  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.
