Emg Onset Detection Using the Maximum Likelihood Method

INTRODUCTION Electromyography (EMG) is used extensively to determine the muscle activation patterns of neuromuscular functions such as motor control, posture, and movement [1,2]. The onset of the EMG activity is a marker for the onset of active control and therefore is one of the most common parameters evaluated from EMG records [3], but there is no standard method to determine this parameter [4]. The accurate detection of the onset of muscle activity is extremely important since differences in the time from stimulus to EMG onset can be as low as 20 ms [5,6]. Computerized techniques for the determination of the onset of muscle activity exist but their performance varies considerably. Also the accuracy of these methods degrades as the signal to noise ratio is decreased. In this study we have developed an algorithm to detect the onset of muscle activity from EMG records using the Maximum Likelihood Method. The performance of this method was compared against DiFabio’s threshold method [7], and against two experienced human observers in a wide range of standard deviation ratios (SDR) of the samples. The SDR is a measure of the intensity of the signal.