Symbolic Analysis Based Pipeline for EEG Data

This paper is concerned with developing an analysis tool for electroencephalogram (EEG) data. It explores the possibility of using the self-organizing map algorithm as starting point in a symbolic analysis based approach, with the aim of reducing the challenges brought by the high dimensionality and complexity of EEG data. The solution represents a pipeline integrating different steps for accomplishing an in-depth analysis: self-organizing map training, clustering, color sequence generation, pattern specificity index, pattern triggered average and peristimulus time histogram computation.