Discrepancy between inter- and intra-subject variability in EEG-based motor imagery brain-computer interface: Evidence from multiple perspectives.
Discrepancy between inter- and intra-subject variability in EEG-based motor imagery brain-computer interface: Evidence from multiple perspectives.
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基于EEG的运动想象脑机接口中受试者间和受试者内变异性之间的差异：来自多个视角的证据。
DOI：
10.3389/fnins.2023.1122661
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发表时间：
2023
影响因子：
4.3
通讯作者：
Dan, Guo
中科院分区：
文献类型：
--
作者：
Huang, Gan;Zhao, Zhiheng;Zhang, Shaorong;Hu, Zhenxing;Fan, Jiaming;Fu, Meisong;Chen, Jiale;Xiao, Yaqiong;Wang, Jun;Dan, Guo
关键词：
受试者之间和受试者内部的变异是由心理和神经生理因素随时间和受试者之间的变异引起的。在脑机接口（BCI）的应用中，主体间和主体内可变性的存在严重降低了机器学习模型的泛化能力，进一步限制了BCI在现实生活中的应用。虽然许多迁移学习方法都能在一定程度上弥补被试之间和被试内部的差异，但对跨被试和跨会话脑电图信号特征分布的变化仍缺乏明确的认识。为了研究这一问题，本文建立了一个运动图像脑机接口解码的在线平台。对多被试（Exp1）和多会话（Exp2）实验的脑电信号进行了多角度分析。首先，我们发现在分类结果的变异性相似的情况下，Exp2中脑电信号的被试内时频响应比Exp1中脑电信号的跨被试时频响应更一致。第二，共同空间格局（CSP）特征的标准差在Exp1和Exp2之间存在显著差异。第三，对于模型训练，对于跨学科、跨会话的任务，应该采用不同的训练样本选择策略。所有这些发现加深了对主体间和主体内变异性的理解。它们还可以指导基于脑电图的脑机接口中新的迁移学习方法的开发实践。此外，这些结果也证明了脑机接口的低效率不是由被试在运动想象过程中无法产生事件相关的去同步/同步（ERD/ERS）信号引起的。
Inter- and intra-subject variability are caused by the variability of the psychological and neurophysiological factors over time and across subjects. In the application of in Brain-Computer Interfaces (BCI), the existence of inter- and intra-subject variability reduced the generalization ability of machine learning models seriously, which further limited the use of BCI in real life. Although many transfer learning methods can compensate for the inter- and intra-subject variability to some extent, there is still a lack of clear understanding about the change of feature distribution between the cross-subject and cross-session electroencephalography (EEG) signal. To investigate this issue, an online platform for motor-imagery BCI decoding has been built in this work. The EEG signal from both the multi-subject (Exp1) and multi-session (Exp2) experiments has been analyzed from multiple perspectives. Firstly we found that with the similar variability of classification results, the time-frequency response of the EEG signal within-subject in Exp2 is more consistent than cross-subject results in Exp1. Secondly, the standard deviation of the common spatial pattern (CSP) feature has a significant difference between Exp1 and Exp2. Thirdly, for model training, different strategies for the training sample selection should be applied for the cross-subject and cross-session tasks. All these findings have deepened the understanding of inter- and intra-subject variability. They can also guide practice for the new transfer learning methods development in EEG-based BCI. In addition, these results also proved that BCI inefficiency was not caused by the subject’s unable to generate the event-related desynchronization/synchronization (ERD/ERS) signal during the motor imagery.
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