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Steady-state visual evoked potential (SSVEP)-based brain computer-interface (BCI) is one of the most popular BCI systems. An efficient SSVEP-based BCI system in shorter time with higher accuracy in recognizing SSVEP has been pursued by many studies. This paper introduces a novel multiway canonical correlation analysis (Multiway CCA) approach to recognize(More)
P300 brain-computer interface (BCI) systems typically use a row/column (RC) approach. This article presents a P300 BCI based on a 12 x 7 matrix and new paradigmatic approaches to flashing characters designed to decrease the number of flashes needed to identify a target character. Using an RC presentation, a 12 x 7 matrix requires 19 flashes to present all(More)
Canonical correlation analysis (CCA) has been one of the most popular methods for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). Despite its efficiency, a potential problem is that using pre-constructed sine-cosine waves as the required reference signals in the CCA method often does not result(More)
Keywords: Brain–computer interfaces (BCIs) Electroencephalogram (EEG) Least absolute shrinkage and selection operator (LASSO) Steady-state visual evoked potential (SSVEP) Time window (TW) a b s t r a c t Steady-state visual evoked potential (SSVEP) has been increasingly used for the study of brain–computer interface (BCI). How to recognize SSVEP with(More)
Most automatic spike detection systems in the scalp electroencephalogram (EEG) focused on the characteristics of "spike." However, the characteristics of "false positives" (FPs) have not been fully studied. In this paper, we proposed a system that contains a series of algorithms to eliminate FPs and a template method to confirm spikes. The system used large(More)
The P300-based brain-computer interface (BCI) is an extension of the oddball paradigm, and can facilitate communication for people with severe neuromuscular disorders. It has been shown that, in addition to the P300, other event-related potential (ERP) components have been shown to contribute to successful operation of the P300 BCI. Incorporating these(More)
Quantitative analysis and detection of electroencephalogram (EEG) recordings during evoked activities is essential for clinical diagnosis on neurological disorders. However, the process of interpreting EEG is time consuming for electroencephalographers (EEGers). In this study, an automatic EEG interpretation system constructed in the way of qualified(More)