2005).</li>
<li>Herr, H., Popovic, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Angular+momentum+in+human+walking%5BTitle%5D)+AND+%22The+Journal+of+Experimental+Biology%22%5BJournal%5D)">Angular momentum in human walking.</a> The Journal of Experimental Biology. 211, (Pt 4) 467-481 (2008).</li>
<li>Taub, E., Morris, D. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Constraint-induced+movement+therapy+to+enhance+recovery+after+stroke%5BTitle%5D)+AND+%22Current+atherosclerosis+reports%22%5BJournal%5D)">Constraint-induced movement therapy to enhance recovery after stroke.</a> Current atherosclerosis reports. 3, 279-286 (2001).</li>
<li>Kasten, E., Wuest, S., Sabel, B. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Residual+vision+in+transition+zones+in+patients+with+cerebral+blindness%5BTitle%5D)+AND+%22Journal+of+Clinical+and+Experimental+Neuropsychology%22%5BJournal%5D)">Residual vision in transition zones in patients with cerebral blindness.</a> Journal of Clinical and Experimental Neuropsychology. 20, 581-598 (1998).</li>
<li>Marshall, S. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Identifying+Cognitive+State+from+Eye+Metrics%5BTitle%5D)+AND+%22Aviation%2C+Space%2C+and+Environmental+Medicine%22%5BJournal%5D)">Identifying Cognitive State from Eye Metrics.</a> Aviation, Space, and Environmental Medicine. 78, 165-175 (2007).</li>
<li>Weerdesteyn, V., de Niet, M., van Duijnhoven, H. J. R., Geurts, A. C. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Falls+in+individuals+with+stroke%5BTitle%5D)+AND+%22Journal+of+Rehabilitation+Research+and+Development%22%5BJournal%5D)">Falls in individuals with stroke.</a> Journal of Rehabilitation Research and Development. 45, 1195-1213 (2008).</li>
<li>Stinear, C. M., Barber, P. A., Petoe, M., Anwar, S., Byblow, W. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+PREP+algorithm+predicts+potential+for+upper+limb+recovery+after+stroke%5BTitle%5D)+AND+%22Brain%3A+A+Journal+of+Neurology%22%5BJournal%5D)">The PREP algorithm predicts potential for upper limb recovery after stroke.</a> Brain: A Journal of Neurology. 135 ((Pt 8)), 2527-2535 (2012).</li>
<li>Dutta, A., Lahiri, D., Kumar, U., Das, A., Padma, M. V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Post-stroke+engagement-sensitive+balance+rehabilitation+under+an+adaptive+multi-level+electrotherapy%3A+clinical+hypothesis+and+computational+framework%5BTitle%5D)+AND+%22Neuroscience+and+Biomedical+Engineering%22%5BJournal%5D)">Post-stroke engagement-sensitive balance rehabilitation under an adaptive multi-level electrotherapy: clinical hypothesis and computational framework.</a> Neuroscience and Biomedical Engineering. 2 (2), 68-80 (2015).</li>
</ol>

The authors have nothing to disclose.

A simple-to-use, clinically valid low-cost tool for movement and balance therapy will be a paradigm shift for neurorehabilitation in a low-resource setting. It is likely to have a very high societal impact since neurological disorders like stroke will dramatically increase in future due to aging world population2. There is, therefore, a pressing need to leverage cyber physical systems where the ability to customize, monitor, and support neuro-rehabilitation at remote sites has recently become possible with the...

