Multi-dimensional space-time-frequency component analysis of event related EEG data using closed-form PARAFAC

Authors:
Martin Weis;Florian Romer;Martin Haardt;Dunja Jannek;Peter Husar
Affiliations:
Ilmenau University of Technology, Communications Research Laboratory, D-98684, Germany;Ilmenau University of Technology, Communications Research Laboratory, D-98684, Germany;Ilmenau University of Technology, Communications Research Laboratory, D-98684, Germany;Ilmenau University of Technology, Biosignal Processing Group, D-98684, Germany;Ilmenau University of Technology, Biosignal Processing Group, D-98684, Germany
Venue:
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Year:
2009

Citing 0
Cited 4

Multi-way space-time-wave-vector analysis for EEG source separation

Signal Processing
Non-homogeneous spatial filter optimization for ElectroEncephaloGram (EEG)-based motor imagery classification

Neurocomputing
A semi-algebraic framework for approximate CP decompositions via simultaneous matrix diagonalizations (SECSI)

Signal Processing
Multidimensional prewhitening for enhanced signal reconstruction and parameter estimation in colored noise with Kronecker correlation structure

Signal Processing

Quantified Score

Hi-index	0.01

Visualization

Abstract

The efficient analysis of electroencephalographic (EEG) data is a long standing problem in neuroscience, which has regained new interest due to the possibilities of multidimensional signal processing. We analyze event related multi-channel EEG recordings on the basis of the time-varying spectrum for each channel. It is a common approach to use wavelet transformations for the time-frequency analysis (TFA) of the data. To identify the signal components we decompose the data into time-frequency-space atoms using Parallel Factor (PARAFAC) analysis. In this paper we show that a TFA based on the Wigner-Ville distribution together with the recently developed closed-form PARAFAC algorithm enhance the separability of the signal components. This renders it an attractive approach for processing EEG data. Additionally, we introduce the new concept of component amplitudes, which resolve the scaling ambiguity in the PARAFAC model and can be used to judge the relevance of the individual components.