OmniNeuro: A Multimodal HCI Framework for Explainable BCI Feedback via Generative AI and Sonification
This addresses the issue of user frustration and poor neuroplasticity outcomes in clinical BCI applications by making decoding more transparent, though it appears incremental as it builds on existing interpretability methods.
The paper tackled the problem of poor clinical adoption of Brain-Computer Interfaces (BCIs) due to their 'black box' nature by proposing OmniNeuro, a framework that provides explainable feedback via generative AI and sonification, achieving a mean accuracy of 58.52% on the PhysioNet dataset and showing in pilot studies that it helps users regulate mental effort and reduces trial-and-error phases.
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.