An episode of neurological dysfunction caused by focal cerebral, spinal, or retinal infarction is called stroke1. Stroke is a global health problem and fourth leading cause of disability worldwide1. In countries like India and China, the two most populous nations of the world, neurologic disability due to stroke is being labeled as hidden epidemic2. One of the most common medical complications after a stroke are falls with a reported incidence of up to 73% in the first year post-stroke3. The post-stroke fall is multifactorial and includes both spinal and supraspinal factors like balance and visuospatial neglect4. A review by Geurts and colleagues5 identified 1) multi-directionally impaired maximal weight shifting during bipedal standing, 2) slow speed, 3) directional imprecision, and 4) small amplitudes of single and cyclic sub-maximal frontal plane weight shifts as the balance factors for fall risk. The consequent impact on activities of daily living can be significant since prior works have shown that balance is associated with ambulatory ability and independence in gross motor function5,6. Moreover, Geurts and colleagues5 suggested that supraspinal multisensory integration (and muscle coordination7) in addition to muscle strength is critical for balance recovery which is lacking in current protocols. Towards multisensory integration, our hypothesis8 on volitionally driven non-invasive electrotherapy (NMES/SES) is that this adaptive behavior can be shaped and facilitated by modulating active perception of sensory inputs during NMES/SES-assisted movement of the affected limb such that the brain can incorporate this feedback into subsequent movement output by recruiting alternate motor pathways9, if needed.
To achieve volitionally driven NMES/SES-assisted balance training in a resource-poor setting, a low-cost human-machine-interface (HMI) was developed by leveraging available open-source software and recent advances in off-the-shelf video game sensor technology for visual-auditory biofeedback. NMES involves coordinated electrical stimulation of nerves and muscles that has been shown to improve muscle strength and reduce spasticity10. Also, SES involves stimulation of sensory nerves with electrical current to evoke sensations where preliminary published work11 showed that subsensory stimulation applied over the tibialis anterior muscles alone is effective in attenuating postural sway. Here, the HMI will make possible sensory-motor integration during interactive post-stroke balance therapy where volitionally-driven NMES/SES for the ankle muscles will act as a muscle amplifier (with NMES) as well as enhance afferent feedback (with SES) to assist healthy ankle strategies12,13,14 to maintain upright stance during postural sways. This is based on the hypothesis presented in Dutta et al.8 that an increased corticospinal excitability of relevant ankle muscles effected through non-invasive electrotherapy may lend to an improved supraspinal modulation of ankle stiffness. Indeed, prior work has shown that NMES/SES elicits lasting changes in corticospinal excitability, possibly as a result of co-activating motor and sensory fibers15,16. Moreover, Khaslavskaia and Sinkjaer17 showed in humans that concurrent motor cortical drive present at the time of NMES/SES enhanced motor cortical excitability. Therefore, volitionally-driven NMES/SES may induce short-term neuroplasticity in spinal reflexes (e.g., reciprocal Ia inhibition17) where corticospinal neurons that project via descending pathways to a given motoneuron pool can inhibit the antagonistic motoneuron pool via Ia-inhibitory interneurons in humans18, as shown in Figure 1, towards an operant conditioning paradigm (see Dutta et al.8).
<img alt="Figure 1" src="/files/ftp_upload/52394/52394fig1.jpg" /> 
Figure 1: The concept (details at Dutta et al.21) underlying interactive human machine interface (HMI) to drive the center of pressure (CoP) cursor to the cued target to improve ankle muscle coordination under volitionally driven neuromuscular electrical stimulation (NMES)-assisted visuomotor balance therapy. EEG: electroencephalography, MN: &#945;-motoneuron, IN: Ia-inhibitory interneuron, EMG: electromyogram, DRG: dorsal root ganglion. Reproduced from8 and37&#8203;.&#160;<a href="https://www.jove.com/files/ftp_upload/52394/52394fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The antero-posterior (A-P) displacements in center of mass (CoM) are performed by ankle plantarflexors (such as medial gastrocnemius and soleus muscles) and dorsiflexors (such as the anterior tibial muscle) while medio-lateral (M-L) displacements are performed by ankle invertors (such as the anterior tibial muscle) and evertors (such as peroneus longus and brevis muscles). Consequently, stroke-related ankle impairments including weakness of the ankle dorsiflexor muscles and increased spasticity of the ankle plantarflexor muscles lead to impaired postural control. Here, agility training programs6 can be leveraged in a virtual reality (VR) based gaming platform that challenge dynamic balance where tasks are progressively increased in difficulty which may be more effective than static stretching/weight-shifting exercise program in preventing falls6. For example, subjects can perform volitionally driven NMES/SES assisted A-P and M-L displacements during a dynamic visuomotor balance task where the difficulty can be progressively increased to ameliorate post-stroke ankle-specific control problems in weight shifting during bipedal standing. Towards volitionally driven NMES/SES assisted balance therapy in a resource-poor setting, we present a low-cost HMI for Mobile Brain/Body Imaging (MoBI)19, towards visual-auditory biofeedback which can also be used for data collection from low-cost sensors for offline data exploration in MoBILAB (see Ojeda et al.20).

