Magnetoencephalography (MEG) Based Non-Invasive Chinese Speech Decoding
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918060000018432 |
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| author | Jia, Zhihong Wang, Hongbin Shen, Yuanzhong Hu, Feng An, Jiayu Shu, Kai Wu, Dongrui |
| author_facet | Jia, Zhihong Wang, Hongbin Shen, Yuanzhong Hu, Feng An, Jiayu Shu, Kai Wu, Dongrui |
| contents | As an emerging paradigm of brain-computer interfaces (BCIs), speech BCI has the potential to directly reflect auditory perception and thoughts, offering a promising communication alternative for patients with aphasia. Chinese is one of the most widely spoken languages in the world, whereas there is very limited research on speech BCIs for Chinese language. This paper reports a text-magnetoencephalography (MEG) dataset for non-invasive Chinese speech BCIs. It also proposes a multi-modality assisted speech decoding (MASD) algorithm to capture both text and acoustic information embedded in brain signals during speech activities. Experiment results demonstrated the effectiveness of both our text-MEG dataset and our proposed MASD algorithm. To our knowledge, this is the first study on modality-assisted decoding for non-invasive speech BCIs. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_12817 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Magnetoencephalography (MEG) Based Non-Invasive Chinese Speech Decoding Jia, Zhihong Wang, Hongbin Shen, Yuanzhong Hu, Feng An, Jiayu Shu, Kai Wu, Dongrui Audio and Speech Processing Sound As an emerging paradigm of brain-computer interfaces (BCIs), speech BCI has the potential to directly reflect auditory perception and thoughts, offering a promising communication alternative for patients with aphasia. Chinese is one of the most widely spoken languages in the world, whereas there is very limited research on speech BCIs for Chinese language. This paper reports a text-magnetoencephalography (MEG) dataset for non-invasive Chinese speech BCIs. It also proposes a multi-modality assisted speech decoding (MASD) algorithm to capture both text and acoustic information embedded in brain signals during speech activities. Experiment results demonstrated the effectiveness of both our text-MEG dataset and our proposed MASD algorithm. To our knowledge, this is the first study on modality-assisted decoding for non-invasive speech BCIs. |
| title | Magnetoencephalography (MEG) Based Non-Invasive Chinese Speech Decoding |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2506.12817 |