Neural interfaces are one of the most exciting emerging
technologies to impact bioengineering and neuroscience because they
enable an alternate communication channel linking directly the
nervous system with man-made devices. This book reveals the
essential engineering principles and signal processing tools for
deriving control commands from bioelectric signals in large
ensembles of neurons. The topics featured include analysis
techniques for determining neural representation, modeling in motor
systems, computing with neural spikes, and hardware implementation
of neural interfaces. Beginning with an exploration of the
historical developments that have led to the decoding of
information from neural interfaces, this book compares the theory
and performance of new neural engineering approaches for BMIs.
Contents: Introduction to Neural Interfaces / Foundations of
Neuronal Representations / Input-Outpur BMI Models / Regularization
Techniques for BMI Models / Neural Decoding Using Generative BMI
Models / Adaptive Algorithms for Point Processes / BMI Systems
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