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Brain-Machine Interface Engineering [electronic resource] / by Justin C. Sanchez, José C. Príncipe.

By: Sanchez, Justin C [author.].
Contributor(s): Príncipe, José C [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Biomedical Engineering: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2007Edition: 1st ed. 2007.Description: X, 234 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031016219.Subject(s): Engineering | Biophysics | Biomedical engineering | Technology and Engineering | Biophysics | Biomedical Engineering and BioengineeringAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 620 Online resources: Click here to access online
Contents:
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.
In: Springer Nature eBookSummary: 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 GenerativeBMI Models / Adaptive Algorithms for Point Processes / BMI Systems.
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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.

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 GenerativeBMI Models / Adaptive Algorithms for Point Processes / BMI Systems.

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