RT Journal Article SR Electronic T1 Comparison of Matching Pursuit Algorithm with Other Signal Processing Techniques for Computation of the Time-Frequency Power Spectrum of Brain Signals JF The Journal of Neuroscience JO J. Neurosci. FD Society for Neuroscience SP 3399 OP 3408 DO 10.1523/JNEUROSCI.3633-15.2016 VO 36 IS 12 A1 Chandran KS, Subhash A1 Mishra, Ashutosh A1 Shirhatti, Vinay A1 Ray, Supratim YR 2016 UL http://www.jneurosci.org/content/36/12/3399.abstract AB 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.