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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 phase of SSVEPs accurately, it is required to...
The classification accuracy of a brain–computer interface (BCI) frequently suffers from ill-posed and overfitting problems. To avoid and alleviate these problems, we propose a method of a multilinear discriminant analysis with constraints to augment parameter reduction, regularization, and additional prior information for event-related potential (ERP)-based BCIs. The method reduces the number of parameters...
Brain-computer interfacing (BCI) based on steady-state visual evoked potentials (SSVEPs) is one of the most practical BCIs because of its high recognition accuracies and little training of a user. Mixed frequency and phase coding which can implement a number of commands and achieve a high information transfer rate (ITR) has recently been gaining much attention. In order to implement mixed-coded SSVEP-BCI...
Detection of frequency for steady-state visual evoked potentials (SSVEP) is addressed. We propose to use the combination of CCA and training data-based template matching between two level of data adaptive reference signals that can deal with the dominant frequency. On the basis of magnitude of stimulus frequency components, the dominant channels are selected. The recognition accuracy as well as the...
Brain-computer interfaces (BCIs) based on event-related potentials (ERP) are promising tools to communicate with patients suffering from some severe disabled diseases. ERP is evoked by various stimuli such as auditory, olfactory, and visual stimuli. Some auditory based BCIs with certain synthetic tone have been proposed, however, it is still challenging to increase the number of commands in auditory-based...
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