Conference paper

Comparison of different feature classifiers for brain computer interfaces

Changes in EEG power spectra related to the imagination of movements may be used to build up a direct communication channel between the brain and computer (Brain Computer Interface; BCI). However, for the practical implementation of a BCI device, the feature classifier plays a crucial role. In this paper, we compared the performance of three different feature classifiers for the detection of the imagined movements in a group of 6 normal subject from the EEG. The feature classifiers compared were those based on the Hidden Markov Models (HMM), the Artificial Neural Networks (ANN), and on the Mahalanobis distance (MD). Results show a better performance of the MD and ANN classifiers with respect to the HMM classifier.

    Keywords: learning


    • EPFL-CONF-82987

    Record created on 2006-03-10, modified on 2017-05-10


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