A Robust Multi-Scale Framework with Test-Time Adaptation for sEEG-Based Speech Decoding
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866918150551896064 |
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| author | Wang, Suli Li, Yang-yang Cai, Siqi Li, Haizhou |
| author_facet | Wang, Suli Li, Yang-yang Cai, Siqi Li, Haizhou |
| contents | Decoding speech from stereo-electroencephalography (sEEG) signals has emerged as a promising direction for brain-computer interfaces (BCIs). Its clinical applicability, however, is limited by the inherent non-stationarity of neural signals, which causes domain shifts between training and testing, undermining decoding reliability. To address this challenge, a two-stage framework is proposed for enhanced robustness. First, a multi-scale decomposable mixing (MDM) module is introduced to model the hierarchical temporal dynamics of speech production, learning stable multi-timescale representations from sEEG signals. Second, a source-free online test-time adaptation (TTA) method performs entropy minimization to adapt the model to distribution shifts during inference. Evaluations on the public DU-IN spoken word decoding benchmark show that the approach outperforms state-of-the-art models, particularly in challenging cases. This study demonstrates that combining invariant feature learning with online adaptation is a principled strategy for developing reliable BCI systems. Our code is available at https://github.com/lyyi599/MDM-TENT. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_24700 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Robust Multi-Scale Framework with Test-Time Adaptation for sEEG-Based Speech Decoding Wang, Suli Li, Yang-yang Cai, Siqi Li, Haizhou Human-Computer Interaction Decoding speech from stereo-electroencephalography (sEEG) signals has emerged as a promising direction for brain-computer interfaces (BCIs). Its clinical applicability, however, is limited by the inherent non-stationarity of neural signals, which causes domain shifts between training and testing, undermining decoding reliability. To address this challenge, a two-stage framework is proposed for enhanced robustness. First, a multi-scale decomposable mixing (MDM) module is introduced to model the hierarchical temporal dynamics of speech production, learning stable multi-timescale representations from sEEG signals. Second, a source-free online test-time adaptation (TTA) method performs entropy minimization to adapt the model to distribution shifts during inference. Evaluations on the public DU-IN spoken word decoding benchmark show that the approach outperforms state-of-the-art models, particularly in challenging cases. This study demonstrates that combining invariant feature learning with online adaptation is a principled strategy for developing reliable BCI systems. Our code is available at https://github.com/lyyi599/MDM-TENT. |
| title | A Robust Multi-Scale Framework with Test-Time Adaptation for sEEG-Based Speech Decoding |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2509.24700 |