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Updated: May 23, 2026

EEG Mu Rhythm in Typical and Atypical Development
Published on: April 9, 2014
Mu suppression as an index of sensorimotor contributions to speech processing: evidence from continuous EEG signals
Megan Cuellar1, Andrew Bowers, Ashley W Harkrider
1Department of Audiology and Speech Pathology, University of Tennessee College of Allied Health Sciences, Knoxville TN 37996, United States. mmorris5@uthsc.edu
Abstract:
Mu rhythm suppression is an index of sensorimotor activity during the processing of sensory stimuli. Two present studies investigate the extent to which this measure is sensitive to differences in acoustic processing. In both studies, participants were required to listen to 90second acoustic stimuli clips with their eyes closed and identify predetermined targets. Experimental conditions were designed to vary the acoustic processing demands. Mu suppression was measured continuously across central electrodes (C3, Cz, and C4). Ten adult females participated in the first study in which the target was a pseudoword presented in three conditions (identification, discrimination, discrimination in noise). Mu suppression was strongest and reached significance relative to baseline only in the discrimination in noise task at C3 (indicative of left hemisphere sensorimotor activity) when measured in a 10-12Hz bandwidth. Thirteen adult females participated in the second study, which measured mu suppression to acoustic stimuli with 'segmentation' (i.e., separating a parsed stimulus into individual components) versus non-segmentation requirements in both speech and tone discrimination conditions. Significantly greater overall suppression to speech relative to tone tasks was found in the 10-12Hz bandwidth. Further, suppression relative to baseline was significant only at C3 during the speech discrimination with segmentation task. Taken together, findings indicate that mu rhythm suppression in acoustic processing is sensitive to dorsal stream processing. More specifically, it is sensitive to (1) increases in overall processing demands and (2) processing linguistic versus non-linguistic information.

