  Kohonen's Adaptive Subspace Self-Organizing Map (ASSOM) learns several
subspaces of the data where each subspace represents some invariant
characteristics of the data. To deal with the imbalance classification problem,
earlier we have proposed a method for oversampling the minority class using
Kohonen's ASSOM. This investigation extends that study, clarifies some issues
related to our earlier work, provides the algorithm for generation of the
oversamples, applies the method on several benchmark data sets, and makes
application to three Brain Computer Interface (BCI) applications. First we
compare the performance of our method using some benchmark data sets with
several state-of-the-art methods. Finally, we apply the ASSOM-based technique
to analyze the three BCI based applications using electroencephalogram (EEG)
datasets. These tasks are classification of motor imagery , drivers' fatigue
states, and phases of migraine. Our results demonstrate the effectiveness of
the ASSOM-based meth od in dealing with imbalance classification problem.
