<![CDATA[A brain-computer interface (BCI) is a transformative technology that enables users to control external devices or communicate solely through the analysis of their brain activity. One promising aspect of BCIs is the utilization of steady-state visually evoked potentials (SSVEPs), a neurophysiological response in the brain that synchronizes with repetitive visual stimuli. This paper introduces a novel approach known as the trans-subject feature fusion approach (TFA), designed to improve SSVEP-based BCIs. This methodology streamlines data pre-processing, creates invariant SSVEP templates, and simplifies calibration, addressing key challenges that have hindered BCI adoption. By doing so, the main aim is to contribute to the advancement of BCIs, making them more accessible and efficient for a range of applications, from assistive technologies to healthcare, ultimately enhancing users’ communication, and control capabilities.]]>
