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Statistical Signal Processing for Neuroscience and Neurotechnology
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  • Statistical Signal Processing for Neuroscience and Neurotechnology
ID: 175053
Karim G. Oweiss
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This is a uniquely comprehensive article of information about the neurobiological environment, which is an alternative to the theory of neuroscience. . It provides a broad and comprehensive approach to neuroscience problems.

Written by neuroscience, neuroinformatics, neuropsychology and neural physiology. By giving a broad overview of the principles of the neuroscience.


  • A comprehensive overview of problems and techniques in a community

  • Contains state-of-the-art signal processing, information theory, and machine learning algorithms and techniques for neuroscience research

  • Presents quantitative and information-driven, and translational neuroscience problems


Introduction; Detection and Classification of Extracellular Action Potential Recordings; Information-Theoretic Analysis of Neural Data; Identification of Nonlinear Dynamics in Neural Population Activity; Graphical Models of Functional and Effective Neuronal Connectivity; State-Space Modeling of Neural Spike Train and Behavioral Data; Neural Decoding for Motor and Communication Prostheses; Inner Products for Representation and Learning in the Spike Train Domain; Signal Processing and Machine Learning for Single-trial Analysis of Simultaneously Acquired EEG and fMRI; Statistical Pattern Recognition and Machine Learning in Brain-Computer Interfaces; Prediction of Muscle Activity from Cortical Signals to Restore Hand Grasp in People with Spinal Cord Injury:

175053

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