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Singular spectrum analysis improves analysis of local field potentials from macaque V1 in active fixation task

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Abstract

Local field potentials (LFPs) represent the relatively slow varying components of the neural signal, and their analysis is instrumental in understanding normal brain function. To be properly analyzed, this signal needs to be separated in its fundamental frequency bands. Recent studies have shown that empirical mode decomposition (EMD) can be exploited to pre-process LFP recordings in order to achieve a proper separation. However, depending on the analyzed signal, EMD is known to generate components that may cover a too wide frequency range to be considered as narrow banded. As an alternative, we present here an improved version of the singular spectrum analysis (SSA) algorithm, validated by numerical simulations, and applied to LFP recordings in V1 of a macaque monkey exposed to simple visual stimuli. The components generated by the improved SSA algorithm are shown to be more meaningful than those generated by EMD, paving the way for its use in LFP analysis.
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Details

Original languageEnglish
Pages (from-to)2945-2948
JournalIEEE Engineering in Medicine and Biology Society
Volume2012
DOIs
Publication statusPublished - 1 Jan 2012
Event2012 Annual International Conference of the IEEE -
Duration: 28 Aug 20121 Sep 2012