LAPACK Users' guide (third ed.)
LAPACK Users' guide (third ed.)
Independent component analysis: algorithms and applications
Neural Networks
Journal of Computational and Applied Mathematics
Computer Methods and Programs in Biomedicine
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A new approach to filter multi-channel signals is presented, called filtering by optimal projection (FOP) in this paper. This approach is based on common spatial subspace decomposition (CSSD) theory. Moreover, an evolution of this method for non-stationary signals is also introduced which is called adaptative FOP (AFOP). As ICA, a filtering matrix is set up in the best way to remove artifacts with linear combination of channels. This filtering matrix is characterized by two subspaces. The first one is determined during a learning phase, by finding components maximizing the ratio signal over noise. The second one will be determined during a filtering phase, by reconstructing signals of a sliding window, by a least square method. These methods are completely automated and enable to filter independently numerous artifact types. Moreover, this filtering can be improved by applying this process on frequency band decomposed signals. Various tests have been made on electroencephalogram (EEG) signals in order to remove ocular and muscular activity while conserving pathological activity (slow waves, paroxysms). The results are compared with ICA filtering and medical inspection has been carried out to prove that this approach yields very good performance.