抽象的な

Application of Independent Component Analysis

P.R.Gomathi , 2V.Jamuna

Electroencephalographic (EEG) recordings are often contaminated by artifacts, i.e., signals with non-cerebral origin that might mimic some cognitive or pathologic activity, this way affecting the clinical interpretation of traces.In this paper, Independent Component Analysis (ICA) is applied to EEG signals collected from different mental tasks in order to remove the artifacts from the EEG signals.There are several algorithms based on different approaches for ICA widely in use for all sort of applications. These algorithms include, but not limited to, the popular Fast- ICA, Joint Approximate Diagonalization of Eigenvalues (JADE), Infomax, and Extended Infomax etc. A framework for accommodating four ICA algorithms is developed to estimate the convergence speed of the algorithms and hence selects the best algorithm for the specific type of data.