  Averaging amplitudes over consecutive time samples within a time-window is
widely used to calculate the amplitude of an event-related potential (ERP) for
cognitive neuroscience. Objective determination of the time-window is critical
for determining the ERP component. Clustering on the spatio-temporal ERP data
can obtain the time-window in which the consecutive time samples topographies
are expected to be highly similar in practice. However, there exists a
challenging problem of determining an optimal number of clusters. Here, we
develop a novel methodology to obtain the optimal number of clusters using
consensus clustering on the spatio-temporal ERP data. Various clustering
methods, namely, K-means, hierarchical clustering, fuzzy C-means,
self-organizing map, and diffusion maps spectral clustering are combined in an
ensemble clustering manner to find the most reliable clusters. When a range of
numbers of clusters is applied on the spatio-temporal ERP dataset, the optimal
number of clusters should correspond to the cluster of interest within which
the average of correlation coefficients between topographies of every two-time
sample in the time-window is the maximum for an ERP of interest. In our method,
we consider fewer cluster maps for analyzing an optimal number of clusters for
isolating the components of interest in the spatio-temporal ERP. The
statistical comparison demonstrates that the present method outperforms other
conventional approaches. This finding would be practically useful for
discovering the optimal clustering in spatio-temporal ERP, especially when the
cognitive knowledge about time-window is not clearly defined.
