Brain-Computer Interfaces (BCI) have allowed for direct communication from the brain to external applications for the automatic detection of cognitive processes such as error recognition. Error-related potentials (ErrPs) are a particular brain signal elicited when one commits or observes an erroneous event. However, due to the noisy properties of the brain and recording devices, ErrPs vary from instance to instance as they are combined with an assortment of other brain signals, biological noise, and external noise, making the classification of ErrPs a non-trivial problem. Recent works have revealed particular cognitive processes such as awareness, embodiment, and predictability that contribute to ErrP variations. In this paper, we explore the performance of classifier transferability when trained on different ErrP variation datasets generated by varying the levels of awareness and embodiment for a given task. In particular, we look at transference between observational and interactive ErrP categories when elicited by similar and differing tasks. Our empirical results provide an exploratory analysis into the ErrP transferability problem from a data perspective.
翻译:大脑-计算机界面(BCI)允许大脑与外部应用直接通信,以便自动检测认知过程,如错误识别。错误相关潜力(ERP)是当一个人实施或观察错误事件时产生的特定大脑信号。然而,由于大脑和记录装置的吵闹性质,ERP因实例而异,因为它们与其他大脑信号、生物噪音和外部噪音的分解相结合,使ERP的分类成为一个非三重问题。最近的工作揭示了特定的认知过程,例如认识、化和可预测性,这些过程有助于ERP的变异。在本文件中,我们探讨了在对不同认识水平和感化为特定任务产生的不同的ERP变异数据集进行培训时,分类可转移性的表现。特别是,我们从类似和不同的任务引出观测和互动的ERP类别时,我们研究了这些类别的转移。我们的经验结果从数据角度对ERP的可转移性问题进行了探索性分析。