Variation in EMG activity: A hierarchical approach

Recordings of naturally occurring Electromyographic (EMG) signals are variable. One of the first formal and successful attempts to quantify variation in EMG signals was Shaffer and Lauder's (1985) study examining several levels of variation but not within muscle. The goal of the current study was to quantify the variation that exists at different levels, using more detailed measures of EMG activity than did Shaffer and Lauder (1985). The importance of accounting for different levels of variation in an EMG study is both biological and statistical. Signal variation within the same muscle for a stereotyped action suggests that each recording represents a sample drawn from a pool of a large number of motor units that, while biologically functioning in an integrated fashion, showed statistical variation. Different levels of variation for different muscles could be related to different functions or different tasks of those muscles. The statistical impact of unaccounted or inappropriately analyzed variation can lead to false rejection (type I error) or false acceptance (type II error) of the null hypothesis. Type II errors occur because such variation will accrue to the error, reducing power, and producing an artificially low F-value. Type I errors are associated with pseudoreplication, in which the replicated units are not truly independent, thereby leading to inflated degrees of freedom, and an underestimate of the error mean square. To address these problems, we used a repeated measures, nested multifactor model to measure the relative contribution of different hierarchical levels of variation to the total variation in EMG signals during swallowing. We found that variation at all levels, among electrodes in the same muscle, in sequences of the same animal, and among individuals and between differently named muscles, was significant. These findings suggest that a single intramuscular electrode, recording from a limited sample of the motor units, cannot be relied upon to characterize the activity of an entire muscle. Furthermore, the use of both a repeated-measures model, to avoid pseudoreplication, and a nested model, to account for variation, is critical for a correct testing of biological hypotheses about differences in EMG signals.