2 research outputs found
Correntropy Based Robust Decomposition of Neuromodulations
Neuromodulations as observed in the extracellular electrical potential
recordings obtained from Electroencephalograms (EEG) manifest as organized,
transient patterns that differ statistically from their featureless noisy
background. Leveraging on this statistical dissimilarity, we propose a
noniterative robust classification algorithm to isolate, in time, these
neuromodulations from the temporally disorganized but structured background
activity while simultaneously incorporating temporal sparsity of the events.
Specifically, we exploit the ability of correntropy to asses higher - order
moments as well as imply the degree of similarity between two random variables
in the joint space regulated by the kernel bandwidth. We test our algorithm on
DREAMS Sleep Spindle Database and further elaborate on the hyperparameters
introduced. Finally, we compare the performance of the algorithm with two
algorithms designed on similar ideas; one of which is a quick, simple norm
based technique while the other parallels the state-of-the-art Robust Principal
Component Analysis (RPCA) to achieve classification. The algorithm is able to
match the performance of the state-of-the-art techniques while saving
tremendously on computation time and complexity.Comment: 4 pages, Engineering in Medicine and Biolog
Local power estimation of neuromodulations using point process modeling
Extracellular electrical potentials (EEP) recorded from the brain are an
active manifestation of all cellular processes that propagate within a volume
of brain tissue. A standard approach for their quantification are power
spectral analyses methods that reflect the global distribution of signal power
over frequency. However, these methods incorporate analysis windows to achieve
locality and therefore, are limited by the inherent trade - off between time
and frequency resolutions. In this paper, we present a novel approach to
estimate local power more precisely at a resolution as high as the sampling
frequency. Our methods are well grounded on established neurophysiology of the
bio-signals where we model EEPs as comprising of two components:
neuromodulations and background activity. A local measure of power, we call
Marked Point Process (MPP) spectrogram, is then derived as a power - weighted
intensity function of the point process for neuromodulations. We demonstrate
our results on two datasets: 1) local field potentials recorded from the
prefrontal cortex of 3 rats performing a working memory task and 2) EEPs
recorded via electroencephalography from the visual cortex of human subjects
performing a conditioned stimulus task. A detailed analysis of the power -
specific marked features of neuromodulations confirm high correlation between
power spectral density and power in neuromodulations establishing the aptness
of MPP spectrogram as a finer measure of power where it is able to track local
variations in power while preserving the global structure of signal power
distribution.Comment: 6 page