A Method for Brain CAVE Interface realizing High Recognition Rates in Virtual Object Selection

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  • Touyama Hideaki
    Graduate School of Information Science and Technology, The University of Tokyo
  • Hirose Michitaka
    Graduate School of Information Science and Technology, The University of Tokyo

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Other Title
  • 高精度に注視仮想物体を認識する脳・CAVE・インタフェースの基礎技術(「BMI/BCI時代の心理学とVR」特集)
  • 高精度に注視仮想物体を認識する脳・CAVE・インタフェースの基礎技術
  • コウセイド ニ チュウシ カソウ ブッタイ オ ニンシキスル ノウ CAVE インタフェース ノ キソ ギジュツ

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In this article, we propose an innovative technology of "Brain CAVE Interface". The electroencephalogram signals of steady-state visual evoked potentials, induced by virtual objects illuminated with constant frequencies, were recorded. Several pattern recognition techniques were investigated to discriminate between brain signals induced by one virtual object and that by another. Machine learning made a remarkable performance. The support vector machine with single trial data for 2.0 (1.0) seconds resulted in 94.6 (91.2) % of averaged recognition rate. This extended concept of non-invasive brain computer interface would enable us to realize intuitive and non-muscular manipulations of virtual objects. The promising visual evoked potentials in CAVE will be suggested in the viewpoint of direction controlling for walking application.

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