Neural ensemble activity from multiple brain regions predicts kinematic and dynamic variables in a multiple force field reaching task

• Published in: IEEE transactions on neural systems and rehabilitation engineering 2006-06, Vol.14 (2), p.172-174
• Main Authors: Francis, J.T., Chapin, J.K.
• Format: Article
• Language: English
• Subjects: Algorithms; Animals; Behavior, Animal - physiology; Biomechanical Phenomena - methods; Brain Mapping - methods; Brain modeling; Brain-machine interface (BCI); Communication Aids for Disabled; Computer Simulation; Evoked Potentials, Motor - physiology; Female; Hand Strength; Kinematics; Man-Machine Systems; manipulandum; Manipulator dynamics; Microelectrodes; Micromotors; Models, Neurological; Motor Cortex - physiology; motor learning; Movement - physiology; Nerve Net - physiology; Neural prosthesis; Predictive models; Rats; reaching movements; Robotics - methods; Stress, Mechanical; Studies; Task Performance and Analysis; Torque; User-Computer Interface
• ISSN: 1534-4320; 1558-0210
• DOI: 10.1109/TNSRE.2006.875553

In everyday life, we reach, grasp, and manipulate a variety of different objects all with their own dynamic properties. This degree of adaptability is essential for a brain-controlled prosthetic arm to work in the real world. In this study, rats were trained to make reaching movements while holding a torque manipulandum working against two distinct loads. Neural recordings obtained from arrays of 32 microelectrodes spanning the motor cortex were used to predict several movement related variables. In this paper, we demonstrate that a simple linear regression model can translate neural activity into endpoint position of a robotic manipulandum even while the animal controlling it works against different loads. A second regression model can predict, with 100% accuracy, which of the two loads is being manipulated by the animal. Finally, a third model predicts the work needed to move the manipulandum endpoint. This prediction is significantly better than that for position. In each case, the regression model uses a single set of weights. Thus, the neural ensemble is capable of providing the information necessary to compensate for at least two distinct load conditions.