Brain-Computer Interface Using Wavelet Transformation and Naïve Bayes Classifier

Conference paper

DOI: 10.1007/978-0-387-79100-5_8

Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 657)
Cite this paper as:
Bassani T., Nievola J.C. (2010) Brain-Computer Interface Using Wavelet Transformation and Naïve Bayes Classifier. In: Hussain A., Aleksander I., Smith L., Barros A., Chrisley R., Cutsuridis V. (eds) Brain Inspired Cognitive Systems 2008. Advances in Experimental Medicine and Biology, vol 657. Springer, New York, NY


The main purpose of this work is to establish an exploratory approach using electroencephalographic (EEG) signal, analyzing the patterns in the time-frequency plane. This work also aims to optimize the EEG signal analysis through the improvement of classifiers and, eventually, of the BCI performance. In this paper a novel exploratory approach for data mining of EEG signal based on continuous wavelet transformation (CWT) and wavelet coherence (WC) statistical analysis is introduced and applied. The CWT allows the representation of time-frequency patterns of the signal’s information content by WC qualiatative analysis. Results suggest that the proposed methodology is capable of identifying regions in time-frequency spectrum during the specified task of BCI. Furthermore, an example of a region is identified, and the patterns are classified using a Naïve Bayes Classifier (NBC). This innovative characteristic of the process justifies the feasibility of the proposed approach to other data mining applications. It can open new physiologic researches in this field and on non stationary time series analysis.


Pattern analysis Classification Signal processing Brain computer interface 

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Instituto Nacional de Inovação em Diagnósticos para a Saúde Pública - CPGEI–Programa de Pós-Graduação em Engenharia Elétrica e Informática IndustrialFlorianópolisBrazil
  2. 2.UTFPR - Universidade Tecnológica Federal do ParanáLondrinaBrazil
  3. with PUCPR at PPGIa (Programa de Pós-Graduação em Informática)He is a Full Professor at PUCPR and the Team Leader of the Knowledge Discovery and Machine Learning Research GroupPanamaPanama

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