<table><tbody><tr><td>NMES stimulator</td><td>Vivaltis, France</td><td>PhenixUSBNeo</td><td>NMES stimulator cum EMG sensor (Figure 2b)</td></tr><tr><td>Balance Board</td><td>Nintendo, USA</td><td>Wii Balance Board</td><td>Balance Board (Figure 2b)</td></tr><tr><td>Motion Capture</td><td>Microsoft, USA</td><td>XBOX-360 Kinect</td><td>Motion Capture (Figure 2b)</td></tr><tr><td>Eye Tracker&amp;nbsp;</td><td>Eye Tribe</td><td>The Eye Tribe</td><td>SmartEye Tracker (Figure 2a)</td></tr><tr><td>EEG Data Acquisition System</td><td>Emotiv, Australia</td><td>Emotiv Neuroheadset</td><td>Wireless EEG headset (Figure 2b)</td></tr><tr><td>EEG passive electrode</td><td>Olimex</td><td>EEG-PE</td><td>EEG passive electrode for EOG and references (6 in number) (Figure 2b)</td></tr><tr><td>EEG active electrode</td><td>Olimex</td><td>EEG-AE</td><td>EEG active electrode (10 in number) (Figure 2b)</td></tr><tr><td>Computer with PC monitor</td><td>Dell</td><td></td><td>Data processing and visual feedback (Figure 2)</td></tr><tr><td>Softwares, EMG electrodes, NMES electrodes, and cables</td><td></td><td></td><td></td></tr></tbody></table>

a human-machine-interface integrating low-cost sensors with a neuromuscular electrical stimulation system for post-stroke balance rehabilitation

A stroke is caused when an artery carrying blood from heart to an area in the brain bursts or a clot obstructs the blood flow to brain thereby preventing delivery of oxygen and nutrients. About half of the stroke survivors are left with some degree of disability. Innovative methodologies for restorative neurorehabilitation are urgently required to reduce long-term disability. The ability of the nervous system to reorganize its structure, function and connections as a response to intrinsic or extrinsic stimuli is called neuroplasticity. Neuroplasticity is involved in post-stroke functional disturbances, but also in rehabilitation. Beneficial neuroplastic changes may be facilitated with non-invasive electrotherapy, such as neuromuscular electrical stimulation (NMES) and sensory electrical stimulation (SES). NMES involves coordinated electrical stimulation of motor nerves and muscles to activate them with continuous short pulses of electrical current while SES involves stimulation of sensory nerves with electrical current resulting in sensations that vary from barely perceivable to highly unpleasant. Here, active cortical participation in rehabilitation procedures may be facilitated by driving the non-invasive electrotherapy with biosignals (electromyogram (EMG), electroencephalogram (EEG), electrooculogram (EOG)) that represent simultaneous active perception and volitional effort. To achieve this in a resource-poor setting, e.g., in low- and middle-income countries, we present a low-cost human-machine-interface (HMI) by leveraging recent advances in off-the-shelf video game sensor technology. In this paper, we discuss the open-source software interface that integrates low-cost off-the-shelf sensors for visual-auditory biofeedback with non-invasive electrotherapy to assist postural control during balance rehabilitation. We demonstrate the proof-of-concept on healthy volunteers.

Figure 4 shows the eye gaze features that were extracted offline for the quantification of an able-bodied performance during a smooth pursuit task. The following features were extracted as shown in Table 1:
Feature 1 = percentage deviation between target stimulus position and the centroid of participant&#39;s fixation points when the stimulus is changing position in the horizontal direction....

