Limitations of ICA for Artefact Removal.

This paper reports analysis of the limitations of using Independent Component Analysis (ICA) for biosignal analysis especially artefact removal. The possible difficulty is that there are limited number of electrodes (recordings) making it an overcomplete problem (non-square ICA). The other difficulty is the distribution of biosignal being close to Gaussian.

Limitations and applications of ICA for surface electromyogram.

Surface electromyogram (SEMG) has numerous applications, but the presence of artefacts and noise, especially at low level of muscle activity make the recordings unreliable. Spectral and temporal overlap can make the removal of artefacts and noise, or separation of relevant signals from other bioelectric signals extremely difficult. Individual muscles may be considered as independent at the local level and this makes an argument for separating the signals using independent component analysis (ICA).

EEG coherence changes between right and left motor cortical areas during voluntary muscular contraction.

It is known that movements of the right side of the body are controlled by the left motor cortex of the brain. The aim of this study is to evaluate the contribution of right motor cortex of the brain in the central motor control of right-sided muscle contraction. EEG/EEG coherence analysis has been used to determine the functional coupling between the right and left motor cortical areas in twenty normal volunteers, during maximum voluntary contraction (MVC) and 50% MVC of right Adductor Pollicis muscle (APM).