The electromyograph (EMG) signal generated from a cat's jaw muscle in movement, is analyzed. After a series of experiments at UMDNJ, three major categories of EMG were recorded: rest EMG, biting EMG and hissing EMG.

The biting files contained a series of EMG collected in successive episodes. Power spectrum and statistical analysis were applied to the episodes. Each episode had approximately 43,000 samples. Results showed that these episodes had a similar spectrum with statistically the same mean and variance.

The hissing files were produced by electrical stimulation of the hypothalamus and contained EMG embedded in a stimulus artifact. The problem was to filter the stimulus out of the signal that contained hissing EMG for analysis. Unfortunately, the spectrum of the stimulus is within the bandwidth of the hissing EMG. Therefore we could not apply classical digital filtering techniques, and we had rather use adaptive filtering techniques. A suitable algorithm is the LMS algorithm because it is powerful and has a wide range of use. Moreover it is easy to use, and if used properly, it can give good results.

A conclusion was drawn that the response due to hissing is statistically different from the one due to biting. On the other hand, the spectra of the biting episodes are similar to each other.