Free Ebook Statistical Signal Processing for Neuroscience and NeurotechnologyFrom Academic Press
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Statistical Signal Processing for Neuroscience and NeurotechnologyFrom Academic Press
Free Ebook Statistical Signal Processing for Neuroscience and NeurotechnologyFrom Academic Press
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This is a uniquely comprehensive reference that summarizes the state of the art of signal processing theory and techniques for solving emerging problems in neuroscience, and which clearly presents new theory, algorithms, software and hardware tools that are specifically tailored to the nature of the neurobiological environment. It gives a broad overview of the basic principles, theories and methods in statistical signal processing for basic and applied neuroscience problems.
Written by experts in the field, the book is an ideal reference for researchers working in the field of neural engineering, neural interface, computational neuroscience, neuroinformatics, neuropsychology and neural physiology. By giving a broad overview of the basic principles, theories and methods, it is also an ideal introduction to statistical signal processing in neuroscience.
- A comprehensive overview of the specific problems in neuroscience that require application of existing and development of new theory, techniques, and technology by the signal processing community
- Contains state-of-the-art signal processing, information theory, and machine learning algorithms and techniques for neuroscience research
- Presents quantitative and information-driven science that has been, or can be, applied to basic and translational neuroscience problems
- Sales Rank: #2027242 in Books
- Published on: 2010-08-18
- Original language: English
- Number of items: 1
- Dimensions: 9.43" h x 1.08" w x 7.39" l, 2.08 pounds
- Binding: Hardcover
- 433 pages
Review
"Large-scale recording of multiple single neurons has become an indispensable tool in system neuroscience. The chapters of this edited volume will take the reader from spike detection and processing through analyses to modeling and interpretation. Both experimentalists and theorists will benefit from the well-condensed and organized content."
Gy�rgy Buzs�ki, M.D., Ph.D. Center for Molecular and Behavioral Neuroscience Rutgers University
From the Back Cover
This is a uniquely comprehensive reference that summarizes the state of the art of signal processing and machine learning theory and techniques applied to emerging problems in neuroscience, with special emphasis on basic and clinical applications of neurotechnology. Written by experts in the field, the book is an ideal reference for engineering researchers and graduate students working in the field of neural engineering, neuroprosthesis, brain machine and brain computer interfaces, computational and systems neuroscience, neuroinformatics, and neurophysiology. It provides a broad overview of the basic principles, theories and methods of statistical signal processing, information theory and machine learning and their applications in neuroscience.
�Features:
● Provides a comprehensive overview of classical and modern signal processing theory and techniques for analyzing neural data
● Presents quantitative and information-driven science that has been, or can be, applied to basic and translational neuroscience problems
● Discusses practical implementation issues and design considerations for neurotechnology, particularly related to neuroprosthetic and brain machine interface system design.
Karim G. Oweiss�received his Ph.D. in Electrical Engineering and Computer Science from the University of Michigan, Ann Arbor in 2002 and has been with the Department of Electrical and Computer Engineering and the Neuroscience program at Michigan State University since 2003. He is a member of the IEEE and Society for Neuroscience and was awarded the excellence in Neural Engineering award from the National Science Foundation in 2001.
"Large-scale recording of multiple single neurons has become an indispensable tool in system neuroscience. The chapters of this edited volume will take the reader from spike detection and processing through analyses to modeling and interpretation. Both experimentalists and theorists will benefit from the well-condensed and organized content."
Gy�rgy Buzs�ki, M.D., Ph.D., Center for Molecular and Behavioral Neuroscience, Rutgers University
About the Author
Karim G. Oweiss received his B.S. (1993) and M.S. (1996) degrees with honors in electrical engineering from the University of Alexandria, Egypt, and his Ph.D. (2002) in electrical engineering and computer science from the University of Michigan, Ann Arbor. In that year he also completed postdoctoral training with the Department of Biomedical Engineering at the University of Michigan. In 2003, he joined the Department of Electrical and Computer Engineering and the Neuroscience Program at Michigan State University, where he is currently an associate professor and director of the Neural Systems Engineering Laboratory. His research interests are in statistical signal processing, information theory, machine learning, and control theory, with direct applications to studies of neuroplasticity, neural integration and coordination in sensorimotor systems, neurostimulation and neuromodulation in brain-machine interfaces, and computational neuroscience.
Professor Oweiss is a member of the IEEE and the Society for Neuroscience. He served as a member of the board of directors of the IEEE Signal Processing Society on Brain-Machine Interfaces and is currently an active member of the technical and editorial committees of the IEEE Biomedical Circuits and Systems Society, the IEEE Life Sciences Society, and the IEEE Engineering in Medicine and Biology Society. He is also associate editor of IEEE Signal Processing Letters, Journal of Computational Intelligence and Neuroscience, and EURASIP Journal on Advances in Signal Processing. He currently serves on an NIH Federal Advisory Committee for the Emerging Technologies and Training in Neurosciences. In 2001, Professor Oweiss received the Excellence in Neural Engineering Award from the National Science Foundation.
Most helpful customer reviews
2 of 2 people found the following review helpful.
Well researched, comprehensive exploration
By Comdet
This textbook brings together some of the top people in the neuroscience field to create a highly effective compendium of articles. Topics range from basic background concepts (which is very well done - clear and effective) to outline directions of potential future breakthroughs.
