TY - JOUR
T1 - Singular spectrum analysis improves analysis of local field potentials from macaque V1 in active fixation task
AU - Bonizzi, P.
AU - Karel, J.
AU - de Weerd, P.
AU - Lowet, E.
AU - Roberts, M.
AU - Westra, R.
AU - Meste, O.
AU - Peeters, Ralf
PY - 2012/1/1
Y1 - 2012/1/1
N2 - 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.
AB - 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.
U2 - 10.1109/embc.2012.6346581
DO - 10.1109/embc.2012.6346581
M3 - Conference article in journal
SN - 1557-170X
VL - 2012
SP - 2945
EP - 2948
JO - IEEE Engineering in Medicine and Biology Society
JF - IEEE Engineering in Medicine and Biology Society
T2 - 2012 Annual International Conference of the IEEE
Y2 - 28 August 2012 through 1 September 2012
ER -