EMG AMPLITUDE ESTIMATORS BASED ON PROBABILITY DISTRIBUTION FOR MUSCLE–COMPUTER INTERFACE

• Published in: Fluctuation and noise letters Vol. 12; no. 3; pp. 1350016 - 1-1350016-18
• Main Authors: PHINYOMARK, ANGKOON, QUAINE, FRANCK, LAURILLAU, YANN, THONGPANJA, SIRINEE, LIMSAKUL, CHUSAK, PHUKPATTARANONT, PORNCHAI
• Format: Journal Article
• Language: English
• Published: Singapore World Scientific Publishing Company 01.09.2013; World Scientific Publishing Co. Pte., Ltd; World Scientific Publishing
• Subjects: Amplitudes; Biomechanics; Computing time; Density; Electromyography; Engineering Sciences; Estimators; Fluctuation; Forearm; Gaussian distribution; Mechanics; Muscles; Physics; Probability density functions; Signal to noise ratio; Variations; Wavelet transforms; Wrist; signal-to-noise ratio (SNR); fluctuating signal; probability density function (PDF); Electromyography (EMG); feature extraction
• ISSN: 0219-4775; 1793-6780
• DOI: 10.1142/S0219477513500168

To develop an advanced muscle–computer interface (MCI) based on surface electromyography (EMG) signal, the amplitude estimations of muscle activities, i.e., root mean square (RMS) and mean absolute value (MAV) are widely used as a convenient and accurate input for a recognition system. Their classification performance is comparable to advanced and high computational time-scale methods, i.e., the wavelet transform. However, the signal-to-noise-ratio (SNR) performance of RMS and MAV depends on a probability density function (PDF) of EMG signals, i.e., Gaussian or Laplacian. The PDF of upper-limb motions associated with EMG signals is still not clear, especially for dynamic muscle contraction. In this paper, the EMG PDF is investigated based on surface EMG recorded during finger, hand, wrist and forearm motions. The results show that on average the experimental EMG PDF is closer to a Laplacian density, particularly for male subject and flexor muscle. For the amplitude estimation, MAV has a higher SNR, defined as the mean feature divided by its fluctuation, than RMS. Due to a same discrimination of RMS and MAV in feature space, MAV is recommended to be used as a suitable EMG amplitude estimator for EMG-based MCIs.