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Tradeoff between User Experience and BCI Classification Accuracy with Frequency Modulated Steady-State Visual Evoked Potentials

机译:用户体验和BCI分类精度之间的权衡频率调制的稳态视觉诱发电位

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摘要

Steady-state visual evoked potentials (SSVEPs) have been widely employed for the control of brain-computer interfaces (BCIs) because they are very robust, lead to high performance, and allow for a high number of commands. However, such flickering stimuli often also cause user discomfort and fatigue, especially when several light sources are used simultaneously. Different variations of SSVEP driving signals have been proposed to increase user comfort. Here, we investigate the suitability of frequency modulation of a high frequency carrier for SSVEP-BCIs. We compared BCI performance and user experience between frequency modulated (FM) and traditional sinusoidal (SIN) SSVEPs in an offline classification paradigm with four independently flickering light-emitting diodes which were overtly attended (fixated). While classification performance was slightly reduced with the FM stimuli, the user comfort was significantly increased. Comparing the SSVEPs for covert attention to the stimuli (without fixation) was not possible, as no reliable SSVEPs were evoked. Our results reveal that several, simultaneously flickering, light emitting diodes can be used to generate FM-SSVEPs with different frequencies and the resulting occipital electroencephalography (EEG) signals can be classified with high accuracy. While the performance we report could be further improved with adjusted stimuli and algorithms, we argue that the increased comfort is an important result and suggest the use of FM stimuli for future SSVEP-BCI applications.
机译:稳态视觉诱发电位(SSVEP)已被广泛用于控制脑机接口(BCI),因为它们非常健壮,可实现高性能并允许大量命令。但是,这种闪烁刺激也常常引起用户不适和疲劳,特别是当同时使用多个光源时。已经提出了SSVEP驱动信号的不同变化以增加用户舒适度。在这里,我们研究了SSVEP-BCI的高频载波频率调制的适用性。我们在离线分类范例中比较了频率调制(FM)和传统正弦曲线(SIN)SSVEP之间的BCI性能和用户体验,并使用四个独立闪烁的发光二极管进行了固定安装。虽然通过FM刺激使分类性能略有下降,但用户的舒适度却大大提高了。由于没有诱发出可靠的SSVEP,因此无法对SSVEP进行比较以隐蔽地注意刺激(没有固定)。我们的结果表明,可以使用几个同时闪烁的发光二极管来生成具有不同频率的FM-SSVEP,并且可以对所产生的枕脑电图(EEG)信号进行高精度分类。虽然我们报告的性能可以通过调整刺激和算法来进一步提高,但我们认为增加的舒适度是重要的结果,并建议在未来的SSVEP-BCI应用中使用FM刺激。

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