基于单次试验脑电的在线人类意图推断作为潜在控制状态

中文摘要 AI 辅助

人类意图可以被建模为一种潜在的内部状态，它调节在人机系统中感觉信息如何被评估并转化为行动。然而，大多数现有的脑机接口（BCIs）依赖于与外部施加的刺激紧密耦合的控制信号，并不明确推断感知到的刺激是否与用户的内部目标一致。在此，我们研究是否可以从单次试验脑电图（EEG）中推断出意图作为一种潜在的、依赖于目标的状态。我们引入了一种基于刺激的范式，其中意图由内部提示的目标类别指定，而物体身份在刺激之间独立变化。为了在单次试验神经变异性下估计意图，我们提出了一种可解释的模糊原型网络，将每次试验映射到编码意图特定动态的可解释模糊原型上。该模型使用一组具有软隶属度的紧凑模糊原型来表示与意图相关的神经活动，从而无需依赖工程化的中介刺激即可实现稳健的解码。实验结果表明，在受试者内部的单次试验意图解码中，该模型可靠且优于代表性的深度学习基线，达到了93.22% ± 3.21%的准确率。在线验证进一步证实了实时可行性，准确率为70.11% ± 10.87%。总之，这些发现将意图感知的脑机接口从刺激驱动的检测推进到对依赖于目标的内部状态的原则性推断。

英文摘要

Human intention can be modeled as a latent internal state that modulates how sensory information is evaluated and translated into action in human-machine systems. However, most existing brain-computer interfaces (BCIs) rely on control signals tightly coupled to externally imposed stimulation and do not explicitly infer whether perceived stimuli align with a user's internal goals. Here, we investigate whether intention can be inferred as a latent, goal-dependent state from single-trial electroencephalography (EEG). We introduce a stimulus-based paradigm in which intention is specified by an internally cued target category, while object identity varies independently across stimuli. To estimate intention under single-trial neural variability, we propose an interpretable fuzzy prototype-based network that maps each trial onto interpretable fuzzy prototypes encoding intention-specific dynamics. The model represents intention-related neural activity using a compact set of fuzzy prototypes with soft memberships, enabling robust decoding without reliance on engineered mediating stimuli. Experimental results demonstrate reliable within-subject single-trial intention decoding that outperforms representative deep learning baselines, achieving an accuracy of 93.22% +/- 3.21%. Online validation further confirms real-time feasibility, with an accuracy of 70.11% +/- 10.87%. Together, these findings advance intention-aware BCIs from stimulus-driven detection toward principled inference of goal-dependent internal states.

发表机构

• University of Technology Sydney（悉尼科技大学）

机构由 AI 辅助整理，请以论文原文为准。