计算机科学
任务(项目管理)
人机系统
人机交互
人工智能
多任务学习
解码方法
机器学习
系统工程
工程类
电信
作者
Jiarong Wang,Luzheng Bi,Weijie Fei
出处
期刊:IEEE transactions on systems, man, and cybernetics
[Institute of Electrical and Electronics Engineers]
日期:2023-04-01
卷期号:53 (4): 2510-2521
被引量:4
标识
DOI:10.1109/tsmc.2022.3212744
摘要
Brain-controlled intelligent vehicles (BCIVs) refer to intelligent vehicles, where brain-computer interfaces (BCIs) are applied to help a person operate (or teleoperate) a vehicle by decoding human intention from brain signals. Existing studies on BCIVs are focused on the single-task operation scenario. Considering that the multitask operation is common in practice, in this article, we design a multitask-oriented BCIV system for the first time by integrating a novel neural decoding method of driver-secondary-task intention with an adaptive brain–machine collaborative controller. We build an experimental platform of the proposed multitask-oriented BCIV system and test the performance of both the primary and secondary tasks by human-and-hardware-in-the-loop experiments. Experimental results show that the proposed multitask-oriented BCIV system performs well. This work has essential values in moving the exploration of brain-controlled systems toward a new step of the multitask operation and opens a new avenue for cognitive neuroscience to be applied to intelligent systems and human–machine integration.
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