Brain Machine Interface Engineering

Brain-Machine Interface Engineering

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

Brain-Machine Interface Engineering

Brain-Machine Interface

This book provides a complete overview of significant design challenges in respect to circuit miniaturization and power reduction of the neural recording system, along with circuit topologies, architecture trends, and (post-silicon) circuit optimization algorithms. The introduced novel circuits for signal conditioning, quantization, and classification, as well as system configurations focus on optimized power-per-area performance, from the spatial resolution (i.e. number of channels), feasible wireless data bandwidth and information quality to the delivered power of implantable system.