Improving the SNR of EEG generated by deep sources with weighted multielectrode leads

We have developed a multielectrode lead technique to improve the signal-to-noise ratio (SNR) of scalp-recorded electroencephalography (EEG) signals generated deep in the brain. The basis of the method lies in optimization of the measurement sensitivity distribution of the multielectrode lead. We claim that two factors improve the SNR in a multielectrode lead: (1) the sensitivity distribution of a multielectrode lead is more specific in measuring signals generated deep in the brain and (2) spatial averaging of noise occurs when several electrodes are applied in the synthesis of a multielectrode lead. We showed theoretically that within a three-layer spherical head model the sensitivity distributions of multielectrode leads are more specific for deep sources than those of traditional bipolar leads. We also estimated with simulations and with preliminary measurements the total improvement in SNR achieved by both the more specific lead field and spatial averaging. Results obtained with simulations and with experimental measurements show an apparent improvement in SNR obtained with multielectrode leads. This encourages for future development of the method.