Generative Topographic Mapping of electroencephalography (EEG) data
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Generative Topographic Mapping (GTM) assumes that the features of high dimensional data can be described by a few variables (usually 1 or 2). Based on this assumption, the GTM trains unsupervised on the high dimensional data to find these variables from which the features can be generated. The variables can be used to represent and visualize the original data on a low dimensional space. Here, we have applied the GTM algorithm on Electroencephalography (EEG) signals in order to find a two dimensional representation for them. The 2-D representation can also be used to classify the EEG signals ...