Reference Signal Impact on EEG Energy

Authors:
Sanqing Hu;Matt Stead;Hualou Liang;Gregory A. Worrell
Affiliations:
Department of Neurology, Division of Epilepsy and Electroencephalography, Mayo Clinic, Rochester, USA MN 55905 and School of Biomedical Engineering, Drexel University, Philadelphia, USA PA 19104;Department of Neurology, Division of Epilepsy and Electroencephalography, Mayo Clinic, Rochester, USA MN 55905;School of Biomedical Engineering, Drexel University, Philadelphia, USA PA 19104;Department of Neurology, Division of Epilepsy and Electroencephalography, Mayo Clinic, Rochester, USA MN 55905
Venue:
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Year:
2009

Citing 2
Cited 0

Independent component analysis: algorithms and applications

Neural Networks
Combining Energy and Wavelet Transform for Epileptic Seizure Prediction in an Advanced Computational System

BMEI '08 Proceedings of the 2008 International Conference on BioMedical Engineering and Informatics - Volume 02

Quantified Score

Hi-index	0.00

Visualization

Abstract

A reference is required to record electroencephalography (EEG) signals, and therefore the reference signal can effect any quantitative EEG analysis. In this study, we investigate the impact of reference signal amplitude on a commonly used quantitative measure of the EEG, the signal energy. We show that: (i) when the reference signal and the non-referential signal have negative correlation, the energy of the referential signal will monotonically increase as the amplitude of the reference signal increases from 0 to ***. (ii) When the reference signal and the non-referential signal have positive correlation, energy of the referential signal first decreases to some nonnegative value and then increases as the amplitude of the reference signal increases from 0 to ***. In general, the reference signal may decrease or increase energy values. But a reference signal with higher relative amplitude will surely increase energy values. In [1], we developed a method to identify and extract the reference signal contribution to EEG recordings. Here we apply this approach to referential EEG recorded from human subjects and directly investigate the contribution of recording reference on energy and show that the reference signal may have a significant effect on energy values.