Incorporating Feedback from Multiple Sensory Modalities Enhances Brain–Machine Interface Control

The brain typically uses a rich supply of feedback from multiple sensory modalities to control movement in healthy individuals. In many individuals, these afferent pathways, as well as their efferent counterparts, are compromised by disease or injury resulting in significant impairments and reduced quality of life. Brain–machine interfaces (BMIs) offer the promise of recovered functionality to these individuals by allowing them to control a device using their thoughts. Most current BMI implementations use visual feedback for closed-loop control; however, it has been suggested that the inclusion of additional feedback modalities may lead to improvements in control. We demonstrate for the first time that kinesthetic feedback can be used together with vision to significantly improve control of a cursor driven by neural activity of the primary motor cortex (MI). Using an exoskeletal robot, the monkey\u27s arm was moved to passively follow a cortically controlled visual cursor, thereby providing the monkey with kinesthetic information about the motion of the cursor. When visual and proprioceptive feedback were congruent, both the time to successfully reach a target decreased and the cursor paths became straighter, compared with incongruent feedback conditions. This enhanced performance was accompanied by a significant increase in the amount of movement-related information contained in the spiking activity of neurons in MI. These findings suggest that BMI control can be significantly improved in paralyzed patients with residual kinesthetic sense and provide the groundwork for augmenting cortically controlled BMIs with multiple forms of natural or surrogate sensory feedback

Improving Brain–Machine Interface Performance by Decoding Intended Future Movements

Objective. A brain–machine interface (BMI) records neural signals in real time from a subject\u27s brain, interprets them as motor commands, and reroutes them to a device such as a robotic arm, so as to restore lost motor function. Our objective here is to improve BMI performance by minimizing the deleterious effects of delay in the BMI control loop. We mitigate the effects of delay by decoding the subject\u27s intended movements a short time lead in the future. Approach. We use the decoded, intended future movements of the subject as the control signal that drives the movement of our BMI. This should allow the user\u27s intended trajectory to be implemented more quickly by the BMI, reducing the amount of delay in the system. In our experiment, a monkey (Macaca mulatta) uses a future prediction BMI to control a simulated arm to hit targets on a screen. Main Results. Results from experiments with BMIs possessing different system delays (100, 200 and 300 ms) show that the monkey can make significantly straighter, faster and smoother movements when the decoder predicts the user\u27s future intent. We also characterize how BMI performance changes as a function of delay, and explore offline how the accuracy of future prediction decoders varies at different time leads. Significance. This study is the first to characterize the effects of control delays in a BMI and to show that decoding the user\u27s future intent can compensate for the negative effect of control delay on BMI performance

Improving Grasp Skills Using Schema Structured Learning

Abstract In the control-based approach to robotics, complex behavior is created by sequencing and combining control primitives. While it is desirable for the robot to autonomously learn the correct control sequence, searching through the large number of potential solutions can be time consuming. This paper constrains this search to variations of a generalized solution encoded in a framework known as an action schema. A new algorithm, SCHEMA STRUCTURED LEARNING, is proposed that repeatedly executes variations of the generalized solution in search of instantiations that satisfy action schema objectives. This approach is tested in a grasping task where Dexter, the UMass humanoid robot, learns which reaching and grasping controllers maximize the probability of grasp success

Double-outlet right ventricle: Morphologic demonstration using nuclear magnetic resonance imaging

Sixteen patients with double-outlet right ventricle, aged 1 week to 29 years (median 5 months), were studied with a 1.5 tesla nuclear magnetic resonance (NMR) imaging scanner. Two-dimensional echocardiography was performed in all patients. Thirteen patients underwent angiography, including nine who underwent subsequent surgical correction. Three patients underwent postmortem examination.Small children and infants were scanned inside a 32 cm diameter proton head coil. Multiple 5 mm thick sections separated by 0.5 mm and gated to the patient's electrocardiogram were acquired with a spin-echo sequence and an echo time of 30 ms. A combination of standard and oblique imaging planes was used. Imaging times were <90 min. The NMR images were technically unsuitable in one patient because of excessive motion artifact.In the remaining patients, the diagnosis of double outlet right ventricle was confirmed and correlated with surgical and postmortern findings. The NMR images were particularly valuable in demonstrating the interrelations between the great arteries and the anatomy of the outlet septum and the spatial relations between the ventricular septal defect and the great arteries. Although the atrioventricular (AV) valves were not consistently demonstrated, NMR imaging in two patients identified abnormalities of the mitral valve that were not seen with two-dimensional echocardiography. In one patient who had a superoinferior arrangement of the ventricles, NMR imaging was the most useful imaging technique for demonstrating the anatomy.In patients with double-outlet right ventricle, NMR imaging can provide clinically relevant and accurate morphologic information that may contribute to future improvement in patient management

A Computational Model of Muscle Recruitment for Wrist Movements

this paper, we present a model of muscle recruitment in the wrist step-tracking task. Muscle activation levels for five muscles are selected so as to satisfy task constraints (moving to the designated target) while also minimizing a measure of the total effort in producing the movement. Imposing these constraints yields muscle activation patterns qualitatively similar to those observed experimentally. In particular, the model reproduces the observed cosine-like recruitment of muscles as a function of movement direction and also appropriately predicts that certain muscles will be recruited most strongly in movement directions that differ significantly from their direction of action. These results suggest that the observed recruitment behavior may not be an explicit strategy employed by the nervous system, but instead may result from a process of movement optimizatio