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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.20480 |
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| _version_ | 1866913859965550592 |
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| author | Zheng, Hui Wang, Hai-Teng Jing, Yi-Tao Lin, Pei-Yang Zhao, Han-Qing Chen, Wei Wei, Peng-Hu Shan, Yong-Zhi Zhao, Guo-Guang Liu, Yun-Zhe |
| author_facet | Zheng, Hui Wang, Hai-Teng Jing, Yi-Tao Lin, Pei-Yang Zhao, Han-Qing Chen, Wei Wei, Peng-Hu Shan, Yong-Zhi Zhao, Guo-Guang Liu, Yun-Zhe |
| contents | Decoding speech directly from neural activity is a central goal in brain-computer interface (BCI) research. In recent years, exciting advances have been made through the growing use of intracranial field potential recordings, such as stereo-ElectroEncephaloGraphy (sEEG) and ElectroCorticoGraphy (ECoG). These neural signals capture rich population-level activity but present key challenges: (i) task-relevant neural signals are sparsely distributed across sEEG electrodes, and (ii) they are often entangled with task-irrelevant neural signals in both sEEG and ECoG. To address these challenges, we introduce a unified Coarse-to-Fine neural disentanglement framework, BrainStratify, which includes (i) identifying functional groups through spatial-context-guided temporal-spatial modeling, and (ii) disentangling distinct neural dynamics within the target functional group using Decoupled Product Quantization (DPQ). We evaluate BrainStratify on two open-source sEEG datasets and one (epidural) ECoG dataset, spanning tasks like vocal production and speech perception. Extensive experiments show that BrainStratify, as a unified framework for decoding speech from intracranial neural signals, significantly outperforms previous decoding methods. Overall, by combining data-driven stratification with neuroscience-inspired modularity, BrainStratify offers a robust and interpretable solution for speech decoding from intracranial recordings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_20480 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BrainStratify: Coarse-to-Fine Disentanglement of Intracranial Neural Dynamics Zheng, Hui Wang, Hai-Teng Jing, Yi-Tao Lin, Pei-Yang Zhao, Han-Qing Chen, Wei Wei, Peng-Hu Shan, Yong-Zhi Zhao, Guo-Guang Liu, Yun-Zhe Signal Processing Computation and Language Neurons and Cognition Decoding speech directly from neural activity is a central goal in brain-computer interface (BCI) research. In recent years, exciting advances have been made through the growing use of intracranial field potential recordings, such as stereo-ElectroEncephaloGraphy (sEEG) and ElectroCorticoGraphy (ECoG). These neural signals capture rich population-level activity but present key challenges: (i) task-relevant neural signals are sparsely distributed across sEEG electrodes, and (ii) they are often entangled with task-irrelevant neural signals in both sEEG and ECoG. To address these challenges, we introduce a unified Coarse-to-Fine neural disentanglement framework, BrainStratify, which includes (i) identifying functional groups through spatial-context-guided temporal-spatial modeling, and (ii) disentangling distinct neural dynamics within the target functional group using Decoupled Product Quantization (DPQ). We evaluate BrainStratify on two open-source sEEG datasets and one (epidural) ECoG dataset, spanning tasks like vocal production and speech perception. Extensive experiments show that BrainStratify, as a unified framework for decoding speech from intracranial neural signals, significantly outperforms previous decoding methods. Overall, by combining data-driven stratification with neuroscience-inspired modularity, BrainStratify offers a robust and interpretable solution for speech decoding from intracranial recordings. |
| title | BrainStratify: Coarse-to-Fine Disentanglement of Intracranial Neural Dynamics |
| topic | Signal Processing Computation and Language Neurons and Cognition |
| url | https://arxiv.org/abs/2505.20480 |