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Review
, 3 (1), 34-7

Restoration of Whole Body Movement: Toward a Noninvasive Brain-Machine Interface System

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Review

Restoration of Whole Body Movement: Toward a Noninvasive Brain-Machine Interface System

José Contreras-Vidal et al. IEEE Pulse.

Abstract

This article highlights recent advances in the design of noninvasive neural interfaces based on the scalp electroencephalogram (EEG). The simplest of physical tasks, such as turning the page to read this article, requires an intense burst of brain activity. It happens in milliseconds and requires little conscious thought. But for amputees and stroke victims with diminished motor-sensory skills, this process can be difficult or impossible. Our team at the University of Maryland, in conjunction with the Johns Hopkins Applied Physics Laboratory (APL) and the University of Maryland School of Medicine, hopes to offer these people newfound mobility and dexterity. In separate research thrusts, were using data gleaned from scalp EEG to develop reliable brainmachine interface (BMI) systems that could soon control modern devices such as prosthetic limbs or powered robotic exoskeletons.

Figures

FIGURE 1
FIGURE 1
A noninvasive EEG-based neural interface is easier to repair or replace, if needed, and the technology is very user friendly requiring only a fabric cap and the slight inconvenience of some goo on a person’s head where the sensors are attached. (Photograph by John T. Consoli, University of Maryland.)
FIGURE 2
FIGURE 2
A representative scalp map of the spatial distribution (r2) of decoding accuracies across scalp electrodes for the joint angle of the right ankle joint in a healthy individual. Note the sparse network, in the EEG sensor space, that contains information about the ankle position.
FIGURE 3
FIGURE 3
Decoded ankle joint kinematics of a healthy able-bodied participant using a linear decoder. Blue is the joint angle recorded during treadmill walking, and red is the joint angle of the ankle that we predicted using our EEG-based neural interface.
FIGURE 4
FIGURE 4
Noninvasive BMI systems for the control of walking avatars that could soon control sophisticated prosthetic devices. Shown are bioengineering student Steve Graff (foreground, with cap), kinesiology doctoral student Alessandro Presacco (center), and lead researcher and electrical engineer José L. Contreras-Vidal, Ph.D (background). (Photograph by John T. Consoli, University of Maryland.)
FIGURE 5
FIGURE 5
Demonstration of the Rex-powered robotic exoskeleton (Rex Bionics) by a wheelchair user wearing a wireless 64-channel active EEG cap used in our neural interface development. Shown are the time series of the user’s brain waves as recorded by the EEG cap. From left: Faisal Almesfer, Steve Holbert, and Jedy Shishbaradaran. (Photograph by Joy Wilson, Department of Health and Human Performance, University of Houston.)

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