Encyclopedia of Computational Neuroscience

2015 Edition
| Editors: Dieter Jaeger, Ranu Jung

Spectral Methods in Neural Data Analysis: Overview

Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-6675-8_777

Detailed Description

Spectral analysis is a powerful and widely used approach to the study of time series data (Warner 1998; Bloomfield 2000). It provides a useful complement to other types of analysis in computational neuroscience. Spectral analysis refers to a host of techniques relating to transformed time series in the frequency domain. A spectral representation of a time series is a function of frequency, where frequency is expressed in units of cycles per second, or hertz (Hz). Although spectral analysis is applicable to deterministic time functions, neural data is typically stochastic and thus requires statistical spectral analysis (Brillinger 2001; Bendat and Piersol 2010). Neural data types that are subjected to spectral analysis commonly include continuous time series such as the electroencephalogram (EEG), magnetoencephalogram (MEG), electrocorticogram (ECoG), and local field potential (LFP) but may also include point process time series such as single-unit and multiunit...

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Further Reading

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Cognitive Neurodynamics Laboratory, Center for Complex Systems and Brain Sciences, Department of PsychologyFlorida Atlantic UniversityBoca RatonUSA