Dimitrova Maya, Wagatsuma Hiroaki, Krastev Aleksandar, Vrochidou Eleni, Nunez-Gonzalez J David
Department of Interactive Robotics and Control Systems, Institute of Robotics, Bulgarian Academy of Sciences, Sofia, Bulgaria.
Department of Human Intelligence Systems, Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology (KYUTECH), Kitakyushu, Japan.
Front Robot AI. 2021 Aug 31;8:715962. doi: 10.3389/frobt.2021.715962. eCollection 2021.
Cyber-physical systems (CPSs) for special education rely on effective mental and brain processing during the lesson, performed with the assistance of humanoid robots. The improved diagnostic ability of the CPS is a prerogative of the system for efficient technological support of the pedagogical process. The article focuses on the available knowledge of possible EEG markers of abstraction, attentiveness, and memorisation (in some cases combined with eye tracking) related to predicting effective mental and brain processing during the lesson. The role of processing abstraction is emphasised as the learning mechanism, which is given priority over the other mechanisms by the cognitive system. The main markers in focus are P1, N170, Novelty P3, RewP, N400, and P600. The description of the effects is accompanied by the analysis of some implications for the design of novel educational scenarios in inclusive classes.
用于特殊教育的网络物理系统(CPS)在课程期间依赖于在人形机器人的协助下进行的有效的心理和大脑处理。CPS诊断能力的提高是教学过程高效技术支持系统的一项特权。本文重点关注与预测课程期间有效的心理和大脑处理相关的抽象、注意力和记忆(在某些情况下与眼动追踪相结合)的可能脑电图标记的现有知识。强调了抽象处理作为学习机制的作用,认知系统将其置于其他机制之上。重点关注的主要标记是P1、N170、新奇P3、奖励正波(RewP)、N400和P600。在描述这些效应的同时,还分析了其对融合课堂中新型教育场景设计的一些启示。