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Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have potential to provide a fast communication channel between human brain and external devices. In SSVEP-based BCIs, Canonical Correlation Analysis (CCA) has been widely used to detect frequency-coded SSVEPs due to its high efficiency and robustness. However, the detectability of SSVEPs differs among frequencies due...
In this paper, we studied the brain computer interface (BCI) based on periodic code modulation visual evoked potential (VEP). The code modulation VEP (c-VEP) is one of electroencephalogram (EEG)-based BCI methods, and can achieve high speed communication. In this method, by identifying a pseudorandom binary code (PRBC) that modulates visual stimulus from measured EEG, we can transfer the command related...
We propose a novel method for extracting a rhythmically oscillating signal from EEG recordings including multiple source signals which have similar frequencies. The main application of this method is brain computer interfaces (BCI), which use rhythmically oscillating signals such as alpha, mu, and beta waves, as feature signals. It is difficult to separate those components and/or extract an command-related...
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