Optimization of SSVEP brain responses with application to eight-command Brain-Computer Interface
Optimization of SSVEP brain responses with application to eight-command Brain-Computer Interface
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DOI：
10.1016/j.neulet.2009.11.039
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发表时间：
2010-01-18
影响因子：
2.5
通讯作者：
Cichocki, Andrzej
中科院分区：
文献类型：
--
作者：
Bakardjian, Hovagim;Tanaka, Toshihisa;Cichocki, Andrzej
本研究旨在优化稳态视觉诱发电位（SSVEP）范式中对小反转模式的大脑反应，该范式可用于最大限度地提高脑机接口（BCI）等应用程序的效率。我们研究了32个频率（5-84 Hz）的SSVEP频率响应，以及在8、14和28 Hz的大脑响应的时间动态，以帮助定义最佳神经生理参数，并概述SSVEP刺激在诸如我们先前描述的四命令BCI系统的应用中的起始延迟和其他限制。我们的研究结果表明，5.6-15.3 Hz的模式反转刺激诱发最强的反应，峰值在12 Hz，并表现出较弱的局部最大值在28和42 Hz。刺激开始后，长期SSVEP反应是高度非平稳的，并且动态（包括第一个峰）是频率依赖性的。对具有动态神经反馈的频率优化的八命令BCI系统的性能的评估显示，平均成功率为98%，时间延迟为3.4 s。所有受试者都实现了强大的BCI性能，即使使用许多小图案，这些图案彼此非常接近，并在2D空间中快速移动。这些结果强调了SSVEP应用不仅需要优化分析算法，还需要优化刺激，以最大限度地提高它们所依赖的大脑反应。（C）2009爱思唯尔爱尔兰有限公司保留所有权利。
This study pursues the optimization of the brain responses to small reversing patterns in a Steady-State Visual Evoked Potentials (SSVEP) paradigm, which could be used to maximize the efficiency of applications such as Brain-Computer Interfaces (BCI). We investigated the SSVEP frequency response for 32 frequencies (5-84 Hz), and the time dynamics of the brain response at 8,14 and 28 Hz, to aid the definition of the optimal neurophysiological parameters and to outline the onset-delay and other limitations of SSVEP stimuli in applications such as our previously described four-command BCI system. Our results showed that the 5.6-15.3 Hz pattern reversal stimulation evoked the strongest responses, peaking at 12 Hz, and exhibiting weaker local maxima at 28 and 42 Hz. After stimulation onset, the long-term SSVEP response was highly non-stationary and the dynamics, including the first peak, was frequency-dependent. The evaluation of the performance of a frequency-optimized eight-command BCI system with dynamic neurofeedback showed a mean success rate of 98%, and a time delay of 3.4 s. Robust BCI performance was achieved by all subjects even when using numerous small patterns clustered very close to each other and moving rapidly in 2D space. These results emphasize the need for SSVEP applications to optimize not only the analysis algorithms but also the stimuli in order to maximize the brain responses they rely on. (C) 2009 Elsevier Ireland Ltd. All rights reserved.