When experts analyze EEGs they look for landmarks in the traces corresponding to established waveform patterns, such as phasic events of particular frequency or morphology. This modeling approach automatically learns the waveforms corresponding to transient, reoccurring events within EEG traces.
The methodology is based on a sparsely excited model of a single EEG trace, and the model parameters are estimated using shift-invariant dictionary learning algorithms developed in the signal processing community. On the motor imagery dataset, linear discriminant analysis can distinguish the type of motor imagery based on the spatial patterns of a subset of the learned waveforms.
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