Abu-Alqumsan Mohammad, Ebert Felix, Peer Angelika
Chair of Automatic Control Engineering, Technical University of Munich (TUM), Munich, Germany.
J Neural Eng. 2017 Jun;14(3):036024. doi: 10.1088/1741-2552/aa66e0. Epub 2017 Mar 15.
This work proposes principled strategies for self-adaptations in EEG-based Brain-computer interfaces (BCIs) as a way out of the bandwidth bottleneck resulting from the considerable mismatch between the low-bandwidth interface and the bandwidth-hungry application, and a way to enable fluent and intuitive interaction in embodiment systems. The main focus is laid upon inferring the hidden target goals of users while navigating in a remote environment as a basis for possible adaptations.
To reason about possible user goals, a general user-agnostic Bayesian update rule is devised to be recursively applied upon the arrival of evidences, i.e. user input and user gaze. Experiments were conducted with healthy subjects within robotic embodiment settings to evaluate the proposed method. These experiments varied along three factors: the type of the robot/environment (simulated and physical), the type of the interface (keyboard or BCI), and the way goal recognition (GR) is used to guide a simple shared control (SC) driving scheme.
Our results show that the proposed GR algorithm is able to track and infer the hidden user goals with relatively high precision and recall. Further, the realized SC driving scheme benefits from the output of the GR system and is able to reduce the user effort needed to accomplish the assigned tasks. Despite the fact that the BCI requires higher effort compared to the keyboard conditions, most subjects were able to complete the assigned tasks, and the proposed GR system is additionally shown able to handle the uncertainty in user input during SSVEP-based interaction. The SC application of the belief vector indicates that the benefits of the GR module are more pronounced for BCIs, compared to the keyboard interface.
Being based on intuitive heuristics that model the behavior of the general population during the execution of navigation tasks, the proposed GR method can be used without prior tuning for the individual users. The proposed methods can be easily integrated in devising more advanced SC schemes and/or strategies for automatic BCI self-adaptations.
这项工作提出了基于脑电图的脑机接口（BCI）中自适应的原则性策略，以解决低带宽接口与高带宽需求应用之间的巨大不匹配所导致的带宽瓶颈问题，并为实现实体系统中的流畅直观交互提供一种途径。主要重点在于推断用户在远程环境中导航时的隐藏目标，以此作为可能进行自适应的基础。
为了推断可能的用户目标，设计了一种通用的与用户无关的贝叶斯更新规则，以便在证据（即用户输入和用户注视）到达时递归应用。在机器人实体环境中对健康受试者进行了实验，以评估所提出的方法。这些实验在三个因素上有所不同：机器人/环境的类型（模拟和物理）、接口的类型（键盘或BCI）以及目标识别（GR）用于指导简单共享控制（SC）驱动方案的方式。
我们的结果表明，所提出的GR算法能够以相对较高的精度和召回率跟踪和推断隐藏的用户目标。此外，实现的SC驱动方案受益于GR系统的输出，能够减少完成指定任务所需的用户工作量。尽管与键盘条件相比，BCI需要更多的努力，但大多数受试者能够完成指定任务，并且所提出的GR系统还显示能够处理基于稳态视觉诱发电位（SSVEP）交互期间用户输入的不确定性。信念向量的SC应用表明，与键盘接口相比，GR模块对BCI的益处更为明显。
所提出的GR方法基于直观的启发式方法，对一般人群在执行导航任务时的行为进行建模，无需对个体用户进行预先调整即可使用。所提出的方法可以很容易地集成到设计更先进的SC方案和/或自动BCI自适应策略中。
