Enhanced real-time cursor control algorithm, based on the spectral analysis of electromyograms

This paper presents a new version of an EMG-based, hands-free, cursor control system, and compares its performance to that of a previous version. Both systems use classification algorithms that rely on the periodogram estimation of the power spectral density (PSD) of electromyogram (EMG) signals from muscles in the face. The older system requires three electrodes for EMG input, and utilizes an algorithm that calculates partial power accumulations over the frequency ranges of 0Hz - 145Hz and 145Hz - 600Hz in the PSDs of the EMG signals. The new system requires four electrodes for EMG input, and utilizes an algorithm that calculates mean power frequency (MPF) values to assist in distinguishing the cranial muscle that contracted. An experiment was devised to gauge the point-and-click capabilities of both systems. The experimental results were evaluated using Fitts' Law analysis. The results show that the new algorithm provides improved point-and-click performance over the old algorithm.