Sengelmann Malte, Engel Andreas K, Maye Alexander
IEEE Trans Biomed Eng. 2017 Feb;64(2):381-394. doi: 10.1109/TBME.2016.2559527.
Compared to the different brain signals used in brain-computer interface (BCI) paradigms, the s teady-state visually evoked potential (SSVEP) features a high signal to noise ratio, enabling reliable and fast classification of neural activity patterns without extensive training requirements. In this paper, we present methods to further increase the information transfer rates (ITRs) of SSVEP-based BCIs. Starting with stimulus parameter optimizations methods, we develop an improved approach for the use of Canonical correlation analysis and analyze properties of the SSVEP when the user fixates a target and during transitions between targets. These transitions show a negative effect on the system's ITR which we trace back to delays and dead times of the SSVEP. Using two classifier types adapted to continuous and transient SSVEPs and two control modes (fast feedback and fast input), we present a simulated online BCI implementation which addresses the challenges introduced by transient SSVEPs. The resulting system reaches an average ITR of 181 Bits/min and peak ITR values of up to 295 Bits/min for individual users.
与脑机接口（BCI）范式中使用的不同脑信号相比，稳态视觉诱发电位（SSVEP）具有高信噪比，能够在无需大量训练的情况下可靠且快速地对神经活动模式进行分类。在本文中，我们提出了进一步提高基于SSVEP的BCI信息传输率（ITR）的方法。从刺激参数优化方法入手，我们开发了一种改进的典型相关分析使用方法，并分析了用户注视目标时以及目标之间转换期间SSVEP的特性。这些转换对系统的ITR有负面影响，我们将其追溯到SSVEP的延迟和死区时间。使用两种适用于连续和瞬态SSVEP的分类器类型以及两种控制模式（快速反馈和快速输入），我们展示了一种模拟在线BCI实现，该实现解决了瞬态SSVEP带来的挑战。对于个体用户，所得系统的平均ITR达到181比特/分钟，峰值ITR值高达295比特/分钟。