The math required is significant, but for the most part, nothing beyond an undergraduate level understanding. I have not read all the chapters since some deal with issues of lesser interest to me, but the ones I have read have been clearly written and organized, typically the sign of a skilled editor. Kudos to Oweiss for a job well done.
I find the marriage of neuroscience and engineering to be fascinating. The concept and promise of neural prostheses is incredible, especially when dealing with spinal cord injuries. I envy those just getting into this field, since the next few decades will see some enormous advances.
3 of 4 people found the following review helpful.
A comprehensive textbook, heavy on the math
By neurotome
This 2010 textbook provides an overview of the state of the art in computational and statistical methods of analyzing neuronally generated potentials. From in vitro and in vivo single-neuron spike mapping, to detailed analysis of event analysis in scalp EEG, to more complicated analysis of contemporaneous EEG and imaging waveforms, this book presents theoretical groundings, appropriate statistical methods, and computational models and methods. There is a good deal of applied math, probability theory, and statistical sophistication that is assumed for the reader. I think the target reader probably has or is pursuing a Ph.D. in one of these topics, and this book is probably best suited as a text for an advanced graduate level course.
I liked the book and found it useful, clear and easily referenced. I will be incorporating some of the ideas I found in it to an upcoming project.
0 of 0 people found the following review helpful.
Numerical Analysis of Thought and Information Processing
By Scholastic Reader
This book is quite informative of the more recent ways neuroscience and neurotechnology has been numerically analyzed. Granted that neorscience and neurotechnology have recently received some momentum, this book would be of good benefit for those looking for some texts that focus on the numerical methods used in studying and interpreting complex networks (biological and technological). The first half of the book focuses on biology. The second half focuses on technology. In both, it is easily seen that many advances have already been made with much of it requiring simply adjusting, but there is some room for major advancements. The chapters on prosthetic of hand gripping and neoruoimaging offer a glimpse into what technologies have existed thus far to assist procurement of data on neural correlates and medical advances relating to paralysis and brain-machine communications. The book notes that studying the brain is really a sophisticated task since the brain is the most sophisticated organ in the universe. The amount of neural networks and the complexity of cognition speak volumes on this. The dynamics of thought are what make these studies in "electrical" activity different from non-biological "electrical" activity. Really the only way to study the brain is by statistical clumping of neural activity and monitoring/interpreting spike trains. This seems to be the reason why the book notes "statistical" on the title. Overall it is a medium-to-high read. It is very much mathematically involved, but most chapters are not overwhelming. However, prior knowledge of statistics and process control approaches does help.
Here are the contents (this is not exhaustive):
Ch. 1 - Introduction
Mainly an overview of all other chapters
Ch. 2 - Detection and Classification of Extracellular Action Potential Recordings
Classification of "spikes" in neural activity recordings, stochastic considerations of "noise" that emerges from raw data, transform domain used to reduce noise, approaches to spike sorting - distinguishing one neuron form a mixture of neurons , practical implementation problems in implants ("on a chip"), using sensible sensory models for safe neural cluster differentiations
Ch. 3 - Information-Theoretic Analysis of Neural Data
Shannon's information theory which focused on digital communication is mentioned on how it can be applied to neuroscience, differences in Shannon and neuroscience interpretation of entropy, encoding & channel, Rate Distortion Theory
Ch. 4 - Identification of Nonlinear Dynamics in Neural Population Activity
Attempt at quantitative modeling of hippocampus (as it serves function in cognition) and understanding of its neuronal network, a nonlinear model is developed which consists of spiking neuron modeling and Volterra series, Multiple input multiple output and multiple input single output configurations, the model is applied to data from rats and a two lever experiment
Ch. 5 - Graphical Models of Functional and Effective Neuronal Connectivity
Focuses on reviewing different approaches in studying neuronal connectivities which are relevant to memory and learning, the approaches are used in experimental data from rats and relevant findings are discussed
Ch. 6 - State Space Modeling of Neural Spike Train and Behavioral Data
What the chapter title says, some studies on animals are used to asses State Space Modeling
Ch. 7 - Neural Decoding for Motor and Communication Prostheses
Focuses on decoding of neural signals to improve prosthetic design, discuses motor and communication prostheses with neural activity, two types of neural activity - plan and movement activity; data from studies in animals discussed, trajectory models for goal directed movements are proposed and tested, propositions to decoding neural signals
Ch. 8 - Inner Products for Representation and Learning in the Spike Train Domain
Framework for machine learning methods for spike trains
Ch. 9 - Signal Processing and Machine Learning for Signal-trial Analysis of Simultaneously Acquired EEG and fMRI
Essentially about issues with EEG and fMRI equipment, as these are neuroimaging tools, and focuses on issues of design of such equipment
Ch. 10 - Statistical Pattern Recognition and Machine Learning in Brain-Computer Interfaces
About how to link the brain to machines via brain-computer interfaces
Ch. 11 - Prediction of Muscle Activity from Cortical Signals to Restore Hand Grasp in Subjects with Spinal Chord Injury
On prosthetics of gripping, some review of current technologies, experiment is done on monkeys to evaluate functional electrical stimulation and brain-machine interfaces
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