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...
This is a preview of subscription content, log in via an institution to check access.
References
Bendat JS, Piersol AG (2010) Random data: analysis and measurement procedures, 4th edn. Wiley, Hoboken
Bloomfield P (2000) Fourier analysis of time series. Wiley, New York
Brillinger D (2001) Time series: data analysis and theory. SIAM, Philadelphia
Chatfield C (2004) The analysis of time series: an introduction. Chapman & Hall/CRC, Boca Raton
Ding M, Bressler SL, Yang W, Liang H (2000) Short-window spectral analysis of cortical event-related potentials by adaptive multivariate autoregressive modeling: data preprocessing, model validation, and variability assessment. Biol Cybern 83:34–45
Ding M, Chen Y, Bressler SL (2006) Granger causality: basic theory and application to neuroscience. In: Schelter B, Winterhalder M, Timmer J (eds) Handbook of time series analysis: recent theoretical developments and applications. Wiley-VCH, Weinheim
Dumermuth G, Molinari L (1987) Spectral analysis of EEG background activity. In: Gevins AS, Remond A (eds) Methods of analysis of brain electrical and magnetic signals. Handbook of electroencephalography and clinical neurophysiology, revised series, vol 1. Elsevier, Amsterdam
Glaser EM, Ruchkin DS (1976) Principles of neurobiological signal analysis. Academic, New York
Hesselmann NL (1991) The fundamentals of discrete Fourier analysis. In: Weitkunat R (ed) Digital biosignal processing. Elsevier, Amsterdam/New York
Jenkins GM, Watts DG (1968) Spectral analysis and its applications. Holden-Day, San Francisco
Kay SM (1988) Modern spectral estimation: theory and application. Prentice-Hall, Englewood Cliffs
Marple SL (1987) Digital spectral analysis with applications. Prentice-Hall, Englewood Cliffs
Percival DB, Walden AT (1993) Spectral analysis for physical applications: multitaper and conventional univariate techniques. Cambridge University Press, Cambridge
Warner RM (1998) Spectral analysis of time-series data. The Guilford Press, New York
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media New York
About this entry
Cite this entry
Bressler, S.L. (2014). Spectral Methods in Neural Data Analysis: Overview. In: Jaeger, D., Jung, R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7320-6_777-1
Download citation
DOI: https://doi.org/10.1007/978-1-4614-7320-6_777-1
Received:
Accepted:
Published:
Publisher Name: Springer, New York, NY
Online ISBN: 978-1-4614-7320-6
eBook Packages: Springer Reference Biomedicine and Life SciencesReference Module Biomedical and Life Sciences
Publish with us
Chapter history
-
Latest
Spectral Methods in Neural Data Analysis: Overview- Published:
- 27 November 2020
DOI: https://doi.org/10.1007/978-1-4614-7320-6_777-2
-
Original
Spectral Methods in Neural Data Analysis: Overview- Published:
- 24 March 2014
DOI: https://doi.org/10.1007/978-1-4614-7320-6_777-1