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Establishing a high-accuracy and training-free brain–computer interface (BCI) system is essential for improving BCI practicality. In this study, we propose for the first time a training-free double-partial least-squares (D-PLS) model for steady-state visual evoked potential (SSVEP) detection that consists of double-layer PLS, a PLS spatial filter, and a PLS feature extractor. Electroencephalographic...
The canonical correlation analysis (CCA), double-partial least-squares (DPLS) methods and least absolute shrinkage and selection operator (LASSO) have been proven effectively in detecting the steady-state visual evoked potential (SSVEP) in SSVEP-based brain-computer interface systems. However, the accuracy of SSVEP classification can be affected by phase shifts of the electroencephalography data,...
With shorter calibration times and higher information transfer rates, steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have been studied most activity in recent years. Target identification is the ongoing core task in BCI researches, and plays a significant role in practical applications. In order to improve the performance of SSVEP-based BCI system, we proposed...
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