title: Identification of independent biological sensors-electromyogram example.
abstract: To ensure that no biological event that may be important is missed, redundancy of sensors is provided. While this is useful, there are shortcomings when there is need to separate the signals from different sources using blind source separation techniques. An example of such a situation is over-complete surface electromyogram (sEMG) recording. Techniques such as principal component analysis (PCA) and entropy measures are used to identify the suitable channels. The shortcomings in these are the need for prior estimation of the number of channels. This paper has used the determinant of the global matrix of the mixtures to determine the number of independent sources in a mixture. The results indicate that the technique is able to distinguish between dependent and independent channels and this may be applied for determining the number of independent sources. The applications of this include data reduction by identifying redundant data, and for pre-processing of the data prior to use of any data classification techniques.
authors.affiliation: RMIT University Melbourne, GPO BOX 2476 V, Melbourne 3001, Australia. ganesh.naik@rmit.edu.au
authors.forename: Ganesh R
authors.initials: GR
authors.lastname: Naik
authors.forename: Dinesh K
authors.initials: DK
authors.lastname: Kumar
authors.forename: Marimuthu
authors.initials: M
authors.lastname: Palaniswami
mesh_terms: D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004566:Electrodes; D004576:Electromyography / Q000295:instrumentation* / Q000379:methods*; D006801:Humans; D009119:Muscle Contraction / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology*; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014159:Transducers