Comparison of Matching Pursuit Algorithm with Other Signal Processing Techniques for Computation of the Time-Frequency Power Spectrum of Brain Signals

Signals recorded from the brain often show rhythmic patterns at different frequencies, which are tightly coupled to the external stimuli as well as the internal state of the subject. In addition, these signals have very transient structures related to spiking or sudden onset of a stimulus, which hav...

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Published inThe Journal of neuroscience Vol. 36; no. 12; pp. 3399 - 3408
Main Authors Chandran KS, Subhash, Mishra, Ashutosh, Shirhatti, Vinay, Ray, Supratim
Format Journal Article
LanguageEnglish
Published United States Society for Neuroscience 23.03.2016
Subjects
Algorithms
Brain Waves - physiology
Electroencephalography - methods
Fourier Analysis
Humans
Machine Learning
Pattern Recognition, Automated - methods
Reproducibility of Results
Sensitivity and Specificity
Signal Processing, Computer-Assisted
TechSights
Visual Cortex - physiology
Wavelet Analysis
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Summary:Signals recorded from the brain often show rhythmic patterns at different frequencies, which are tightly coupled to the external stimuli as well as the internal state of the subject. In addition, these signals have very transient structures related to spiking or sudden onset of a stimulus, which have durations not exceeding tens of milliseconds. Further, brain signals are highly nonstationary because both behavioral state and external stimuli can change on a short time scale. It is therefore essential to study brain signals using techniques that can represent both rhythmic and transient components of the signal, something not always possible using standard signal processing techniques such as short time fourier transform, multitaper method, wavelet transform, or Hilbert transform. In this review, we describe a multiscale decomposition technique based on an over-complete dictionary called matching pursuit (MP), and show that it is able to capture both a sharp stimulus-onset transient and a sustained gamma rhythm in local field potential recorded from the primary visual cortex. We compare the performance of MP with other techniques and discuss its advantages and limitations. Data and codes for generating all time-frequency power spectra are provided.
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A.M. and V.S. contributed equally to this work.
ISSN:0270-6474
1529-2401
DOI:10.1523/JNEUROSCI.3633-15.2016