OmniNeuro: A Multimodal HCI Framework for Explainable BCI Feedback via Generative AI and Sonification

TL;DR

OmniNeuro introduces a multimodal HCI framework that enhances BCI interpretability using physics, chaos, and quantum-inspired metrics, improving user understanding and clinical feedback through real-time sonification and AI reports.

Contribution

This work presents OmniNeuro, a novel, decoder-agnostic interpretability framework integrating multiple metrics for real-time explainable BCI feedback using generative AI and sonification.

Findings

Achieved 58.52% accuracy on PhysioNet dataset.

Qualitative pilot studies show improved user regulation of mental effort.

Explainable feedback reduces trial-and-error in BCI use.

Abstract

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.