ArticleFrontiers in neuroscience2020
Decoding Imagined and Spoken Phrases From Non-invasive Neural (MEG) Signals.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 2 of them syntheses that pooled it.
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Who cites it
43 citing papers in PubMed, 2 syntheses or guidelines pooled it, 119 citations in OpenAlex.
- Artificial intelligence for brain-to-speech decoding in paralysis: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Biomedical signals and machine learning in amyotrophic lateral sclerosis: a systematic review.Biomedical engineering online · 2021Pooled it
- Reconstruction of Imagined Melody with Relative Pitch Decoding in Electrocorticography.eNeuro · 2026Article
- MEG Neural Decoding Pipeline: The Issues Residing Within The Data and Methods to Improve Your Decoding Accuracy.Annals of biomedical engineering · 2026Article
- Imagined Speech Brain-Computer Interface: A Task-Oriented Review of Neural Decoding.Sensors (Basel, Switzerland) · 2026Review
- Time-efficient Methodology for Robustly Assessing Speech-related Mismatch Responses in Adults and Infants.Journal of cognitive neuroscience · 2026Article
- Optimal sensor set for MEG-based spontaneous and intended speech decoding toward practical communication.Frontiers in human neuroscience · 2026Article
- The current status of foundation models in decoding inner speech from non-invasive brain signals: a mini review.Frontiers in human neuroscience · 2026Review
- Cortical activity increases in speech motor areas as a function of the subjective loudness of inner speech.Frontiers in human neuroscience · 2026Article
- 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
- Natural sounds can be reconstructed from human neuroimaging data using deep neural network representation.PLoS biology · 2025Article
- VocalMind: A Stereotactic EEG Dataset for Vocalized, Mimed, and Imagined Speech in Tonal Language.Scientific data · 2025Article
- Classification of Imagined Speech Signals Using Functional Connectivity Graphs and Machine Learning Models.Brain topography · 2025Article
- Classification of Imagined Speech Signals Using Functional Connectivity Graphs and Machine Learning Models.Brain topography · 2025Article
- From pronounced to imagined: improving speech decoding with multi-condition EEG data.Frontiers in neuroinformatics · 2025Article
- Using data from cue presentations results in grossly overestimating semantic BCI performance.Scientific reports · 2024Article
- Neural Decoding of Spontaneous Overt and Intended Speech.Journal of speech, language, and hearing research : JSLHR · 2024Article
- A bilingual speech neuroprosthesis driven by cortical articulatory representations shared between languages.Nature biomedical engineering · 2024Article
- Representation of internal speech by single neurons in human supramarginal gyrus.Nature human behaviour · 2024Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Speech production is a hierarchical mechanism involving the synchronization of the brain and the oral articulators, where the intention of linguistic concepts is transformed into meaningful sounds. Individuals with locked-in syndrome (fully paralyzed but aware) lose their motor ability completely including articulation and even eyeball movement. The neural pathway may be the only option to resume a certain level of communication for these patients. Current brain-computer interfaces (BCIs) use patients' visual and attentional correlates to build communication, resulting in a slow communication rate (a few words per minute). Direct decoding of imagined speech from the neural signals (and then driving a speech synthesizer) has the potential for a higher communication rate. In this study, we investigated the decoding of five imagined and spoken phrases from single-trial, non-invasive magnetoencephalography (MEG) signals collected from eight adult subjects. Two machine learning algorithms were used. One was an artificial neural network (ANN) with statistical features as the baseline approach. The other was convolutional neural networks (CNNs) applied on the spatial, spectral and temporal features extracted from the MEG signals. Experimental results indicated the possibility to decode imagined and spoken phrases directly from neuromagnetic signals. CNNs were found to be highly effective with an average decoding accuracy of up to 93% for the imagined and 96% for the spoken phrases.
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Registered trials
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.