Liu Yu-Ting, Pal Nikhil R, Marathe Amar R, Wang Yu-Kai, Lin Chin-Teng
Faculty of Engineering and Information Technology, Center for Artificial Intelligence, University of Technology SydneySydney, NSW, Australia.
Electronics and Communication Sciences Unit, Indian Statistical InstituteCalcutta, India.
Front Neurosci. 2017 Jun 20;11:332. doi: 10.3389/fnins.2017.00332. eCollection 2017.
A brain-computer interface (BCI) creates a direct communication pathway between the human brain and an external device or system. In contrast to patient-oriented BCIs, which are intended to restore inoperative or malfunctioning aspects of the nervous system, a growing number of BCI studies focus on designing auxiliary systems that are intended for everyday use. The goal of building these BCIs is to provide capabilities that augment existing intact physical and mental capabilities. However, a key challenge to BCI research is human variability; factors such as fatigue, inattention, and stress vary both across different individuals and for the same individual over time. If these issues are addressed, autonomous systems may provide additional benefits that enhance system performance and prevent problems introduced by individual human variability. This study proposes a human-machine autonomous (HMA) system that simultaneously aggregates human and machine knowledge to recognize targets in a rapid serial visual presentation (RSVP) task. The HMA focuses on integrating an RSVP BCI with computer vision techniques in an image-labeling domain. A fuzzy decision-making fuser (FDMF) is then applied in the HMA system to provide a natural adaptive framework for evidence-based inference by incorporating an integrated summary of the available evidence (i.e., human and machine decisions) and associated uncertainty. Consequently, the HMA system dynamically aggregates decisions involving uncertainties from both human and autonomous agents. The collaborative decisions made by an HMA system can achieve and maintain superior performance more efficiently than either the human or autonomous agents can achieve independently. The experimental results shown in this study suggest that the proposed HMA system with the FDMF can effectively fuse decisions from human brain activities and the computer vision techniques to improve overall performance on the RSVP recognition task. This conclusion demonstrates the potential benefits of integrating autonomous systems with BCI systems.
脑机接口（BCI）在人脑与外部设备或系统之间创建了一条直接的通信路径。与旨在恢复神经系统无法正常运作或出现故障的部分功能的面向患者的BCI不同，越来越多的BCI研究专注于设计供日常使用的辅助系统。构建这些BCI的目标是提供增强现有完好的身体和心理能力的功能。然而，BCI研究面临的一个关键挑战是人类的变异性；诸如疲劳、注意力不集中和压力等因素在不同个体之间以及同一个体随时间推移都会有所不同。如果解决了这些问题，自主系统可能会带来额外的好处，即提高系统性能并防止个体人类变异性带来的问题。本研究提出了一种人机自主（HMA）系统，该系统同时整合人类和机器知识，以在快速序列视觉呈现（RSVP）任务中识别目标。HMA专注于在图像标注领域将RSVP BCI与计算机视觉技术相结合。然后，在HMA系统中应用模糊决策融合器（FDMF），通过纳入可用证据（即人类和机器决策）的综合摘要以及相关不确定性，为基于证据的推理提供一个自然的自适应框架。因此，HMA系统动态地整合涉及人类和自主智能体不确定性的决策。HMA系统做出的协作决策能够比人类或自主智能体单独实现的决策更高效地实现并维持卓越性能。本研究中展示的实验结果表明，所提出的带有FDMF的HMA系统能够有效地融合来自人类大脑活动和计算机视觉技术的决策，从而提高RSVP识别任务的整体性能。这一结论证明了将自主系统与BCI系统集成的潜在好处。
