The long-term objective of this proposal is to develop a novel brain computer interface (BCI) algorithm targeted for use in applications such as functional neuromuscular stimulation of paralyzed limbs. Toward this overall goal, the first aim of this proposal seeks to identify properties of motor cortical areas that may aid in our BCI pursuits. Specifically, our first objective will seek to identify differences and flexibility in electrocorticography (ECoG) representations of movement parameters across motor-associated cortical areas when movement of the end-effector for a task (i.e. a computer cursor used to complete the task) is dissociated from the physical movement leading up to it. Previous studies have shown that high gamma band ECoG and LFP activity (>60 Hz) over motor cortex is well correlated to hand speed during reaching tasks, while single unit studies have suggested that premotor cortical areas may be more involved in the planning and/or perception of reaching movements than the physical movement itself. However, these studies usually employed a linear transformation of the physical arm movements to visual feedback of the end-effector for the task. To further investigate the differences in representation of movement across cortical areas, we will train three male rhesus monkeys (Macaca mulatta) on a 2-D center-out joystick task in which position of the joystick (used as a proxy for hand position) is remapped to the position, velocity, or acceleration of a goal-oriented computer cursor, thus removing any linear correlations between joystick and cursor for velocity and acceleration remapping schemes. We will first examine the epidural ECoG spectra of arrays implanted over M1, PMd, and parietal area 5 for correlations with movement parameters of the joystick or cursor, and compare these correlations across remapping schemes to identify whether a particular area is more correlated with planning/perceptual aspects of a movement or with its physical execution. Our second objective is to utilize properties of these ECoG representations and determine whether monkeys can learn to modulate a force-based control signal in a brain- computer interface (BCI) task. The majority of BCI experiments to date have utilized kinematic-based control signals (i.e. a brain signal is translated to the position or velocity of an object). However, these control signals may not be suitable for applications such as functional neuromuscular stimulation (FNS) of a paralyzed limb where an accurate model of an individual's arm is unknown or incomplete. To address this problem and assess the feasibility of a dynamics-based control algorithm that may be more appropriate for FNS, we will train monkeys on a force-based BCI task. In these experiments, high gamma (75-105 Hz) ECoG activity will be mapped to a virtual force on a computer cursor in a center-out like task in which the computer cursor will be used to """"""""grab"""""""" and move peripheral targets with mass. The virtual environment will also incorporate simple real-world physics such as gravity and variable masses to assess how well a subject might be able to adapt to these perturbations in real-life with the control algorithm of interest.
This proposal investigates how the brain represents voluntary movements, whether it be their planning, execution, or perception. Understanding the intricacies and flexibility of this system is important for developing treatments for neurological damage and disorders that impact the motor system. With this knowledge we seek to develop new Brain Computer Interface (BCI) technologies that could restore muscular control to patients such as those with spinal cord injuries.
