A Silent Speech Decoding System from EEG and EMG with Heterogenous Electrode Configurations
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908409198018560 |
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| author | Inoue, Masakazu Sato, Motoshige Tomeoka, Kenichi Nah, Nathania Hatakeyama, Eri Arulkumaran, Kai Horiguchi, Ilya Sasai, Shuntaro |
| author_facet | Inoue, Masakazu Sato, Motoshige Tomeoka, Kenichi Nah, Nathania Hatakeyama, Eri Arulkumaran, Kai Horiguchi, Ilya Sasai, Shuntaro |
| contents | Silent speech decoding, which performs unvocalized human speech recognition from electroencephalography/electromyography (EEG/EMG), increases accessibility for speech-impaired humans. However, data collection is difficult and performed using varying experimental setups, making it nontrivial to collect a large, homogeneous dataset. In this study we introduce neural networks that can handle EEG/EMG with heterogeneous electrode placements and show strong performance in silent speech decoding via multi-task training on large-scale EEG/EMG datasets. We achieve improved word classification accuracy in both healthy participants (95.3%), and a speech-impaired patient (54.5%), substantially outperforming models trained on single-subject data (70.1% and 13.2%). Moreover, our models also show gains in cross-language calibration performance. This increase in accuracy suggests the feasibility of developing practical silent speech decoding systems, particularly for speech-impaired patients. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_13835 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Silent Speech Decoding System from EEG and EMG with Heterogenous Electrode Configurations Inoue, Masakazu Sato, Motoshige Tomeoka, Kenichi Nah, Nathania Hatakeyama, Eri Arulkumaran, Kai Horiguchi, Ilya Sasai, Shuntaro Quantitative Methods Machine Learning Neurons and Cognition Silent speech decoding, which performs unvocalized human speech recognition from electroencephalography/electromyography (EEG/EMG), increases accessibility for speech-impaired humans. However, data collection is difficult and performed using varying experimental setups, making it nontrivial to collect a large, homogeneous dataset. In this study we introduce neural networks that can handle EEG/EMG with heterogeneous electrode placements and show strong performance in silent speech decoding via multi-task training on large-scale EEG/EMG datasets. We achieve improved word classification accuracy in both healthy participants (95.3%), and a speech-impaired patient (54.5%), substantially outperforming models trained on single-subject data (70.1% and 13.2%). Moreover, our models also show gains in cross-language calibration performance. This increase in accuracy suggests the feasibility of developing practical silent speech decoding systems, particularly for speech-impaired patients. |
| title | A Silent Speech Decoding System from EEG and EMG with Heterogenous Electrode Configurations |
| topic | Quantitative Methods Machine Learning Neurons and Cognition |
| url | https://arxiv.org/abs/2506.13835 |