用生成式AI和声音化让脑机接口变透明，提升用户理解与训练效率。
OmniNeuro: A Multimodal HCI Framework for Explainable BCI Feedback via Generative AI and Sonification
- 引入物理、混沌与量子启发三类可解释性指标，实时生成反馈信号。
- 在109人数据集上达58.52%平均准确率，小样本试验证实反馈有效减少试错。
- 适合作为任何脑机接口模型的通用解释层，尤其适合临床场景。
尽管深度学习提升了脑机接口（BCI）的解码精度，但其“黑箱”特性阻碍了临床应用，导致用户挫败感和神经可塑性差。我们提出OmniNeuro，一种新型人机交互框架，将静默的解码器转变为透明的反馈伙伴。该框架集成三种可解释性引擎：(1) 物理（能量），(2) 混沌（分形复杂度），(3) 量子启发的不确定性建模。这些指标驱动实时神经声音化与生成式AI临床报告。在PhysioNet数据集（N=109）上，系统平均准确率达58.52%；小规模定性试点研究（N=3）表明，可解释反馈有助于用户调节心理努力，并缩短“试错”阶段。OmniNeuro具备解码器无关性，可作为任意先进架构的必备可解释性层。
原文摘要 · Abstract (English)
While Deep Learning has improved Brain-Computer Interface (BCI) decoding accuracy, clinical adoption is hindered by the "Black Box" nature of these algorithms, leading to user frustration and poor neuroplasticity outcomes. We propose OmniNeuro, a novel HCI framework that transforms the BCI from a silent decoder into a transparent feedback partner. OmniNeuro integrates three interpretability engines: (1) Physics (Energy), (2) Chaos (Fractal Complexity), and (3) Quantum-Inspired uncertainty modeling. These metrics drive real-time Neuro-Sonification and Generative AI Clinical Reports. Evaluated on the PhysioNet dataset ($N=109$), the system achieved a mean accuracy of 58.52%, with qualitative pilot studies ($N=3$) confirming that explainable feedback helps users regulate mental effort and reduces the "trial-and-error" phase. OmniNeuro is decoder-agnostic, acting as an essential interpretability layer for any state-of-the-art architecture.
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