Lingelbach Katharina, Rips Jennifer, Karstensen Lennart, Mathis-Ullrich Franziska, Vukelić Mathias
Applied Neurocognitive Systems, Fraunhofer Institute for Industrial Engineering IAO, Stuttgart, Germany.
Applied Neurocognitive Psychology, Department of Psychology, Carl von Ossietzky University, Oldenburg, Germany.
Front Neuroergon. 2025 Feb 19;6:1535799. doi: 10.3389/fnrgo.2025.1535799. eCollection 2025.
Enhancing medical robot training traditionally relies on explicit feedback from physicians to identify optimal and suboptimal robotic actions during surgery. Passive brain-computer interfaces (BCIs) offer an emerging alternative by enabling implicit brain-based performance evaluations. However, effectively decoding these evaluations of robot performance requires a comprehensive understanding of the spatiotemporal brain dynamics identifying optimal and suboptimal robot actions within realistic settings.
We conducted an electroencephalographic study with 16 participants who mentally assessed the quality of robotic actions while observing simulated robot-assisted laparoscopic surgery scenarios designed to approximate real-world conditions. We aimed to identify key spatiotemporal dynamics using the surface Laplacian technique and two complementary data-driven methods: a mass-univariate permutation-based clustering and multivariate pattern analysis (MVPA)-based temporal decoding. A second goal was to identify the optimal time interval of evoked brain signatures for single-trial classification.
Our analyses revealed three distinct spatiotemporal brain dynamics differentiating the quality assessment of optimal vs. suboptimal robotic actions during video-based laparoscopic training observations. Specifically, an enhanced left fronto-temporal current source, consistent with P300, LPP, and P600 components, indicated heightened attentional allocation and sustained evaluation processes during suboptimal robot actions. Additionally, amplified current sinks in right frontal and mid-occipito-parietal regions suggested prediction-based processing and conflict detection, consistent with the oERN and interaction-based ERN/N400. Both mass-univariate clustering and MVPA provided convergent evidence supporting these neural distinctions.
The identified neural signatures propose that suboptimal robotic actions elicit enhanced, sustained brain dynamics linked to continuous attention allocation, action monitoring, conflict detection, and ongoing evaluative processing. The findings highlight the importance of prioritizing late evaluative brain signatures in BCIs to classify robotic actions reliably. These insights have significant implications for advancing machine-learning-based training paradigms.
传统上，强化医学机器人训练依赖于医生的明确反馈，以在手术过程中识别最佳和次优的机器人操作。被动脑机接口（BCI）通过实现基于大脑的隐式性能评估提供了一种新兴的替代方法。然而，要有效解码这些对机器人性能的评估，需要全面了解在现实场景中识别最佳和次优机器人操作的时空脑动力学。
我们对16名参与者进行了一项脑电图研究，他们在观察模拟的机器人辅助腹腔镜手术场景（旨在近似真实世界情况）时，在心里评估机器人操作的质量。我们旨在使用表面拉普拉斯技术和两种互补的数据驱动方法来识别关键的时空动力学：基于大规模单变量置换的聚类和基于多变量模式分析（MVPA）的时间解码。第二个目标是确定用于单次试验分类的诱发脑信号的最佳时间间隔。
我们的分析揭示了三种不同的时空脑动力学，它们在基于视频的腹腔镜训练观察期间区分了最佳与次优机器人操作的质量评估。具体而言，与P300、LPP和P600成分一致的增强的左额颞电流源表明，在次优机器人操作期间注意力分配增加且持续评估过程增强。此外，右额叶和枕顶叶中部区域放大的电流汇表明基于预测的处理和冲突检测，与oERN以及基于交互的ERN/N400一致。大规模单变量聚类和MVPA都提供了支持这些神经差异的趋同证据。
所识别的神经特征表明，次优机器人操作会引发与持续注意力分配动作监测、冲突检测和正在进行的评估处理相关的增强且持续的脑动力学。这些发现突出了在BCI中优先考虑晚期评估脑特征以可靠地对机器人操作进行分类的重要性。这些见解对推进基于机器学习的训练范式具有重要意义。
