Objective. Despite the vast research aimed at improving the performance of steady-state
visually evoked potential (SSVEP)-based brain–computer interfaces (BCIs), several …

Objective: This study proposes and evaluates a novel data-driven spatial filtering approach
for enhancing steady-state visual evoked potentials (SSVEPs) detection toward a high …

Brain-computer interfaces (BCIs) provide a direct communication channel between human
brain and output devices. Due to advantages such as non-invasiveness, ease of use, and …

A high-speed steady-state visual evoked potentials (SSVEP)-based brain-computer
interface (BCI) system using dry EEG electrodes was demonstrated in this study. The dry …

Objective. This study aims to establish a generalized transfer-learning framework for
boosting the performance of steady-state visual evoked potential (SSVEP)-based brain …

Objective: A user-friendly steady-state visual evoked potential (SSVEP)-based brain-
computer interface (BCI) prefers no calibration for its target recognition algorithm, however …

Learning from subject's calibration data can significantly improve the performance of a
steady-state visually evoked potential (SSVEP)-based brain–computer interface (BCI), for …

Objective: Currently, ensemble task-related component analysis (eTRCA) and task
discriminative component analysis (TDCA) are the state-of-the-art algorithms for steady-state …

Ear-electroencephalography (EEG) is a promising tool for practical brain-computer interface
(BCI) applications because it is more unobtrusive, comfortable, and mobile than a typical …

Objective: This paper proposes a novel device-to-device transfer-learning algorithm for
reducing the calibration cost in a steady-state visual evoked potential (SSVEP)-based brain …