Watch this Scientific Journal Video about A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation at JoVE.com

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

A novel low-cost human-machine interface for interactive post-stroke balance rehabilitation system is presented in this article. The system integrates off-the-shelf low-cost sensors towards volitionally driven electrotherapy paradigm. The proof-of-concept software interface is demonstrated on healthy volunteers.

A stroke is caused when an artery carrying blood from heart to an area in the brain bursts or a clot obstructs the blood flow to brain thereby preventing ...

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Research

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Neuroscience

10.4K Views.  Indian Institute of Technology Gandhinagar. A novel low-cost human-machine interface for interactive post-stroke balance rehabilitation system is presented in this article. The system integrates off-the-shelf low-cost sensors towards volitionally driven electrotherapy paradigm. The proof-of-concept software interface is demonstrated on healthy volunteers.

Video: A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Recent studies support the involvement of supraspinal networks in control of bipedal human walking. Part of this evidence encompasses studies, including our previous work, demonstrating that gait kinematics and limb coordination during treadmill walking can be inferred from the scalp electroencephalogram (EEG) with reasonably high decoding accuracies. These results provide impetus for development of non-invasive brain-machine-interface (BMI) systems for use in restoration and/or augmentation of gait- a primary goal of rehabilitation research. To date, studies examining EEG decoding of activity during gait have been limited to treadmill walking in a controlled environment. However, to be practically viable a BMI system must be applicable for use in everyday locomotor tasks such as over ground walking and turning. Here, we present a novel protocol for non-invasive collection of brain activity (EEG), muscle activity (electromyography (EMG)), and whole-body kinematic data (head, torso, and limb trajectories) during both treadmill and over ground walking tasks. By collecting these data in the uncontrolled environment insight can be gained regarding the feasibility of decoding unconstrained gait and surface EMG from scalp EEG.

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Development of an effective brain-machine-interface (BMI) system for restoration and rehabilitation of bipedal locomotion requires accurate decoding of user's intent. Here we present a novel experimental protocol and data collection technique for simultaneous non-invasive acquisition of neural activity, muscle activity, and whole-body kinematics during various locomotion tasks and conditions.

Recent  studies support the involvement of supraspinal networks in control of bipedal  human walking. Part of this evidence  encompasses studies, ...

Behavior

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

Paradigms of Lower Extremity Electrical Stimulation Training After Spinal Cord Injury

Skeletal muscle atrophy, increased adiposity and reduced physical activity are key changes observed after spinal cord injury (SCI) and are associated with numerous cardiometabolic health consequences. These changes are likely to increase the risk of developing chronic secondary conditions and impact the quality of life in persons with SCI. Surface neuromuscular electrical stimulation evoked resistance training (NMES-RT) was developed as a strategy to attenuate the process of skeletal muscle atrophy, decrease ectopic adiposity, improve insulin sensitivity and enhance mitochondrial capacity. However, NMES-RT is limited to only a single muscle group. Involving multiple muscle groups of the lower extremities may maximize the health benefits of training. Functional electrical stimulation-lower extremity cycling (FES-LEC) allows for the activation of 6 muscle groups, which is likely to evoke greater metabolic and cardiovascular adaptation. Appropriate knowledge of the stimulation parameters is key to maximizing the outcomes of electrical stimulation training in persons with SCI. Adopting strategies for long-term use of NMES-RT and FES-LEC during rehabilitation may maintain the integrity of the musculoskeletal system, a pre-requisite for clinical trials aiming to restore walking after injury. The current manuscript presents a combined protocol using NMES-RT prior to FES-LEC. We hypothesize that muscles conditioned for 12 weeks prior to cycling will be capable of generating greater power, cycle against higher resistance and result in greater adaptation in persons with SCI.

