Making Sense of Reconstruction Models for Human Intention Detection

作     者：Wei Liang Dalin Zhang Xigang Yuan Kaixuan Chen Wei Liang;Dalin Zhang;Xigang Yuan;Kaixuan Chen

作者机构：School of Computer Science and EngineeringUniversity of New South South2052 NSWAustralia Department of Computer Science and EngineeringChangshu Institute of TechnologyChangshu 215500China Business SchoolJiangsu Normal UniversityXuzhou 221116China 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2020年第17卷第4期

页      面：180-192页

核心收录：

学科分类：12[管理学] 07[理学] 08[工学] 0831[工学-生物医学工程（可授工学、理学、医学学位）] 0711[理学-系统科学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 0835[工学-软件工程] 0836[工学-生物工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：intention consuming Reconstruction 

摘      要：Movement intention recognition paves the path to developing Brain-Computer Interface(BCI)*** research mostly focuses on questions like“which hand is intended to move.While answering questions like“whether a hand is intended to moveis more desirable for widely real-world applications,because we cannot continuously perform intention tasks during usage,and the gap periods may cause unintended operation resulting in system ***,this kind of intention detection task is more difficult,since for a“whetherproblem,it is hard to know what the“notsituation is and consequently to acquire training samples for the“not***,the occurrence of genuine intentions is comparatively scarce and unexpected,making the intention detection task hard and *** tackle this problem,we propose a Reconstruction-based Intention Detection(RID)framework,which utilises a reconstruction model to represent a high-level abstraction of EEG signals and leverages the reconstruction errors to determine“whetherthere is a movement *** framework is not only theoretically flexible and robust to any sophisticated real-world scenarios but also hand-crafted feature and domain knowledge *** experiments on detecting movement intention tasks with different reconstruction models demonstrate the promising performance of the proposed reconstruction intention detection framework.