Naoki Morikawa

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A brain-computer interface (BCI) based on steady-state visual-evoked potentials (SSVEP) has two difficulties: limitation of the number of commands and uneven probabilities of command execution. To address these problems, the present paper proposes a paradigm of BCI using frequency-modulated visual stimuli. The commands are translated into code words(More)
This paper presents the results of the SHREC'10 Protein Models Classification Track. The aim of this track is to evaluate how well 3D shape recognition algorithms can classify protein structures according to the CATH [CSL * 08] superfamily classification. Five groups participated in this track, using a total of six methods, and for each method a set of(More)
Steady-state visual evoked potential (SSVEP) is an effective electrophysiological source to implement a brain-computer interface (BCI). In this paper, a novel frequency recognition method is introduced using two levels of reference signals derived from the training set of real world SSVEP signals with canonical correlation analysis (CCA). The first level(More)
Steady-state visual evoked potentials (SSVEPs) enable brain-computer interfaces to achieve efficient performance in command detection accuracy and information transfer rate (ITR). However, a limited bandwidth of SSVEPs causes a limited number of possible command in BCIs. Moreover since the amplitude of SSVEP at a particular frequency depends on users, some(More)
A brain-computer interface (BCI) based on steady state visual evoked potentials (SSVEPs) is one of the most practical BCI, because of high recognition accuracies and short time training. To increase the number of commands of SSVEP-based BCI, recently a frequency and phase mixed-coded SSVEP BCI has been proposed. However, in order to detect frequency and(More)
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