Abstract
The electroencephalogram signals of steady-state visual evoked potentials were recorded for three subjects in immersive virtual environment. A machine learning technique of support vector machine with single trial EEG data for 1.0 seconds resulted in 92.1 % of averaged recognition rate in selecting a virtual button among two. The online demonstrations in CAVE showed good performance in position control of a simple 3D object. © Springer-Verlag Berlin Heidelberg 2007.
Cite
CITATION STYLE
Touyama, H., & Hirose, M. (2007). Brain computer interface via stereoscopic images in CAVE. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4557 LNCS, pp. 1004–1007). Springer Verlag. https://doi.org/10.1007/978-3-540-73345-4_113
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