Learning Recurrent Waveforms within EEGs
Austin J. Brockmeier and Jose C. Principe, IEEE Trans. on Biomedical Engineering, Vol. 63, No. 1, pp. 43-54, 2016.
When experts analyze EEGs they look for landmarks in the traces corresponding to established patterns such as oscillatory and phasic events of particular frequency or morphology. Long records motivate automated analysis techniques. Automation techniques often require design choices such as wavelet family or number of bandpass filters. To overcome this, we explore a modeling approach that automatically learns recurrent temporal waveforms within EEG traces. The estimation is based on a multiple-input, single-output linear model with sparsely excited inputs.
Algorithms and demos (zip)
This archive contains all of the underlying algorithms and three examples scripts to demonstrate the methodology. Please contact the first author at firstname.lastname@example.org if you have troubles or questions.
Shift-invariant waveform clustering algorithm shiftInvariantKmeans.m
that can be used to find centroids of waveforms learned across channels and/or subjects. See the demo above.