Paek, A.; Brown, J. D.; Gillespie, B. R.; O’Malley, M. K.; Shewokis, P. A.; Contreras-Vidal, J. L.
Reconstructing surface EMG from scalp EEG during myoelectric control of a closed looped prosthetic device.
IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5602–5, 2013, ISSN: 1557-170X. / View Abstract, BibTeX and Links


Series elastic actuators are used to significant advantage in many robot designs but have not found their way into the design of haptic devices. We use a pneumatic circuit to realize both a flexible power transmission as well as the elastic element in a series elastic actuator. The pneumatic circuit effectively hides the impedance of a high friction, high mass ball-screw actuator, while a low friction, low mass pneumatic cylinder is used at the end-effector. We offer a comparative study of an impedance-control device, admittance-control device, and a device incorporating a series elastic actuator, investigating both the open-loop and the closed-loop impedance displayed to the user. While all hardware and control designs offer an ability to shape the impedance within their operational bandwidths, the series elastic design has the particular advantage of low impedance (a very compliant spring) outside of that bandwidth. Thus, a haptic device featuring series elastic actuation is capable of providing both the low impedances required in free-space and the high impedance required for rendering stiff virtual walls.

	title = {Reconstructing surface EMG from scalp EEG during myoelectric control of a closed looped prosthetic device.},
	author = { Andrew Y Paek and Jeremy D Brown and R Brent Gillespie and Marcia K O'Malley and Patricia A Shewokis and Jose L Contreras-Vidal},
	url = {},
	doi = {10.1109/EMBC.2013.6610820},
	issn = {1557-170X},
	year = {2013},
	date = {2013-01-01},
	booktitle = {Proc. IEEE Engineering in Medicine and Biology Society (EMBC)},
	pages = {5602--5},
	abstract = {In this study, seven able-bodied human subjects controlled a robotic gripper with surface electromyography (sEMG) activity from the biceps. While subjects controlled the gripper, they felt the forces measured by the robotic gripper through an exoskeleton fitted on their non-dominant left arm. Subjects were instructed to identify objects with the force feedback provided by the exoskeleton. While subjects operated the robotic gripper, scalp electroencephalography (EEG) and functional near infrared spectroscopy (fNIRS) were recorded. We developed neural decoders that used scalp EEG to reconstruct the sEMG used to control the robotic gripper. The neural decoders used a genetic algorithm embedded in a linear model with memory to reconstruct the sEMG from a plurality of EEG channels. The performance of the decoders, measured with Pearson correlation coefficients (median r-value = 0.59, maximum r-value = 0.91) was found to be comparable to previous studies that reconstructed sEMG linear envelopes from neural activity recorded with invasive techniques. These results show the feasibility of developing EEG-based neural interfaces that in turn could be used to control a robotic device.},
	keywords = {},
	pubstate = {published},
	tppubtype = {inproceedings}