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<p>Brain-machine interfaces (BMIs) offer the potential to assist millions of people
worldwide suffering from immobility due to loss of limbs, paralysis, and neurodegenerative
diseases. BMIs function by decoding neural activity from intact cortical brain regions
in order to control external devices in real-time. While there has been exciting
progress in the field over the past 15 years, the vast majority of the work has focused
on restoring of motor function of a single limb. In the work presented in this thesis,
I first investigate the expanded role of primary sensory (S1) and motor (M1) cortex
during reaching movements. By varying target size during reaching movements, I discovered
the cortical correlates of the speed-accuracy tradeoff known as Fitts' law. Similarly,
I analyzed cortical motor processing during tasks where the motor plan is quickly
reprogrammed. In each study, I found that parameters relevant to the reach, such
as target size or alternative movement plans, could be extracted by neural decoders
in addition to simple kinematic parameters such as velocity and position. As such,
future BMI functionality could expand to account for relevant sensory information
and reliably decode intended reach trajectories, even amidst transiently considered
alternatives.</p><p> The second portion of my thesis work was the successful development
of the first bimanual brain-machine interface. To reach this goal, I expanded the
neural recordings system to enable bilateral, multi-site recordings from approximately
500 neurons simultaneously. In addition, I upgraded the experiment to feature a realistic
virtual reality end effector, customized primate chair, and eye tracking system.
Thirdly, I modified the tuning function of the unscented Kalman filter (UKF) to conjointly
represent both arms in a single 4D model. As a result of widespread cortical plasticity
in M1, S1, supplementary motor area (SMA), and posterior parietal cortex (PPC), the
bimanual BMI enabled rhesus monkeys to simultaneously control two virtual limbs without
any movement of their own body. I demonstrate the efficacy of the bimanual BMI in
both a subject with prior task training using joysticks and a subject naïve to the
task altogether, which simulates a common clinical scenario. The neural decoding
algorithm was selected as a result of a methodical comparison between various neural
decoders and decoder settings. I lastly introduce a two-stage switching model with
a classify step and predict step which was designed and tested to generalize decoding
strategies to include both unimanual and bimanual movements.</p>
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