Yijun Wang

TL;DR: This study presents an electroencephalogram-based BCI speller that can achieve information transfer rates (ITRs) up to 5.32 bits per second, the highest ITRs reported inBCI spellers using either noninvasive or invasive methods, and demonstrates that BCIs can provide a truly naturalistic high-speed communication channel using noninvasively recorded brain activities.

TL;DR: The development of a practical brain-computer interface at Tsinghua University uses frequency-coded steady-state visual evoked potentials to determine the gaze direction of the user to ensure more universal applicability of the system.

TL;DR: By incorporating the fundamental and harmonic SSVEP components in target identification, the proposed FBCCA method significantly improves the performance of theSSVEP-based BCI, and thereby facilitates its practical applications such as high-speed spelling.

TL;DR: A comparison of BCI performance between the proposed TRCA-based method and an extended canonical correlation analysis (CCA)-based method using a 40-class SSVEP dataset recorded from 12 subjects validated the efficiency of the proposal.

TL;DR: The results show that by adequately considering the problems encountered in system design, signal processing, and parameter optimization, SSVEPs can provide the most useful information about brain activities using the least number of electrodes, thus benefiting the implementation of a practical BCI.