Spinal cord injury is a traumatic medical condition that may result in elevated risks of chronic secondary metabolic disorders. Here, we presented a protocol using surface neuromuscular electrical stimulation-resistance training in conjunction with functional electrical stimulation-lower extremities cycling as a strategy to ameliorate several of these medical problems.

Skeletal muscle atrophy, increased adiposity and reduced physical activity are key changes observed after spinal cord injury (SCI) and are associated with ...

A Modified Lean and Release Technique to Emphasize Response Inhibition and Action Selection in Reactive Balance

Assessment of reactive balance traditionally imposes some type of perturbation to upright stance or gait followed by measurement of the resultant corrective behavior. These measures include muscle responses, limb movements, ground reaction forces, and even direct neurophysiological measures such as electroencephalography. Using this approach, researchers and clinicians can infer some basic principles regarding how the nervous system controls balance to avoid a fall. One limitation with the way in which these assessments are currently used is that they heavily emphasize reflexive actions without any need to revise automatic postural reactions. Such an exclusive focus on these highly stereotypical reactions would fail to adequately address how we can modify these reactions should the need arise (e.g., avoiding an obstacle with a recovery step). This would appear to be a glaring omission when one considers the enormous complexity of the environments we face daily. Overall, the status quo when evaluating the neural control of balance fails to truly expose how higher brain resources contribute to preventing falls in complex settings. The present protocol offers a way to require suppression of automatic, but inappropriate corrective balance reactions, and force a selection among alternative action choices to successfully recover balance following postural perturbation.

Here we offer a protocol that allows the user to selectively change affordances and/or constraints on movements that are relevant for recovering balance after postural perturbation.

Assessment of reactive balance traditionally imposes some type of perturbation to upright stance or gait followed by measurement of the resultant ...

Standing Neurophysiological Assessment of Lower Extremity Muscles Post-Stroke

Transcranial magnetic stimulation (TMS) is a common tool used to measure the behavior of motor circuits in healthy and neurologically impaired populations. TMS is used extensively to study motor control and the response to neurorehabilitation of the upper extremities. However, TMS has been less utilized in the study of lower extremity postural and walking-specific motor control. The limited use and the additional methodological challenges of lower extremity TMS assessments have contributed to the lack of consistency in lower extremity TMS procedures within the literature. Inspired by the decreased ability to record lower extremity TMS motor evoked potentials (MEP), this methodological report details steps to enable post-stroke TMS assessments in a standing posture. The standing posture allows for the activation of the neuromuscular system, reflecting a state more akin to the system's state during postural and walking tasks. Using dual-top force plates, we instructed participants to equally distribute their weight between their paretic and non-paretic legs. Visual feedback of the participants' weight distribution was provided. Using image guidance software, we delivered single TMS pulses via a double-cone coil to the participants' lesioned and non-lesioned hemispheres and measured the corticomotor response of the paretic and non-paretic tibialis anterior and soleus muscles. Performing assessments in the standing position increased the TMS response rate and allowed for the use of the lower stimulation intensities compared to the standard sitting/resting position. Utilization of this TMS protocol can provide a common approach to assess the lower extremity corticomotor response post-stroke when the neurorehabilitation of postural and gait impairments are of interest.

This protocol describes the process for performing a neurophysiological assessment of the lower extremity muscles, tibialis anterior and soleus, in a standing position using TMS in people post-stroke. This position provides a greater probability of eliciting a post-stroke TMS response and allows for the use of reduced stimulator power during neurophysiological assessments.

Transcranial magnetic stimulation (TMS) is a common tool used to measure the behavior of motor circuits in healthy and neurologically impaired ...

