A comparative study of automatic techniques for ocular artifact reduction in spontaneous EEG signals based on clinical target variables: A simulation case

Abstract

Eye movement artifacts represent a critical issue for quantitative electroencephalography (EEG) analysis and a number of mathematical approaches have been proposed to reduce their contribution in EEG recordings. The aim of this paper was to objectively and quantitatively evaluate the performance of ocular filtering methods with respect to spectral target variables widely used in clinical and functional EEG studies. In particular the following methods were applied: regression analysis and some blind source separation (BSS) techniques based on second-order statistics (PCA, AMUSE and SOBI) and on higher-order statistics (JADE, INFOMAX and FASTICA). Considering blind source decomposition methods, a completely automatic procedure of BSS based on logical rules related to spectral and topographical information was proposed in order to identify the components related to ocular interference. The automatic procedure was applied in different montages of simulated EEG and electrooculography (EOG) recordings: a full montage with 19 EEG and 2 EOG channels, a reduced one with only 6 EEG leads and a third one where EOG channels were not available. Time and frequency results in all of them indicated that AMUSE and SOBI algorithms preserved and recovered more brain activity than the other methods mainly at anterior regions. In the case of full montage: (i) errors were lower than 5% for all spectral variables at anterior sites; and (ii) the highest improvement in the signal-to-artifact (SAR) ratio was obtained up to 40 dB at these anterior sites. Finally, we concluded that second-order BSS-based algorithms (AMUSE and SOBI) provided an effective technique for eye movement removal even when EOG recordings were not available or when data length was short. ᭧

Ocular Reduction in EEG Signals Based on Adaptive Filtering, Regression and Blind Source Separation

“…There have been few empirical studies measuring the effectiveness of ocular reduction techniques on simulated data composed by mixtures of cerebral and ocular activities. 19,32 However, these studies showed opposite conclusions: regression and PCA based algorithms were suggested in Wallstrom et al, 32 adaptive filtering was proposed in He et al, 12 and BSS techniques based on second-order statistics were recommended in Kierkels et al 19 and Romero et al 25 in spite of adaptive filtering was not evaluated in both. Moreover, in spite of the theoretical advantages of BSS based approaches, a very recent study based on real data and using expert scorers for identifying ocular artifacts concluded that regression procedure could significantly reduce ocular artifacts better than ICA when few EEG channels were available.…”

“…The selection criterion was that no samples from VEOG and HEOG signals exceeded 25 lV. This threshold was lower than 40 lV, which was used in a previous study, 25 in order to better consider a reduced potential ocular activity in EEG data. Neural sources were obtained by high-pass filtering the EEG channels with a cut-off frequency of 0.5 Hz in order to reduce very low frequency components which could be possibly more related to ocular activity.…”

“…In the present study, simulated EEG and EOG data were obtained by more general linear mixtures whose coefficients were calculated by means of multi-input single-output (MISO) linear models applied to a database composed of real EEG and EOG recordings. A more restrictive condition than used in Romero et al 25 was applied in order to consider EEG recordings more free of artifacts. Bidirectional contamination, where EOG recordings are also interfered by some cerebral activity, was also taken into account by linear mixtures in both directions.…”

“…In this study, a fully automatic procedure for ocular correction from spontaneous EEG signals based on BSS described in Romero et al 25 was used. The objective of the present study was twofold.…”

“…In He et al 13 an arbitrary gain function for ocular propagation was used to obtain simulated EEG and EOG signals. This function was frequency dependent and constructed based on Gasser et al 10 In Romero et al, 25 simulated data was generated from spontaneous EOG and EEG signals with closed eyes and with normal (not fast forced) eye movements, which is the common procedure used in clinical routine. The simplest linear instantaneous mixtures were carried out by factors from regression coefficients and by using EEG recordings considered free of ocular artifacts with a not very restrictive condition.…”