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

By Mike X Cohen

ISBN-10: 0262019876

ISBN-13: 9780262019873

This publication deals a finished consultant to the speculation 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 capability (LFP) recordings from people and nonhuman animals. it's the merely e-book at the subject that covers either the theoretical heritage and the implementation in language that may be understood via readers with no wide formal education in arithmetic, together with cognitive scientists, neuroscientists, and psychologists.

Readers who pass throughout 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 accustomed to utilizing computerized courses to accomplish complex analyses will examine what occurs once they click on the "analyze now" button. The ebook presents pattern facts and downloadable Matlab code. all the 38 chapters covers one research subject, and those themes development from basic to complicated. such a lot chapters finish with routines that extra boost the fabric lined within the bankruptcy. a number of the tools provided (including convolution, the Fourier remodel, and Euler's formulation) are primary and shape the basis for different complex information research equipment. Readers who grasp the equipment within the e-book should be ready to benefit different methods.

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

This publication bargains a complete advisor to the speculation and perform of studying electric mind signs. It explains the conceptual, mathematical, and implementational (via Matlab programming) facets of time-, time-frequency- and synchronization-based analyses of magnetoencephalography (MEG), electroencephalography (EEG), and native box power (LFP) recordings from people and nonhuman animals.

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

Example text

Understanding the mathematical bases of advanced data analyses will help you correctly perform those analyses, appropriately interpret results, get more out of your data, avoid errors or suboptimal analysis choices, and critically evaluate results you see in publications, talks, and conference posters. The sample data are given in eeglab format for convenience, but this is not meant as an explicit endorsement of eeglab, nor is it meant as an implicit repudiation of any other Matlab toolbox for analyzing EEG data.

Similarly, different cognitive processes and neural functions seem to utilize different frequency ranges or conjunctions of frequency ranges. Brain rhythms are grouped into bands that are defined by logarithmically increasing center frequencies and frequency widths. Brain rhythm frequency bands include delta (2–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (15–30 Hz), lower gamma (30–80 Hz), and upper gamma (80–150 Hz). These are not the only frequency bands; there are oscillations in the subdelta and omega (up to 600 Hz) ranges, but the frequency bands that are most typically associated with cognitive processes in the literature are between 2 Hz and 150 Hz.

In contrast the temporal precision of 1-Hz bandpass-filtered activity is much lower because each millisecond of the 1-Hz activity is a weighted average of temporally surrounding activity (up to several seconds of activity). This means that there is some temporal leakage, and 1-Hz events that occur at time 200 ms will affect the 1-Hz results at time 600 ms. You will learn why this is the case in chapters 10–14. Thus, the temporal precision depends on the analysis applied, the parameters selected, and the frequency band (higher frequency bands generally have greater temporal precision).

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


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