A self-paced BCI prototype system based on the incorporation of an intelligent environment-understanding approach for rehabilitation hospital environmental control. (March 2020)

• Record Type: Journal Article
• Authors: Liu, Yaru; Liu, Yadong; Tang, Jingsheng; Yin, Erwei; Hu, Dewen; Zhou, Zongtan
• Is Part Of: Computers in biology and medicine. Volume 118(2020)
• Publication Date: 2020-03
• Subjects: Brain–computer interface -- Environmental control -- Asynchronous control -- Rehabilitation assistance -- Intelligent environment understanding; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2020.103618
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: 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 3.38s 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. Highlights: Self-paced brain–computer interface for rehabilitation hospital environmental control. Intelligent environment understanding algorithm reduces the instruction set. Decision-making integrates the user's intention and the environment information. The system is built on robot operating system.