A self-paced BCI prototype system based on the incorporation of an intelligent environment-understanding approach for rehabilitation hospital environmental control
A self-paced BCI prototype system based on the incorporation of an intelligent environment-understanding approach for rehabilitation hospital environmental control
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基于智能环境理解方法的自定进度 BCI 原型系统用于康复医院环境控制
DOI：
10.1016/j.compbiomed.2020.103618
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发表时间：
2020-01
影响因子：
7.7
通讯作者：
Zhou Zongtan
中科院分区：
文献类型：
--
作者：
Liu Yaru;Liu Yadong;Tang Jingsheng;Yin Erwei;Hu Dewen;Zhou Zongtan
本文提出了一种自主节奏的脑-机接口（BCI）的基础上纳入一个智能的环境理解的方法到运动想象（MI）的BCI系统康复医院环境控制。该界面集成了四种类型的日常协助任务：医疗呼叫、服务呼叫、设备控制和餐饮服务。该系统引入智能环境理解技术，通过对象检测神经网络提取当前环境中的潜在操作对象，建立关于用户控制意图的初步预测。根据四类控制和服务的特点，建立不同的响应机制，并采用智能决策方法设计和动态优化相应的控制指令集。控制反馈通过语音提示传达给用户;它避免了在整个交互过程中使用视觉通道。MI-BCI的异步和同步模式分别用于启动控制过程和选择特定操作。特别地，通过优化的识别算法增强了MI-BCI的可靠性。在线实验表明，该系统可以快速响应，平均在3.38 s内生成激活命令，同时有效防止错误激活; BCI同步命令的平均准确率为89.2%，这代表了足够有效的控制。该系统是有效的，适用的，可以用来提高系统的信息吞吐量和减少心理负荷。所提出的系统可用于协助严重运动障碍患者的日常生活。
This paper presents a self-paced brain–computer interface (BCI) based on the incorporation of an intelligent environment-understanding approach into a motor imagery (MI) BCI system for rehabilitation hospital environmental control. The interface integrates four types of daily assistance tasks: medical calls, service calls, appliance control and catering services. The system introduces intelligent environment understanding technology to establish preliminary predictions concerning a user’s control intention by extracting potential operational objects in the current environment through an object detection neural network. According to the characteristics of the four types of control and services, we establish different response mechanisms and use an intelligent decision-making method to design and dynamically optimize the relevant control instruction set. The control feedback is communicated to the user via voice prompts; it avoids the use of visual channels throughout the interaction. The asynchronous and synchronous modes of the MI-BCI are designed to launch the control process and to select specific operations, respectively. In particular, the reliability of the MI-BCI is enhanced by the optimized identification algorithm. An online experiment demonstrated that the system can respond quickly and it generates an activation command in an average of 3.38 s while effectively preventing false activations; the average accuracy of the BCI synchronization commands was 89.2%, which represents sufficiently effective control. The proposed system is efficient, applicable and can be used to both improve system information throughput and to reduce mental loads. The proposed system can be used to assist with the daily lives of patients with severe motor impairments.
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影响因子：
22.7
作者：
McFarland DJ;Wolpaw JR
通讯作者：
Wolpaw JR
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影响因子：
8
作者：
Jin, Jing;Sellers, Eric W.;Cichocki, Andrzej
通讯作者：
Cichocki, Andrzej
针对运动想象 BCI 的基于相关性的时间窗口选择方法
影响因子：
7.8
作者：
Feng, Jiankui;Yin, Erwei;Cichocki, Andrzej
通讯作者：
Cichocki, Andrzej
影响因子：
2
作者：
Aloise, Fabio;Schettini, Francesca;Cincotti, Febo
通讯作者：
Cincotti, Febo
DOI：
10.1007/978-3-642-32332-4_6
发表时间：
2012
期刊：
--
影响因子：
--
作者：
Carlo A. Furia;D. Mandrioli;A. Morzenti;M. Rossi
通讯作者：
Carlo A. Furia;D. Mandrioli;A. Morzenti;M. Rossi