Caracterização de Eletroencefalograma utilizando análise de quantificação da recorrência

Abstract

The application of techniques for processing, analyzing and extracting features of electroencephalographic signals collaborate in understanding brain activities. Based on this, we can also mention the creation of diagnostic and monitoring tools allowing the development of brain-computer interface devices. Among the diagnostic tools there are several mathematical methods that can be considered, such as, the spectral analysis of the signal using the Fast Fourier Transform (FFT). In this study we investigated the applicability of a non-linear method already known for the analysis of biomedical signals and its comparison with a conventional analysis method, the FFT. The recurrence quantification analysis (RQA) was used as a method to characterize the alpha rhythm of electroencephalogram signals during the Berger effect (eyes closed). Electroencephalogram signals were obtained from 60 study participants, of both sexes, were analyzed using the electrodes of the occipital region (O1 and O2). The experimental protocol involved two distinct moments: rest (eyes open) and activity (eyes closed). For the application of the non-linear method, the optimized value for the parameters (time delay, embedding dimension and threshold) were obtained for classification. After parameterization, 9 measurements were obtained: recurrence rate, determinism, average of diagonal line, Shannon entropy, laminarity, trapping time, clustering coefficient, transitivity and maximum size of vertical lines. The results found were compared with results of the FFT, which considered as the gold standard of analysis. This combination was performed by multivariate analysis of principal components analysis. The results suggested that the use of RQA is capable of detecting significant statistical differences between the moments studied and that some RQA features may contribute to the analysis provided by the FFT. Thus, the RQA method can be considered for the analysis of alpha rhythm during the Berger effect. For future work, other brain regions can be studied and the methodology extended to analyze other brain waves during the performance of more complex activities.