Addressing source separation and identification issues in surface EMG using blind source separation.
Source separation and identification is one of the challenging areas in the bio signal processing. The processing of Electromyographic (EMG) signals can be viewed as the identification and separation of a series of overlapping sources of muscle activity with slowly varying source distribution and/or levels of activity. Blind source separation (BSS) techniques such as independent component analysis (ICA) lend themselves well to the analysis of such problems. The problem, however, still remains largely ill-posed even through the use of powerful assumptions such as those posed in ICA and other such techniques. It is generally the case in EMG signals that a certain level of a priori knowledge is available on the spatio-temporal and/or frequency distribution of the activities of interest, based on neurophysiological expectations. Here we describe limitations and applications of BSS on surface EMG. The problems we consider include the analysis of facial sEMG recordings during vowel utterance and analysis of hand EMG during finger and wrist movements.
Naik
Ganesh R
GR
RMIT University Melbourne, GPO BOX 2476 V, Melbourne 3001, Australia. ganesh.naik@rmit.edu.au
Kumar
Dinesh K
DK
Palaniswami
Marimuthu
M
D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D001185:Artificial Intelligence; D004576:Electromyography / Q000379:methods*; D006801:Humans; D009119:Muscle Contraction / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity