通过认知训练提升脑机接口性能:一种以大脑为中心的方法。

Ziyuan Zhang , Ziyu Wang , Kaitai Guo , Yang Zheng , Minghao Dong , Jimin Liang
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引用次数: 0

摘要

以前提高脑机接口(bci)性能的努力主要集中在优化解码大脑信号的算法。然而,利用大脑可塑性进行优化的未开发潜力仍未得到充分探索。在本研究中,我们通过色彩显著性认知训练来消除注意眨眼(attention blink, AB),从而提高了人类大脑辨别视觉刺激的时间分辨率,并证实了这一机制是一种基于注意的改进。采用基于快速串行视觉呈现(RSVP)的脑机接口(BCI),分别评价了受试者在高目标百分比(有AB)和低目标百分比(无AB)监测任务中认知训练前后的行为和脑电图(EEG)解码表现。结果一致表明,受过训练的受试者取得了显著的进步。进一步的分析表明,这种改善归因于经过认知训练的大脑产生了更多的鉴别脑电图。我们的工作强调了认知训练作为大脑增强手段提高脑机接口性能的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Boosting brain-computer interface performance through cognitive training: A brain-centric approach
Previous efforts to boost the performance of brain-computer interfaces (BCIs) have predominantly focused on optimizing algorithms for decoding brain signals. However, the untapped potential of leveraging brain plasticity for optimization remains underexplored. In this study, we enhanced the temporal resolution of the human brain in discriminating visual stimuli by eliminating the attentional blink (AB) through color-salient cognitive training, and we confirmed that the mechanism was an attention-based improvement. Using the rapid serial visual presentation (RSVP)-based BCI, we evaluated the behavioral and electroencephalogram (EEG) decoding performance of subjects before and after cognitive training in high target percentage (with AB) and low target percentage (without AB) surveillance tasks, respectively. The results consistently demonstrated significant improvements in the trained subjects. Further analysis indicated that this improvement was attributed to the cognitively trained brain producing more discriminative EEG. Our work highlights the feasibility of cognitive training as a means of brain enhancement to boost BCI performance.
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