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基于稳态视觉诱发电位的脑-机接口使用过程中 EEG 相位同步和眼动信号的变化。

Changes of EEG phase synchronization and EOG signals along the use of steady state visually evoked potential-based brain computer interface.

机构信息

Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau. Centre for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Macau. School of Engineering Technology, Beijing Normal University, Zhuhai, People's Republic of China. Both authors contributed equally to this work.

出版信息

J Neural Eng. 2020 Jul 13;17(4):045006. doi: 10.1088/1741-2552/ab933e.

Abstract

OBJECTIVE

The steady-state visual evoked potential (SSVEP)-based brain computer interface (BCI) has demonstrated relatively high performance with little user training, and thus becomes a popular BCI paradigm. However, due to the performance deterioration over time, its robustness and reliability appear not sufficient to allow a non-expert to use outside laboratory. It would be thus helpful to study what happens behind the decreasing tendency of the BCI performance.

APPROACH

This paper explores the changes of brain networks and electrooculography (EOG) signals to investigate the cognitive capability changes along the use of the SSVEP-based BCI. The EOG signals are characterized by the blink amplitudes and the speeds of saccades, and the brain networks are estimated by the instantaneous phase synchronizations of electroencephalography signals.

MAIN RESULTS

Experimental results revealed that the characteristics derived from EOG and brain networks have similar trends which contain two stages. At the beginning, the blink amplitudes and the saccade speeds start to reduce. Meanwhile, the global synchronizations of the brain networks are formed quickly. These observations implies that the cognitive decline along the use of the SSVEP-based BCI. Then, the EOG and the brain networks related characteristics demonstrate a slow recovery or relatively stable trend.

SIGNIFICANCE

This study could be helpful for a better understanding about the depreciation of the BCI performance as well as its relationship with the brain networks and the EOG along the use of the SSVEP-based BCI.

摘要

目的

基于稳态视觉诱发电位(SSVEP)的脑机接口(BCI)具有较低的用户训练要求和较高的性能,因此成为一种流行的 BCI 范式。然而,由于性能随时间的下降,其稳健性和可靠性似乎不足以允许非专业人员在实验室之外使用。因此,研究 BCI 性能下降背后的原因将很有帮助。

方法

本文通过研究脑网络和眼动电图(EOG)信号的变化,探讨了基于 SSVEP 的 BCI 使用过程中认知能力的变化。EOG 信号的特征是眨眼幅度和眼球运动速度,而脑网络则通过脑电图信号的瞬时相位同步来估计。

主要结果

实验结果表明,从 EOG 和脑网络中提取的特征具有相似的趋势,包含两个阶段。在开始时,眨眼幅度和眼球运动速度开始降低。同时,脑网络的全局同步迅速形成。这些观察结果表明,基于 SSVEP 的 BCI 使用过程中认知能力下降。然后,EOG 和与脑网络相关的特征表现出缓慢恢复或相对稳定的趋势。

意义

本研究有助于更好地理解 BCI 性能的下降以及其与脑网络和 EOG 之间的关系,为基于 SSVEP 的 BCI 的使用提供了参考。

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