A Brain-Machine Interface Enables Bimanual Arm Movements in Monkeys

Brain-machine interfaces (BMIs) are artificial systems that aim to restore sensation and movement to paralyzed patients. So far, BMIs have enabled only one arm to be moved at a time. Control of bimanual arm movements remains a major challenge. We have developed and tested a bimanual BMI that enables rhesus monkeys to control two avatar arms simultaneously. The bimanual BMI was based on the extracellular activity of 374 to 497 neurons recorded from several frontal and parietal cortical areas of both cerebral hemispheres. Cortical activity was transformed into movements of the two arms with a decoding algorithm called a fifth-order unscented Kalman filter (UKF). The UKF was trained either during a manual task performed with two joysticks or by having the monkeys passively observe the movements of avatar arms. Most cortical neurons changed their modulation patterns when both arms were engaged simultaneously. Representing the two arms jointly in a single UKF decoder resulted in improved decoding performance compared with using separate decoders for each arm. As the animals' performance in bimanual BMI control improved over time, we observed widespread plasticity in frontal and parietal cortical areas. Neuronal representation of the avatar and reach targets was enhanced with learning, whereas pairwise correlations between neurons initially increased and then decreased. These results suggest that cortical networks may assimilate the two avatar arms through BMI control. These findings should help in the design of more sophisticated BMIs capable of enabling bimanual motor control in human patients.

Bimanual brain-machine interface

Zheng Li, Miguel Nicolelis, Solaiman Shokur

Brain-machine interfaces (BMIs) - devices that connect brain areas to external actuators - strive to restore limb mobility and sensation to patients suffering from paralysis or limb loss. Here we report a novel BMI that controls two virtual arms simultaneously. The development of BMIs for bimanual control is important because even the most basic daily movements such as opening a jar or buttoning a shirt require two arms. We for the first time have designed and implemented a bimanual BMI where activity of multiple cortical areas is translated in real-time into center-out reaching movements performed by two virtual arms. Eight multielectrode arrays, a total of 768 electrode channels, were implanted in the primary motor (M1), sensory (S1), supplementary motor (SMA), dorsal premotor (PMd), and posterior parietal (PP) cortices of both hemispheres of a rhesus monkey. Movement kinematics of each arm were extracted from the same ensemble of 400 neurons using a Wiener filter and an unscented Kalman filter (UKF). Typically, a single neuron contributed to the movements of both left and right arms. Movements were enacted by arms of a virtual rhesus monkey avatar on a computer screen presented in first-person to the monkey. On each trial, the virtual arms moved their central locations to peripheral targets presented simultaneously on the right and left sides of the computer screen. Peri-event time histograms and linear discriminant analysis revealed a highly distributed encoding scheme, with movement directions of both limbs represented by both ipsilateral and contralateral areas. Furthermore, movements were represented by multiple cortical regions, including both primary and non-primary motor areas which have been previously identified areas important for bimanual coordination. Over the course of several weeks of real-time BMI control, the monkey’s performance clearly improved both when the monkey continued to move the joystick and when the joystick was removed. These results support the feasibility of cortically-driven clinical neural prosthetics for bimanual operations.

2012

Bimanual brain-machine interface

Zheng Li, Miguel Nicolelis, Solaiman Shokur

2012