Abstract
Recently Single Channel ICA has been proposed where it can be shown that the algorithms learn temporal filters for separating the different components. Here we consider the natural extension to learning a set of space-time separating filters. We argue that these are capable of separation above and beyond that possible using only spatial or temporal methods alone. We then consider the potential of these ideas when applied to letal Electroencephalographic (EEG) data and Brain Computer Interaction (BCI). © Springer-Verlag Berlin Heidelberg 2007.
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CITATION STYLE
Davies, M., James, C., & Wang, S. (2007). Space-time ICA and EM brain signals. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4666 LNCS, pp. 577–584). Springer Verlag. https://doi.org/10.1007/978-3-540-74494-8_72
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