Abstract
We investigated which evoked response component occurring in the first 800 ms after stimulus presentation was most suitable to be used in a classical P300-based brain–computer interface speller protocol. Data was acquired from 275 Magnetoencephalographic sensors in two subjects and from 61 Electroencephalographic sensors in four. To better characterize the evoked physiological responses and minimize the effect of response overlap, a 1000 ms Inter Stimulus Interval was preferred to the short (<400 ms) trial length traditionally used in this class of BCIs. To investigate which scalp regions conveyed information suitable for BCI, a stepwise linear discriminant analysis classifier was used. The method iteratively analyzed each individual sensor and determined its performance indicators. These were then plotted on a 2-D topographic head map. Preliminary results for both EEG and MEG data suggest that components other than the P300 maximally represented in the occipital region, could be successfully used to improve classification accuracy and finally drive this class of BCIs.
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Acknowledgments
This project was partially supported by the DCMC Project of the Italian Space Agency. Aston University MEG Laboratory is supported by the Dr. Hadwen Trust. Support from the Aston University Visiting Professor Scholar Fund for Luigi Bianchi.
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This is one of several papers published together in Brain Topography on the ‘‘Special Topic: Cortical Network Analysis with EEG/MEG’’.
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Bianchi, L., Sami, S., Hillebrand, A. et al. Which Physiological Components are More Suitable for Visual ERP Based Brain–Computer Interface? A Preliminary MEG/EEG Study. Brain Topogr 23, 180–185 (2010). https://doi.org/10.1007/s10548-010-0143-0
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DOI: https://doi.org/10.1007/s10548-010-0143-0