Time–Frequency Methods and Brain Rhythm Signal Processing

  • Jesse Gillis
Part of the Springer Series in Computational Neuroscience book series (NEUROSCI, volume 2)


Physiological signal analysis could encompass any of the methods of signal analysis being brought to bear upon a problem in physiology. In practice, physiological signal analysis is a narrower field than this because physiological signals share common features which limit the applicability of different methods. An important property of physiological signals is that they typically possess features determined by their historical contribution to organism fitness. This gives rise to the problem of identifying and classifying features present in a physiological signal. The primary statistical feature of physiological signals is that they are nonstationary, meaning their statistical characteristics change with time, and a variety of different techniques exist to analyze such data. The suitability of such techniques is data dependent, and particularly upon the degree and manner in which the signal changes in time and frequency. This is a well-known problem and specialized techniques have been developed for particular data sets (e.g., Achermann and Borbély, 1998). Developing data-specific methodologies presents its own difficulties in terms of hypothesis and verifiability (e.g., separation into training and testing sets for the methodology). In this article, I focus on introducing standard and well-defined techniques relevant to signal processing of brain rhythms as well as the organization of time–frequency activities in a hippocampal rhythm. In general, analyses consist of three stages: (1) Transformation into the time–frequency domain; (2) Quantification of properties in the time–frequency domain; and (3) Grouping together of these properties. For example, wavelet transform (time–frequency transform) coefficients (quantification) might be extracted and clustered (grouping) (Quiroga et al., 2002). Filtering of a neurophysiological signal might be performed (an implicit time–frequency step), a threshold chosen (an implicit quantification), and the remaining data averaged (grouping). Below, I suggest a generally useful way to incorporate these three steps into analyses with a minimum of additional assumptions, i.e., a time–frequency transformation, followed by characterization of the time–frequency domain’s local activity, and then mixture distribution analysis.


Frequency Resolution Mother Wavelet Wigner Distribution Brain Rhythm Spike Sorting 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.The Centre for High-Throughput Biology (CHiBi), 177, Michael Smith LaboratoriesUniversity of British ColumbiaVancouverCanada

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