Abstract
Brain Computer Interface is a new technology aimed to communicate the user’s intentions without using nerves or muscles. To obtain this objective, BCI devices make use of classifiers which translate inputs from the user’s brain signals into commands for external devices. This paper describes an adaptive bi-stage classifier based on RBF neural networks and Hidden Markov Models. The first stage analyses the user’s electroencephalografic input signal and provides sequences of pre-assignations to the second stage. The segment of EEG signal is assigned to the HMM with the highest probability of generating the pre-assignation sequence.
The algorithm is tested with real samples of electroencephalografic signal, from five healthy volunteers using the cross-validation method. The results allow to conclude that it is possible to implement this algorithm in an on-line BCI device, but a huge dependency in the percentage of the correct classification from the user and the setup parameters has been detected.
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Pérez, J.L.M., Cruz, A.B. (2011). Adaptive RBF-HMM Bi-Stage Classifier Applied to Brain Computer Interface. In: Fred, A., Filipe, J., Gamboa, H. (eds) Biomedical Engineering Systems and Technologies. BIOSTEC 2010. Communications in Computer and Information Science, vol 127. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18472-7_12
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DOI: https://doi.org/10.1007/978-3-642-18472-7_12
Publisher Name: Springer, Berlin, Heidelberg
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