Uma abordagem híbrida para análise de similaridade e classificação de dados de eletroencefalografia.

Abstract

Electroencephalography (EEG) data has been applied to various contexts, such as health and human-computer interaction. EEG allows the production of data by monitoring the electrophysiological activity of individuals in reaction to experiments or by recording their physiological state. These data can be labeled for categorization and efficient retrieval. However, the labeling process may be subject to inaccuracies and noise, often caused by variations in EEG equipment configuration and divergent subjective opinions between individuals. In this context, there is a need for mechanisms that contribute to the understanding of the relationships between the data and the labels assigned to them. Given the above, this research aims to design an approach to measure similarity between classes of neuronal activation patterns, with application in the analysis of influencing factors and classification of EEG data. The approach was outlined through bibliographic review, construction of tools and acquisition of databases. Experimental evaluations were guided by questions and hypothesis tests that involved semi-supervised learning, statistical analysis of the influence of factors on response variable distributions, and parameter optimization via genetic algorithms. As a result, the problem of similarity analysis between classes of activation patterns in EEG data was formalized and an original approach was elaborated. The approach has the following steps: data acquisition, preprocessing, sample selection, cluster quantity calculation, input parameters, semi-supervised analysis, and similarity analysis or classification of neuronal activation patterns. Hypothesis tests indicated the applicability and efficiency of the approach regarding the level of similarity of neuronal activation patterns between classes and the classification accuracy rate when compared to other classifiers (neural networks and support vector machine).