Abstract
In this paper, we tested the efficiency of a two-step blind source separation (BSS) approach for the extraction of independent sources of α-activity from ongoing electroencephalograms (EEG). The method starts with a denoising source separation (DSS) of the recordings, and is followed by either an independent component analysis (ICA) or a temporal decorrelation algorithm (FastICA and TDSEP, respectively). This two-step method was compared with DSS, ICA and TDSEP alone. The tests were performed with simulated data based on real EEG signal, to guarantee the existence of a “ground truth”. The most efficient algorithm, for proper component extraction (regardless of the amount of α-activity in their spectra) is a combination of DSS and ICA. It provided also more stable results than ICA alone. TDSEP, in combination with DSS, was efficient only for the extraction of the components with prominent α-activity.
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Borisov, S., Ilin, A., Vigário, R., Oja, E. (2006). Comparison of BSS Methods for the Detection of α-Activity Components in EEG. In: Rosca, J., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2006. Lecture Notes in Computer Science, vol 3889. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11679363_54
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DOI: https://doi.org/10.1007/11679363_54
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