Surface Laplacian of Central Scalp Electrical Signals is Insensitive to Muscle Contamination

Abstract

The objective of this paper was to investigate the effects of surface Laplacian processing on gross and persistent electromyographic (EMG) contamination of electroencephalographic (EEG) signals in electrical scalp recordings. We made scalp recordings during passive and active tasks, on awake subjects in the absence and in the presence of complete neuromuscular blockade. Three scalp surface Laplacian estimators were compared to left ear and common average reference (CAR). Contamination was quantified by comparing power after paralysis (brain signal, B) with power before paralysis (brain plus muscle signal, B+M). Brain:Muscle (B:M) ratios for the methods were calculated using B and differences in power after paralysis to represent muscle (M). There were very small power differences after paralysis up to 600Hz using surface Laplacian transforms (B:M > 6 above 30Hz in central scalp leads). Scalp surface Laplacian transforms reduce muscle power in central and pericentral leads to less than one sixth of the brain signal, two to three times better signal detection than CAR. Scalp surface Laplacian transformations provide robust estimates for detecting high-frequency (gamma) activity, for assessing electrophysiological correlates of disease, and also for providing a measure of brain electrical activity for use as a standard in the development of brain/muscle signal separation methods.