ArticleJournal of neural engineering2019
Speech synthesis from ECoG using densely connected 3D convolutional neural networks.
Article in Journal of neural engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers, 3 of them syntheses that pooled it.
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Who cites it
85 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial intelligence for brain-to-speech decoding in paralysis: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Decoding Intracranial EEG With Machine Learning: A Systematic Review.Frontiers in human neuroscience · 2022Pooled it
- Electroencephalography, Magnetoencephalography, and Cognitive Reserve: A Systematic Review.Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists · 2021Pooled it
- Speech-related dorsal motor cortex activity does not interfere with iBCI cursor control.Journal of neural engineering · 2020Trial
- Noninvasive decoding of typed sentences from human brain activity.Nature neuroscience · 2026Article
- Distinct neural processes link speech planning and execution.Nature human behaviour · 2026Article
- Minimally invasive implantation of scalable high-density cortical microelectrode arrays for multimodal neural decoding and stimulation.Nature biomedical engineering · 2026Article
- Decoding Mandarin Action Verbs from EEG Using a Dual-LSTM Network: Towards Practical Assistive Brain-Computer Interfaces.Sensors (Basel, Switzerland) · 2026Article
- Decoding intended speech with an intracortical brain-computer interface in a person with long-standing anarthria and locked-in syndrome.Cell reports · 2026Article
- Soft Multiaxial Strain Mapping Interface with AI-Driven Decoding for Silent Speech in Noise.Cyborg and bionic systems (Washington, D.C.) · 2026Article
- 3M-CPSEED, An EEG-based Dataset for Chinese Pinyin Production in Overt, Mouthed, and Imagined Speech.Scientific data · 2025Article
- Revealing spatiotemporal neural activation patterns in electrocorticography recordings of human speech production by mutual information.Neuroscience informatics · 2025Article
- Towards decoding individual words from non-invasive brain recordings.Nature communications · 2025Article
- 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
- Progress, challenges and future of linguistic neural decoding with deep learning.Communications biology · 2025Review
- An instantaneous voice-synthesis neuroprosthesis.Nature · 2025Article
- VocalMind: A Stereotactic EEG Dataset for Vocalized, Mimed, and Imagined Speech in Tonal Language.Scientific data · 2025Article
- 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
25 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors.
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Abstract
objectiveDirect synthesis of speech from neural signals could provide a fast and natural way of communication to people with neurological diseases. Invasively-measured brain activity (electrocorticography; ECoG) supplies the necessary temporal and spatial resolution to decode fast and complex processes such as speech production. A number of impressive advances in speech decoding using neural signals have been achieved in recent years, but the complex dynamics are still not fully understood. However, it is unlikely that simple linear models can capture the relation between neural activity and continuous spoken speech. APPROACH: Here we show that deep neural networks can be used to map ECoG from speech production areas onto an intermediate representation of speech (logMel spectrogram). The proposed method uses a densely connected convolutional neural network topology which is well-suited to work with the small amount of data available from each participant. MAIN
resultsIn a study with six participants, we achieved correlations up to r = 0.69 between the reconstructed and original logMel spectrograms. We transfered our prediction back into an audible waveform by applying a Wavenet vocoder. The vocoder was conditioned on logMel features that harnessed a much larger, pre-existing data corpus to provide the most natural acoustic output. SIGNIFICANCE: To the best of our knowledge, this is the first time that high-quality speech has been reconstructed from neural recordings during speech production using deep neural networks.
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