Extracting electromyographic signals from multi-channel local-field-potentials using independent component analysis without direct muscular recording

Electromyography (EMG) signals have been used to identify animal’s behavioral states including sleep/wake states and freezing behavior. However, EMG recording is often not conducted together with in vivo electrophysiology recording because it requires additional surgeries and setups. While independent component analysis (ICA) has been used as a well-established method to reduce noise from field potential data, there has been no attempt to proactively use the removed “noise”, of which EMG signals are considered to be one of the major sources. Here, we demonstrate that EMG signals can be reconstructed from local field potential data using ICA without direct EMG recording. The extracted component is highly correlated with directly measured EMG signals. This component is further demonstrated to be useful for measuring an animal’s sleep/wake states and freezing behaviors consistently with actual EMG. The EMG component can be also separated by four-channel LFP data from multisite brain regions. Our method will reduce the necessary effort to measure EMGs in wide-ranged in vivo electrophysiology experiments including the investigation of neural correlates of animal behavior.