ICA 2007

ICA 2007
7th International Conference on
Independent Component Analysis
and Signal Separation

London, UK        9 - 12 September 2007

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Paper No: 135

Detection of Paroxysmal EEG Discharges using Multitaper Blind Signal Source Separation

Author(s): Jonathan Halford

Abstract

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'.

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