Download e-book for kindle: Analyzing Neural Time Series Data: Theory and Practice by Mike X Cohen

By Mike X Cohen

ISBN-10: 0262019876

ISBN-13: 9780262019873

This booklet deals a finished consultant to the idea and perform of examining electric mind signs. It explains the conceptual, mathematical, and implementational (via Matlab programming) features of time-, time-frequency- and synchronization-based analyses of magnetoencephalography (MEG), electroencephalography (EEG), and native box strength (LFP) recordings from people and nonhuman animals. it's the simply ebook at the subject that covers either the theoretical heritage and the implementation in language that may be understood by way of readers with out wide formal education in arithmetic, together with cognitive scientists, neuroscientists, and psychologists.

Readers who cross in the course of the booklet bankruptcy via bankruptcy and enforce the examples in Matlab will strengthen an knowing of why and the way analyses are played, easy methods to interpret effects, what the methodological concerns are, and the way to accomplish single-subject-level and group-level analyses. Researchers who're conversant in utilizing automatic courses to accomplish complex analyses will examine what occurs after they click on the "analyze now" button. The publication presents pattern facts and downloadable Matlab code. all of the 38 chapters covers one research subject, and those subject matters development from uncomplicated to complicated. so much chapters finish with workouts that extra advance the cloth lined within the bankruptcy. a number of the equipment offered (including convolution, the Fourier rework, and Euler's formulation) are primary and shape the basis for different complicated information research tools. Readers who grasp the equipment within the ebook might be ready to benefit different techniques.

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Extra info for Analyzing Neural Time Series Data: Theory and Practice

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The spatial precision of EEG is fairly low but can be improved by spatial filters such as the surface Laplacian or adaptive source-space-imaging techniques. Simulation studies show that with many electrodes (>200) and anatomically precise forward models of subject-specific brain and skull anatomy, the spatial precision of source reconstruction can be fairly high. In practice, however, such high spatial precision is more difficult to obtain. The spatial accuracy of EEG is low. Activity recorded from one electrode does not reflect only activity from neurons directly below that electrode, but rather, from a complex mixture of activities from many brain regions close to and distant from that electrode.

Topographical localization does not necessarily mean that the activity was generated by neural populations directly underneath that electrode, although this may be the case, for example, with radial EEG dipoles after the surface Laplacian has been applied. Brain localization refers to identifying the locations in the brain that generated the activity measured from the scalp. Brain localization is more difficult to determine and involves greater uncertainty than topographical localization, in part because brain localization relies on several assumptions about brain shape, electrical conductivity, and so on, and in part because the inverse problem has many possible solutions that are equally likely (that is, a large number of plausible brain states could produce the same topographical distribution of activity).

Frequency precision). On the other hand, if you want to measure changes in heart rate in 5-s steps, you have to accept some uncertainty in the precise rate in order to measure changes over time. For the same reason it is not possible to record 20 ms of EEG and extract information about 5-Hz brain activity. Generally, you can expect uncertainties on the order of tens of milliseconds and several hertz, although this depends on the type of analysis and on the parameters you choose for that analysis.

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Analyzing Neural Time Series Data: Theory and Practice by Mike X Cohen


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