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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.00843 |
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| _version_ | 1866918269743529984 |
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| author | Nia, Ayda Aghaei |
| author_facet | Nia, Ayda Aghaei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_00843 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OmniNeuro: A Multimodal HCI Framework for Explainable BCI Feedback via Generative AI and Sonification Nia, Ayda Aghaei Artificial Intelligence 68T07, 92C20 H.5.2; I.2.7; J.3 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. |
| title | OmniNeuro: A Multimodal HCI Framework for Explainable BCI Feedback via Generative AI and Sonification |
| topic | Artificial Intelligence 68T07, 92C20 H.5.2; I.2.7; J.3 |
| url | https://arxiv.org/abs/2601.00843 |