ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

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

Chandran, Subhash KS and Mishra, Ashutosh and Shirhatti, Vinay and Ray, Supratim (2016) Comparison of Matching Pursuit Algorithm with Other Signal Processing Techniques for Computation of the Time-Frequency Power Spectrum of Brain Signals. In: JOURNAL OF NEUROSCIENCE, 36 (12). pp. 3399-3408.

[img] PDF
Jou_Neu_36-12_3399_2016.pdf - Published Version
Restricted to Registered users only

Download (2160Kb) | Request a copy
Official URL: http://dx.doi.org/10.1523/JNEUROSCI.3633-15.2016

Abstract

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.

Item Type: Journal Article
Related URLs:
Additional Information: Copy right for this article belongs to the SOC NEUROSCIENCE, 11 DUPONT CIRCLE, NW, STE 500, WASHINGTON, DC 20036 USA
Department/Centre: Division of Biological Sciences > Centre for Neuroscience
Division of Electrical Sciences > Electrical Engineering
Division of Physical & Mathematical Sciences > Mathematics
Date Deposited: 23 Apr 2016 05:38
Last Modified: 23 Apr 2016 05:38
URI: http://eprints.iisc.ernet.in/id/eprint/53684

Actions (login required)

View Item View Item