Abstract
The starting point of this paper is the analysis of the reference problem in intra-cerebral electroencephalographic (iEEG) recordings. It is well accepted that both surface and depth EEG signals are always recorded with respect to some unknown time-varying signal called reference. This article discusses different methods for determining and reducing the influence of the reference signal for the iEEG signals. In particular, we derive optimal approaches for the estimation of the reference signal in iEEG recording setups and demonstrate their relation to the well-known minimum power/variance distortionless response approaches derived for general array and antenna signal processing applications. We show that the proposed approaches achieve optimal performance in terms of estimation error and that they outperform other reference identification methods proposed in the literature. The developed algorithms are illustrated on simulated examples and on real iEEG signals.
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Notes
Average or Laplacian references are never used for depth EEG signals. In fact, electrode placement is not symmetric, so there is no reason to suppose that signals should average to 0 as in scalp recordings.
Hu et al. [7] proposed an alternative method replacing the FastICA step by simple principal component analysis, thus not imposing statistical independence but only decorrelation.
Another ambiguity relates to the order of the sources, which is also impossible to recover.
The BSS estimate \(\hat{r}_{\rm BSS}\) was selected, among all sources found by BSS, as the signal having the highest absolute value of the correlation coefficient with the original signal \(r.\)
Still, other BSS algorithms, based on second-order statistics only (SOBI for example) will function also for Gaussian sources.
More specific studies using connectivity measures will be described elsewhere.
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Acknowledgments
Nilesh Madhu has been supported under the framework of ITN-AUDIS, Digital Signal Processing in Audiology (http://www.audis-itn.eu), funded by the European Union under the Framework 7 People Marie-Curie Programme. This research was partially funded by the Neuro-IC programm of CNRS, France. The authors would like to thank Dr. S. Colnat-Coulbois for her neurosurgical contribution.
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Madhu, N., Ranta, R., Maillard, L. et al. A unified treatment of the reference estimation problem in depth EEG recordings. Med Biol Eng Comput 50, 1003–1015 (2012). https://doi.org/10.1007/s11517-012-0946-0
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DOI: https://doi.org/10.1007/s11517-012-0946-0