SACM: SEEG-Audio Contrastive Matching for Chinese Speech Decoding
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916759231004672 |
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| author | Wang, Hongbin Jia, Zhihong Shen, Yuanzhong Wang, Ziwei Li, Siyang Shu, Kai Hu, Feng Wu, Dongrui |
| author_facet | Wang, Hongbin Jia, Zhihong Shen, Yuanzhong Wang, Ziwei Li, Siyang Shu, Kai Hu, Feng Wu, Dongrui |
| contents | Speech disorders such as dysarthria and anarthria can severely impair the patient's ability to communicate verbally. Speech decoding brain-computer interfaces (BCIs) offer a potential alternative by directly translating speech intentions into spoken words, serving as speech neuroprostheses. This paper reports an experimental protocol for Mandarin Chinese speech decoding BCIs, along with the corresponding decoding algorithms. Stereo-electroencephalography (SEEG) and synchronized audio data were collected from eight drug-resistant epilepsy patients as they conducted a word-level reading task. The proposed SEEG and Audio Contrastive Matching (SACM), a contrastive learning-based framework, achieved decoding accuracies significantly exceeding chance levels in both speech detection and speech decoding tasks. Electrode-wise analysis revealed that a single sensorimotor cortex electrode achieved performance comparable to that of the full electrode array. These findings provide valuable insights for developing more accurate online speech decoding BCIs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_19652 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SACM: SEEG-Audio Contrastive Matching for Chinese Speech Decoding Wang, Hongbin Jia, Zhihong Shen, Yuanzhong Wang, Ziwei Li, Siyang Shu, Kai Hu, Feng Wu, Dongrui Human-Computer Interaction Sound Audio and Speech Processing Speech disorders such as dysarthria and anarthria can severely impair the patient's ability to communicate verbally. Speech decoding brain-computer interfaces (BCIs) offer a potential alternative by directly translating speech intentions into spoken words, serving as speech neuroprostheses. This paper reports an experimental protocol for Mandarin Chinese speech decoding BCIs, along with the corresponding decoding algorithms. Stereo-electroencephalography (SEEG) and synchronized audio data were collected from eight drug-resistant epilepsy patients as they conducted a word-level reading task. The proposed SEEG and Audio Contrastive Matching (SACM), a contrastive learning-based framework, achieved decoding accuracies significantly exceeding chance levels in both speech detection and speech decoding tasks. Electrode-wise analysis revealed that a single sensorimotor cortex electrode achieved performance comparable to that of the full electrode array. These findings provide valuable insights for developing more accurate online speech decoding BCIs. |
| title | SACM: SEEG-Audio Contrastive Matching for Chinese Speech Decoding |
| topic | Human-Computer Interaction Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2505.19652 |