Abstract
A Brain-Computer Interface (BCI) enables the user to control a computer by brain activity only. In this paper we investigated the use of different brain connectivity methods to control a Magnetoencephalography (MEG)-based Brain-Computer Interface (BCI). We compared the use of coherence, phase synchronisation and a widely used method for spectral power estimation and found coherence to be a more robust feature extraction method, when using the BCI over a longer time interval across sessions. To validate these results we implemented an online BCI system using coherence and could show that coherence also performed more robust in an online setting than traditional methods.
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Spüler, M., Rosenstiel, W., Bogdan, M. (2014). Using Coherence for Robust Online Brain-Computer Interface (BCI) Control. In: Mladenov, V.M., Ivanov, P.C. (eds) Nonlinear Dynamics of Electronic Systems. NDES 2014. Communications in Computer and Information Science, vol 438. Springer, Cham. https://doi.org/10.1007/978-3-319-08672-9_43
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DOI: https://doi.org/10.1007/978-3-319-08672-9_43
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08671-2
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