Cui Yujie, Xie Songyun, Fu Yingxin, Xie Xinzhou
Shaanxi Joint International Research Center on Integrated Technique of Brain-Computer for Unmanned System, Northwestern Polytechnical University, Xi'an 710129, China.
Xi'an Aeronautics Computing Technique Research Institute, AVIC Xi'an, Xi'an 710068, China.
Brain Sci. 2023 Sep 6;13(9):1288. doi: 10.3390/brainsci13091288.
Motor imagery (MI) electroencephalography (EEG) is natural and comfortable for controllers, and has become a research hotspot in the field of the brain-computer interface (BCI). Exploring the inter-subject MI-BCI performance variation is one of the fundamental problems in MI-BCI application. EEG microstates with high spatiotemporal resolution and multichannel information can represent brain cognitive function. In this paper, four EEG microstates (MS1, MS2, MS3, MS4) were used in the analysis of the differences in the subjects' MI-BCI performance, and the four microstate feature parameters (the mean duration, the occurrences per second, the time coverage ratio, and the transition probability) were calculated. The correlation between the resting-state EEG microstate feature parameters and the subjects' MI-BCI performance was measured. Based on the negative correlation of the occurrence of MS1 and the positive correlation of the mean duration of MS3, a resting-state microstate predictor was proposed. Twenty-eight subjects were recruited to participate in our MI experiments to assess the performance of our resting-state microstate predictor. The experimental results show that the average area under curve (AUC) value of our resting-state microstate predictor was 0.83, and increased by 17.9% compared with the spectral entropy predictor, representing that the microstate feature parameters can better fit the subjects' MI-BCI performance than spectral entropy predictor. Moreover, the AUC of microstate predictor is higher than that of spectral entropy predictor at both the single-session level and average level. Overall, our resting-state microstate predictor can help MI-BCI researchers better select subjects, save time, and promote MI-BCI development.
运动想象（MI）脑电图（EEG）对使用者来说自然且舒适，已成为脑机接口（BCI）领域的研究热点。探索个体间MI-BCI性能差异是MI-BCI应用中的基本问题之一。具有高时空分辨率和多通道信息的脑电微状态能够表征大脑认知功能。本文采用四种脑电微状态（MS1、MS2、MS3、MS4）分析受试者MI-BCI性能差异，并计算了四种微状态特征参数（平均持续时间、每秒出现次数、时间覆盖率和转移概率）。测量了静息态脑电微状态特征参数与受试者MI-BCI性能之间的相关性。基于MS1出现次数的负相关性和MS3平均持续时间的正相关性，提出了一种静息态微状态预测器。招募了28名受试者参与我们的MI实验，以评估我们的静息态微状态预测器的性能。实验结果表明，我们的静息态微状态预测器的平均曲线下面积（AUC）值为0.83，与频谱熵预测器相比提高了17.9%，这表明微状态特征参数比频谱熵预测器能更好地拟合受试者的MI-BCI性能。此外，在单节段水平和平均水平上，微状态预测器的AUC均高于频谱熵预测器。总体而言，我们的静息态微状态预测器可以帮助MI-BCI研究人员更好地选择受试者，节省时间，并促进MI-BCI的发展。
