Paper No: 135
Detection of Paroxysmal EEG Discharges using Multitaper Blind Signal Source Separation
Author(s): Jonathan Halford
The routine electroencephalogram (EEG) is a useful diagnostic test for neurologists.
But this test is frequently misinterpreted by neurologists due to a lack
of systematic understanding of paroxysmal electroencephalographic events
(PEDs), one of the most important features of EEG. A heuristic algorithm
is described which uses conventional blind signal source separation (BSSS)
algorithms to collect all of the PEDs in a routine EEG recording. This algorithm
treats BSSS as a `black box' and applies it in a computationally-intensive
multitaper algorithm in order to detect PEDs without a pre-specification
of signal morphology or scalp distribution. The algorithm also attempts to
overcome some of the limitations of conventional BSSS as applied to the study
of neurophysiology datasets, specifically the 'over-completeness problem'
and the 'non-stationarity problem'.