High-Level Classification for EEG Analysis

High-level classification are supervised learning techniques able to consider topological and structural features of the input data. Several high-level techniques have been proposed in the last years with different strategies, such as the pattern conformation technique which represents the input data as a network and perform classification by analyzing the variation of complex network measures. Such techniques have contributed in several tasks, but their contribution to the classification of sequential patterns has not been investigated yet. In this paper, we propose a high-level technique based on the complex network measures assortativity and average shortest path length to the analysis of electroencephalogram (EEG) data. To be specific, we consider two formulation of the problem of prognosis of patients in coma: binary and multi-class. The problem is very difficult and challenging as the data contains patients from different etiologies. Experimental results with nine other techniques including state-of-the-art ones like convolutional neural networks revealed that our high-level approach has the potential to improve (statistically) the predictive performance of those techniques, especially when considering the results with the assortativity measure for both binary and multi-class formulations. Moreover, this study paves a way in the adoption of complex network measures besides the extraction of features from EEG records, but also for the classification itself.