WISDoM: characterizing neurological timeseries with the Wishart distribution
This provides a method for analyzing neurological timeseries data, such as EEG and brain connectivity, which could aid in neurological studies and diagnosis, but it appears incremental as it builds on existing statistical distributions.
The authors introduced WISDoM, a framework for quantifying deviations of symmetric positive-definite matrices from the Wishart distribution, and applied it to feature ranking in EEG data and classification of autistic subjects in the ABIDE study, achieving results in these tasks.
WISDoM (Wishart Distributed Matrices) is a new framework for the quantification of deviation of symmetric positive-definite matrices associated to experimental samples, like covariance or correlation matrices, from expected ones governed by the Wishart distribution WISDoM can be applied to tasks of supervised learning, like classification, in particular when such matrices are generated by data of different dimensionality (e.g. time series with same number of variables but different time sampling). We show the application of the method in two different scenarios. The first is the ranking of features associated to electro encephalogram (EEG) data with a time series design, providing a theoretically sound approach for this type of studies. The second is the classification of autistic subjects of the ABIDE study, using brain connectivity measurements.