ArticleFrontiers in neuroscience2015
Brain-to-text: decoding spoken phrases from phone representations in the brain.
Article in Frontiers in neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 117 papers, 1 of them a synthesis that pooled it.
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Who cites it
117 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Biomedical signals and machine learning in amyotrophic lateral sclerosis: a systematic review.Biomedical engineering online · 2021Pooled it
- Real-time detection of spoken speech from unlabeled ECoG signals: a pilot study with an ALS participant.Journal of neural engineering · 2025Trial
- Speech Brain-Computer Interfaces: A New Pathway for Restoring Communication in Anarthria.Neuroscience bulletin · 2026Article
- Shared latent representations of speech production for cross-patient speech decoding.Nature communications · 2026Article
- A Wireless, Battery-Free Artificial Throat Patch with Deep Learning for Emotional Speech Recognition.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Decoupling simultaneous motor imagination and execution via orthogonal ECoG neural representations.Nature communications · 2026Article
- High-fidelity neural speech reconstruction through an efficient acoustic-linguistic dual-pathway framework.eLife · 2026Article
- Brain-Computer Interface Improves Symptoms of Isolated Focal Laryngeal Dystonia: A Single-Blind Study.Movement disorders : official journal of the Movement Disorder Society · 2026Article
- Transfer learning via distributed brain recordings enables reliable speech decoding.Nature communications · 2025Article
- Mimed speech as an intermediary state between overt and imagined speech production in an electrocorticography study.Scientific reports · 2025Article
- Signal properties and stability of a chronically implanted endovascular brain computer interface.medRxiv : the preprint server for health sciences · 2025Article
- Progress, challenges and future of linguistic neural decoding with deep learning.Communications biology · 2025Review
- A streaming brain-to-voice neuroprosthesis to restore naturalistic communication.Nature neuroscience · 2025Article
- Decoding speech intent from non-frontal cortical areas.Journal of neural engineering · 2025Article
- Adaptive GCN and Bi-GRU-Based Dual Branch for Motor Imagery EEG Decoding.Sensors (Basel, Switzerland) · 2025Article
- Deep learning for inner speech recognition: a pilot comparative study of EEGNet and a spectro-temporal Transformer on bimodal EEG-fMRI data.Frontiers in human neuroscience · 2025Article
- Acoustic Inspired Brain-to-Sentence Decoder for Logosyllabic Language.Cyborg and bionic systems (Washington, D.C.) · 2025Article
- Patterned electrical brain stimulation by a wireless network of implantable microdevices.Nature communications · 2024Article
- Real-time detection of spoken speech from unlabeled ECoG signals: A pilot study with an ALS participant.medRxiv : the preprint server for health sciences · 2024Article
- Iterative alignment discovery of speech-associated neural activity.Journal of neural engineering · 2024Article
57 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
Abstract
It has long been speculated whether communication between humans and machines based on natural speech related cortical activity is possible. Over the past decade, studies have suggested that it is feasible to recognize isolated aspects of speech from neural signals, such as auditory features, phones or one of a few isolated words. However, until now it remained an unsolved challenge to decode continuously spoken speech from the neural substrate associated with speech and language processing. Here, we show for the first time that continuously spoken speech can be decoded into the expressed words from intracranial electrocorticographic (ECoG) recordings.Specifically, we implemented a system, which we call Brain-To-Text that models single phones, employs techniques from automatic speech recognition (ASR), and thereby transforms brain activity while speaking into the corresponding textual representation. Our results demonstrate that our system can achieve word error rates as low as 25% and phone error rates below 50%. Additionally, our approach contributes to the current understanding of the neural basis of continuous speech production by identifying those cortical regions that hold substantial information about individual phones. In conclusion, the Brain-To-Text system described in this paper represents an important step toward human-machine communication based on imagined speech.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.