ArticleScientific reports2024
Feasibility of decoding covert speech in ECoG with a Transformer trained on overt speech.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A Precise Focusing Simulation Platform for Transcranial Acoustoelectric Brain Imaging.Sensors (Basel, Switzerland) · 2026Article
- The current status of foundation models in decoding inner speech from non-invasive brain signals: a mini review.Frontiers in human neuroscience · 2026Review
- Ethical considerations for multimodal artificial intelligence in healthcare.AI and ethics · 2026Article
- Perception of brain-computer interface implantation surgery for motor, sensory, and autonomic restoration in spinal cord injury and stroke.Frontiers in neuroscience · 2026Article
- Revealing spatiotemporal neural activation patterns in electrocorticography recordings of human speech production by mutual information.Neuroscience informatics · 2025Article
- Transfer learning via distributed brain recordings enables reliable speech decoding.Nature communications · 2025Article
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Authors and funding
7 authors.
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Abstract
Several attempts for speech brain-computer interfacing (BCI) have been made to decode phonemes, sub-words, words, or sentences using invasive measurements, such as the electrocorticogram (ECoG), during auditory speech perception, overt speech, or imagined (covert) speech. Decoding sentences from covert speech is a challenging task. Sixteen epilepsy patients with intracranially implanted electrodes participated in this study, and ECoGs were recorded during overt speech and covert speech of eight Japanese sentences, each consisting of three tokens. In particular, Transformer neural network model was applied to decode text sentences from covert speech, which was trained using ECoGs obtained during overt speech. We first examined the proposed Transformer model using the same task for training and testing, and then evaluated the model's performance when trained with overt task for decoding covert speech. The Transformer model trained on covert speech achieved an average token error rate (TER) of 46.6% for decoding covert speech, whereas the model trained on overt speech achieved a TER of 46.3%
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