A Silent Speech Decoding System from EEG and EMG with Heterogenous Electrode Configurations

• URL: http://arxiv.org/abs/2506.13835v1
• Date: Mon, 16 Jun 2025 07:57:35 GMT
• Title: A Silent Speech Decoding System from EEG and EMG with Heterogenous Electrode Configurations
• Authors: Masakazu Inoue, Motoshige Sato, Kenichi Tomeoka, Nathania Nah, Eri Hatakeyama, Kai Arulkumaran, Ilya Horiguchi, Shuntaro Sasai,
• Abstract summary: We introduce neural networks that can handle EEG/EMG with heterogeneous electrode placements.<n>We show strong performance in silent speech decoding via multi-task training on large-scale EEG/EMG datasets.
• Score: 0.20075899678041528
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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