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Cognitive electrophysiology concerns the study of the brain’s electrical and magnetic responses to both external and internal events. These can be measured using electroencephalograms (EEGs) or magnetoencephalograms (MEGs). With the advent of functional magnetic resonance imaging, another method of tracking brain signals, the tools and techniques of EEG and MEG data acquisition and analysis have been developing at a similarly rapid pace, and this book offers an overview of key recent advances in cognitive electrophysiology. The chapters highlight the increasing overlap in EEG and MEG analytic techniques, describing several methods applicable to both; discuss recent developments, including reverse correlation methods in visual-evoked potentials and a new approach to topographic mapping in high-density electrode montage; and relate the latest thinking on design aspects of EEG/MEG studies, discussing how to optimize the signal-to-noise ratio as well as statistical developments for maximizing power and accuracy in data analysis using repeated-measure ANOVAS.

This book offers an introduction to current methods in computational modeling in neuroscience, and describes realistic modeling methods at levels of complexity ranging from molecular interactions to large neural networks. A “how to” book rather than an analytical account, it focuses on the presentation of methodological approaches, including the selection of the appropriate method and its potential pitfalls. The book is intended for experimental neuroscientists and graduate students who have little formal training in mathematical methods, but will also be useful for scientists with theoretical backgrounds who want to start using data-driven modeling methods. The mathematics needed are kept to an introductory level; the first chapter explains the mathematical methods the reader needs to master to understand the rest of the book. The chapters are written by scientists who have successfully integrated data-driven modeling with experimental work, so all of the material is accessible to experimentalists and offers comprehensive coverage with little overlap, and extensive cross-references moving from basic building blocks to more complex applications.