ReviewFrontiers in human neuroscience2022
A State-of-the-Art Review of EEG-Based Imagined Speech Decoding.
Review in Frontiers in human neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic Review of EEG-Based Imagined Speech Classification Methods.Sensors (Basel, Switzerland) · 2024Pooled it
- Shared latent representations of speech production for cross-patient speech decoding.Nature communications · 2026Article
- Imagined Speech Brain-Computer Interface: A Task-Oriented Review of Neural Decoding.Sensors (Basel, Switzerland) · 2026Review
- EEG imagined speech neuro-signal preprocessing and deep learning classification.Scientific reports · 2026Article
- Individualized brain-computer interface for people with disabilities: a review.Frontiers in human neuroscience · 2026Review
- ArEEG: an Open-Access Arabic Inner Speech EEG Dataset.Scientific data · 2025Article
- An open-access EEG dataset for speech decoding: Exploring the role of articulation and coarticulation.Scientific data · 2025Article
- Acquisition Of Balinese Imagined Spelling using Electroencephalogram (BISE) Dataset.Data in brief · 2025Article
- Machine learning classification of active viewing of pain and non-pain images using EEG does not exceed chance in external validation samples.Cognitive, affective & behavioral neuroscience · 2025Article
- Learning to operate an imagined speech Brain-Computer Interface involves the spatial and frequency tuning of neural activity.Communications biology · 2025Article
- Synthesizing intelligible utterances from EEG of imagined speech.Frontiers in neuroscience · 2025Article
- Systematic review: progress in EEG-based speech imagery brain-computer interface decoding and encoding research.PeerJ. Computer science · 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
- Stochasticity as a solution for overfitting-A new model and comparative study on non-invasive EEG prospects.Frontiers in human neuroscience · 2025Article
- Advances in brain-computer interface for decoding speech imagery from EEG signals: a systematic review.Cognitive neurodynamics · 2024Review
- Decoding Imagined Speech from EEG Data: A Hybrid Deep Learning Approach to Capturing Spatial and Temporal Features.Life (Basel, Switzerland) · 2024Article
- Emerging Brain-to-Content Technologies from Generative AI and Deep Representation Learning.IEEE signal processing magazine · 2024Article
- Imagined speech classification exploiting EEG power spectrum features.Medical & biological engineering & computing · 2024Article
- The speech neuroprosthesis.Nature reviews. Neuroscience · 2024Review
- 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Currently, the most used method to measure brain activity under a non-invasive procedure is the electroencephalogram (EEG). This is because of its high temporal resolution, ease of use, and safety. These signals can be used under a Brain Computer Interface (BCI) framework, which can be implemented to provide a new communication channel to people that are unable to speak due to motor disabilities or other neurological diseases. Nevertheless, EEG-based BCI systems have presented challenges to be implemented in real life situations for imagined speech recognition due to the difficulty to interpret EEG signals because of their low signal-to-noise ratio (SNR). As consequence, in order to help the researcher make a wise decision when approaching this problem, we offer a review article that sums the main findings of the most relevant studies on this subject since 2009. This review focuses mainly on the pre-processing, feature extraction, and classification techniques used by several authors, as well as the target vocabulary. Furthermore, we propose ideas that may be useful for future work in order to achieve a practical application of EEG-based BCI systems toward imagined speech decoding.
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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.