Statistical Signal Processing for Neuroscience and Neurotechnology

• 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: