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EEG signal analysis via a cleaning procedure based on multivariate empirical mode decomposition

EEG signal analysis via a cleaning procedure based on multivariate empirical mode decomposition
 
Gallego Jutglà, Esteve ; Rutkowski, E. ; Cichocki, Andrej ; Solé-Casals, Jordi
2012

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Abstract

Artifacts are present in most of the electroencephalography (EEG) recordings, making it difficult to interpret or analyze the data. In this paper a cleaning procedure based on a multivariate extension of empirical mode decomposition is used to improve the quality of the data. This is achieved by applying the cleaning method to ...»»»»
Artifacts are present in most of the electroencephalography (EEG) recordings, making it difficult to interpret or analyze the data. In this paper a cleaning procedure based on a multivariate extension of empirical mode decomposition is used to improve the quality of the data. This is achieved by applying the cleaning method to raw EEG data. Then, a synchrony measure is applied on the raw and the clean data in order to compare the improvement of the classification rate. Two classifiers are used, linear discriminant analysis and neural networks. For both cases, the classification rate is improved about 20%.^^^^

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Conference object

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(c) SciTePress - Science and Technology Publications
 
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Citation:

Gallego-Jutglà, E., Rutkowski, T. M., Cichocki, A., & Solé-Casals, J. (2012). EEG signal analysis via a cleaning procedure based on multivariate empirical mode decomposition. Paper presented at the IJCCI 2012 - Proceedings of the 4th International Joint Conference on Computational Intelligence, 670-676