Unsupervised clustering of evoked potentials by waveform

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Abstract
A procedure for clustering evoked potentials (EPs) according to their waveforms is presented. Clustering is performed without a priori selection of basis waveforms, the number of basis waveforms or the number of clusters. The method uses the principal-component-analysis coefficients of EP records as features for unsupervised optimal fuzzy clustering (UOFC) of the records. The validity of the procedure is demonstrated in two instances: visual evoked potentials (VEPs) and cognitive event-related potentials (ERPs) from humans in a memory-scanning task. In the clustering of VEPs, the procedure differentiates between waveforms judged to be clinically normal and abnormal. In the clustering of ERPs, the procedure correctly differentiates between waveforms evoked by the same stimuli which differ in their context to the performance of a memory-scanning task (memorised items against probes). Within this classification, the procedure detects two subgroups to probeevoked waveforms, which are not obvious from visual inspection of the waveforms. The advantage of the procedure, which conducts clustering by UOFC, is the adaptive and machine-learning nature of its operation.