Signal Processing in Neuroscience

This book reviews cutting-edge developments in neural signalling processing (NSP), systematically introducing readers to various models and methods in the context of NSP.

Neuronal Signal Processing is a comparatively new field in computer sciences and neuroscience, and is rapidly establishing itself as an important tool, one that offers an ideal opportunity to forge stronger links between experimentalists and computer scientists. This new signal-processing tool can be used in conjunction with existing computational tools to analyse neural activity, which is monitored through different sensorssuch as spike trains, local filed potentials and EEG. The analysis of neural activity can yield vital insights into the function of the brain. This book highlights the contribution of signal processing in the area of computational neuroscience by providing a forum for researchers in this field to share their experiences to date.

Highlighting the contribution of signal processing in the area of computational neuroscience Introducing the cutting-edge development in neural signaling processing Written by experts in this field

Autorentext
Dr. Xiaoli Li is a Professor from State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University. He is the committee member of Medical Neural Engineering Branch of Chinese Society of Biomedical Engineering. He serves as the editor for the journal "International Journal of Tomography & Stimulation" and "International Journal of Applied Mathematics & Statistics". Prof. Li's research interests include neural engineering, bio-signal processing and intelligent monitoring. He has published more than 100 research articles on different international journals.

Inhalt
Brief history and development of electrophysiological recording techniques in neuroscience.- Adaptive Spike Sorting with a Gaussian Mixture Model.- Causality of Spike Trains Based on Entropy.- Quantification of Spike-LFP Synchronization.- Artifact removal in EEG recordings.- Order Time Series Analysis of Neural Signals.- Dynamical Similarity Analysis of EEG Recordings.- Entropy Measures in Neural Signals.- Synchronization Measures in EEG Signals.- Estimating Coupling Direction between Neuronal Populations.- The comodulation measure of neuronal oscillations.- Multivariate EEG synchronization strength measures.- Cross-Frequency Coupling in Neural Oscillations.- Nonnegative Matrix and Tensor Decomposition of EEG.