Home-Based Monitor for Gait and Activity Analysis

Current outcomes in neuromuscular disorder clinical trials include motor function scales, timed tests, and strength measures performed by trained clinical evaluators. These measures are slightly subjective and are performed during a visit to a clinic or hospital and constitute therefore a point assessment. Point assessments can be influenced by daily patient condition or factors such as fatigue, motivation, and intercurrent illness. To enable home-based monitoring of gait and activity, a wearable magneto-inertial sensor (WMIS) has been developed. This device is a movement monitor composed of two very light watch-like sensors and a docking station. Each sensor contains a tri-axial accelerometer, gyroscope, magnetometer, and a barometer that record linear acceleration, angular velocity, the magnetic field of the movement in all directions, and barometric altitude, respectively. The sensors can be worn on the wrist, ankle, or wheelchair to record the subject&#8217;s movements during the day. The docking station enables data uploading and recharging of sensor batteries during the night. Data are analyzed using proprietary algorithms to compute parameters representative of the type and intensity of the performed movement. This WMIS can record a set of digital biomarkers, including cumulative variables, such as total number of meters walked, and descriptive gait variables, such as the percentage of the most rapid or longest stride that represents the top performance of patient over a predefined period of time.

This innovative device uses magneto-inertial sensors to permit gait and activity analysis in uncontrolled environments. Currently in the qualification process as an outcome measure in the European Medical Agency, one of the applications will be to serve as a clinical endpoint in clinical trials in neuromuscular diseases.

Current outcomes in neuromuscular disorder clinical trials include motor function scales, timed tests, and strength measures performed by trained clinical ...

The Muscle Cuff Regenerative Peripheral Nerve Interface for the Amplification of Intact Peripheral Nerve Signals

Robotic exoskeletons have gained recent acclaim within the field of rehabilitative medicine as a promising modality for functional restoration for those individuals with extremity weakness. However, their use remains largely confined to research institutions, frequently operating as a means of static extremity support as motor detection methods remain unreliable. Peripheral nerve interfaces have arisen as a potential solution to this shortcoming; however, due to their inherently small amplitudes, these signals can be difficult to differentiate from background noise, lowering their overall motor detection accuracy. As current interfaces rely on abiotic materials, inherent material breakdown can occur alongside foreign body tissue reaction over time, further impacting their accuracy. The Muscle Cuff Regenerative Peripheral Nerve Interface (MC-RPNI) was designed to overcome these noted complications. Consisting of a segment of free muscle graft secured circumferentially to an intact peripheral nerve, the construct regenerates and becomes reinnervated by the contained nerve over time. In rats, this construct has demonstrated the ability to amplify a peripheral nerve's motor efferent action potentials up to 100 times the normal value through the generation of compound muscle action potentials (CMAPs). This signal amplification facilitates high accuracy detection of motor intent, potentially enabling reliable utilization of exoskeleton devices.

This manuscript provides an innovative method for developing a biologic peripheral nerve interface termed the Muscle Cuff Regenerative Peripheral Nerve Interface (MC-RPNI). This surgical construct can amplify its associated peripheral nerve's motor efferent signals to facilitate accurate detection of motor intent and the potential control of exoskeleton devices.

Robotic exoskeletons have gained recent acclaim within the field of rehabilitative medicine as a promising modality for functional restoration for those ...

Evaluating Postural Control and Lower-extremity Muscle Activation in Individuals with Chronic Ankle Instability

Computerized dynamic posturography (CDP) is an objective technique for the evaluation of postural stability under static and dynamic conditions and perturbation. CDP is based on the inverted pendulum model that traces the interrelationship between the center of pressure and the center of gravity. CDP can be used to analyze the proportions of vision, proprioception, and vestibular sensation to maintain postural stability. The following characters define chronic ankle instability (CAI): persistent ankle pain, swelling, the feeling of &#8220;giving way,&#8221; and self-reported disability. Postural stability and fibular muscle activation level in individuals with CAI decreased due to lateral ankle ligament complex injuries. Few studies have used CDP to explore the postural stability of individuals with CAI. Studies that investigate postural stability and related muscle activation by using synchronized CDP with surface electromyography are lacking. This CDP protocol includes a sensory organization test (SOT), a motor control test (MCT), and an adaption test (ADT), as well as tests that measure unilateral stance (US) and limit of stability (LOS). The surface electromyography system is synchronized with CDP to collect data on lower limb muscle activation during measurement. This protocol presents a novel approach for evaluating the coordination of the visual, somatosensory, and vestibular systems and related muscle activation to maintain postural stability. Moreover, it provides new insights into the neuromuscular control of individuals with CAI when coping with real complex environments.

