Impact of age, VR, immersion, and spatial resolution on classifier performance for a MI-based BCI

IF 1.8 Q3 ENGINEERING, BIOMEDICAL Brain-Computer Interfaces Pub Date : 2022-04-04 DOI:10.1080/2326263x.2022.2054606
D. A. Blanco-Mora, A. Aldridge, C. Jorge, A. Vourvopoulos, P. Figueiredo, S., Bermúdez I Badia
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引用次数: 6

Abstract

There are many factors outlined in the signal processing pipeline that impact brain–computer interface (BCI) performance, but some methodological factors do not depend on signal processing. Nevertheless, there is a lack of research assessing the effect of such factors. Here, we investigate the impact of VR, immersiveness, age, and spatial resolution on the classifier performance of a Motor Imagery (MI) electroencephalography (EEG)-based BCI in naïve participants. We found significantly better performance for VR compared to non-VR (15 electrodes: VR 77.48 ± 6.09%, non-VR 73.5 ± 5.89%, p = 0.0096; 12 electrodes: VR 73.26 ± 5.2%, non-VR 70.87 ± 4.96%, p = 0.0129; 7 electrodes: VR 66.74 ± 5.92%, non-VR 63.09 ± 8.16%, p = 0.0362) and better performance for higher electrode quantity, but no significant differences were found between immersive and non-immersive VR. Finally, there was not a statistically significant correlation found between age and classifier performance, but there was a direct relation found between spatial resolution (electrode quantity) and classifier performance (r = 1, p = 0.0129, VR; r = 0.99, p = 0.0859, non-VR).
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年龄,VR,沉浸和空间分辨率对基于mi的BCI分类器性能的影响
在影响脑机接口(BCI)性能的信号处理管道中列出了许多因素,但一些方法因素并不依赖于信号处理。然而,缺乏评估这些因素影响的研究。在这里,我们研究了VR、沉浸感、年龄和空间分辨率对naïve参与者基于运动图像(MI)脑电图(EEG)的脑机接口分类器性能的影响。我们发现,与非VR相比,VR的性能明显更好(15个电极:VR 77.48±6.09%,非VR 73.5±5.89%,p = 0.0096;12个电极:VR 73.26±5.2%,非VR 70.87±4.96%,p = 0.0129;7种电极:VR 66.74±5.92%,非VR 63.09±8.16%,p = 0.0362)且电极数量越多,效果越好,但沉浸式与非沉浸式VR无显著差异。最后,年龄与分类器性能之间没有统计学意义上的相关性,但空间分辨率(电极数量)与分类器性能之间存在直接关系(r = 1, p = 0.0129, VR;r = 0.99, p = 0.0859,非vr)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.00
自引率
9.50%
发文量
14
期刊最新文献
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