ArticleScientific reports2016
Word pair classification during imagined speech using direct brain recordings.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 50 papers.
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Who cites it
50 citing papers in PubMed.
- The role of high-amplitude bursts of high-gamma activity in naturalistic speech and music listening.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- Towards an understanding of somatosensory perturbation on covert speech production: a functional near infrared spectroscopy study.Frontiers in human neuroscience · 2026Article
- Soft Multiaxial Strain Mapping Interface with AI-Driven Decoding for Silent Speech in Noise.Cyborg and bionic systems (Washington, D.C.) · 2026Article
- Hybrid EEG-fNIRS phoneme classification based on imagined and perceived speech.Frontiers in neuroergonomics · 2026Article
- The current status of foundation models in decoding inner speech from non-invasive brain signals: a mini review.Frontiers in human neuroscience · 2026Review
- Progress, challenges and future of linguistic neural decoding with deep learning.Communications biology · 2025Review
- Don't put words in my mouth: speech perception can falsely activate a brain-computer interface.Journal of neuroengineering and rehabilitation · 2025Article
- Learning to operate an imagined speech Brain-Computer Interface involves the spatial and frequency tuning of neural activity.Communications biology · 2025Article
- Systematic review: progress in EEG-based speech imagery brain-computer interface decoding and encoding research.PeerJ. Computer science · 2025Article
- Differences in spatiotemporal dynamics for processing specific semantic categories: An EEG study.Scientific reports · 2024Article
- Decoding Single and Paired Phonemes Using 7T Functional MRI.Brain topography · 2024Article
- Imagined speech event detection from electrocorticography and its transfer between speech modes and subjects.Communications biology · 2024Article
- The speech neuroprosthesis.Nature reviews. Neuroscience · 2024Review
- Representation of internal speech by single neurons in human supramarginal gyrus.Nature human behaviour · 2024Article
- Feasibility of decoding covert speech in ECoG with a Transformer trained on overt speech.Scientific reports · 2024Article
- Boosting brain-computer interfaces with functional electrical stimulation: potential applications in people with locked-in syndrome.Journal of neuroengineering and rehabilitation · 2023Review
- Lateralization and Time-Course of Cortical Phonological Representations during Syllable Production.eNeuro · 2023Article
- Direct speech reconstruction from sensorimotor brain activity with optimized deep learning models.Journal of neural engineering · 2023Article
- The LightGBM-based classification algorithm for Chinese characters speech imagery BCI system.Cognitive neurodynamics · 2023Article
- Multiclass classification of imagined speech EEG using noise-assisted multivariate empirical mode decomposition and multireceptive field convolutional neural network.Frontiers in human neuroscience · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
Abstract
People that cannot communicate due to neurological disorders would benefit from an internal speech decoder. Here, we showed the ability to classify individual words during imagined speech from electrocorticographic signals. In a word imagery task, we used high gamma (70-150 Hz) time features with a support vector machine model to classify individual words from a pair of words. To account for temporal irregularities during speech production, we introduced a non-linear time alignment into the SVM kernel. Classification accuracy reached 88% in a two-class classification framework (50% chance level), and average classification accuracy across fifteen word-pairs was significant across five subjects (mean = 58%; p < 0.05). We also compared classification accuracy between imagined speech, overt speech and listening. As predicted, higher classification accuracy was obtained in the listening and overt speech conditions (mean = 89% and 86%, respectively; p < 0.0001), where speech stimuli were directly presented. The results provide evidence for a neural representation for imagined words in the temporal lobe, frontal lobe and sensorimotor cortex, consistent with previous findings in speech perception and production. These data represent a proof of concept study for basic decoding of speech imagery, and delineate a number of key challenges to usage of speech imagery neural representations for clinical applications.
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