Xiaogang Chen

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: 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: This paper presents a benchmark steady-state visual evoked potential (SSVEP) dataset acquired with a 40-target brain– computer interface (BCI) speller that provides high-quality data for computational modeling of SSVEPs.

TL;DR: The nonlinear optics of Si photonic wires are discussed in this paper, where the authors review the fundamental optical physics and emerging applications for these Si wires and discuss their potential for functional on-chip nano-nonlinear optical devices of just a few millimeters in length.