Individuals with chronic ankle instability (CAI) exhibit postural control deficiency and delayed muscle activation of lower extremities. Computerized dynamic posturography combined with surface electromyography provides insights into the coordination of the visual, somatosensory, and vestibular systems with muscle activation regulation to maintain postural stability in individuals with CAI.

Computerized dynamic posturography (CDP) is an objective technique for the evaluation of postural stability under static and dynamic conditions and ...

Evaluating Brain Activation by fNIRS During a Lower Limb Robotic Therapy Session

An Experiment Using Functional Near-Infrared Spectroscopy and Robot-Assisted Multi-Joint Pointing Movements of the Lower Limb

Stroke affects approximately 17 million individuals worldwide each year and is a leading cause of long-term disability. Robotic therapy has shown promise in helping stroke patients regain lost motor functions. One potential avenue for increasing the understanding of how motor recovery occurs is to study brain activation during the movements that are targeted by therapy in healthy individuals. Functional Near-Infrared Spectroscopy (fNIRS) has emerged as a promising neuroimaging technique for examining neural underpinnings of motor function. This study aimed to investigate fNIRS neural correlates of complex lower limb movements in healthy subjects. Participants were asked to perform cycles of rest and movement for 6 min using a robotic device for motor rehabilitation. The task required coordinated knee and ankle joint movements to point to targets displayed on a computer screen. Two experimental conditions with different levels of movement assistance provided by the robot were explored. The results showed that the fNIRS protocol effectively detected brain regions associated with motor control during the task. Notably, all subjects exhibited greater activation in the contralateral premotor area during the no-assistance condition compared to the assisted condition. In conclusion, fNIRS appears to be a valuable approach for detecting changes in oxyhemoglobin concentration associated with multi-joint pointing movements of the lower limb. This research might contribute to the understanding of stroke motor recovery mechanisms and might pave the way for improved rehabilitation treatments for stroke patients. However, further research is needed to fully elucidate the potential of fNIRS in studying motor function and its applications in clinical settings.

Author Spotlight: Assessing Brain Activity in Robotic-Assisted Lower Limb Rehabilitation Using fNIRS

It is estimated that 1 in 6 individuals worldwide will have a stroke in their lifetime, causing long-term disability, whose rehabilitation mechanisms are still poorly understood. This study proposes a protocol to evaluate brain activation by functional near-infrared spectroscopy (fNIRS) during a lower limb robotic therapy session.

Stroke affects approximately 17 million individuals worldwide each year and is a leading cause of long-term disability. Robotic therapy has shown promise ...

Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients

This study introduces a Brain-Computer Interface (BCI)-controlled upper limb assistive robot for post-stroke rehabilitation. The system utilizes electroencephalogram (EEG) and electrooculogram (EOG) signals to help users assist upper limb function in everyday tasks while interacting with a robotic hand. We evaluated the effectiveness of this BCI-robot system using the Berlin Bimanual Test for Stroke (BeBiTS), a set of 10 daily living tasks involving both hands. Eight stroke patients participated in this study, but only four participants could adapt to the BCI robot system training and perform the postBeBiTS. Notably, when the preBeBiTS score for each item was four or less, the BCI robot system showed greater assistive effectiveness in the postBeBiTS assessment. Furthermore, our current robotic hand does not assist with arm and wrist functions, limiting its use in tasks requiring complex hand movements. More participants are required to confirm the training effectiveness of the BCI-robot system, and future research should consider using robots that can assist with a broader range of upper limb functions. This study aimed to determine the BCI-robot system's ability to assist patients in performing daily living activities.

This study introduces a brain-computer interface (BCI) system for stroke patients, which combines electroencephalography and electrooculography signals to control an upper limb robotic hand, enhancing daily activities. The evaluation used the Berlin Bimanual Test for Stroke (BeBiTS).

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

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