ArticleCognitive neurodynamics2020
Identification of vowels in consonant-vowel-consonant words from speech imagery based EEG signals.
Article in Cognitive neurodynamics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- The current status of foundation models in decoding inner speech from non-invasive brain signals: a mini review.Frontiers in human neuroscience · 2026Review
- 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
- A computational eye state classification model using EEG signal based on data mining techniques: comparative analysis.Physical and engineering sciences in medicine · 2025Article
- Proficiency in motor imagery is linked to the lateralization of focused ERD patterns and beta PDC.Journal of neuroengineering and rehabilitation · 2025Article
- Systematic review: progress in EEG-based speech imagery brain-computer interface decoding and encoding research.PeerJ. Computer science · 2025Article
- Imagined speech classification exploiting EEG power spectrum features.Medical & biological engineering & computing · 2024Article
- Decoding Subject-Driven Cognitive States from EEG Signals for Cognitive Brain-Computer Interface.Brain sciences · 2024Article
- Integrating Simultaneous Motor Imagery and Spatial Attention for EEG-BCI Control.IEEE transactions on bio-medical engineering · 2024Article
- Effect of functional and effective brain connectivity in identifying vowels from articulation imagery procedures.Cognitive processing · 2022Article
- Preliminary Evidence of EEG Connectivity Changes during Self-Objectification of Workers.Sensors (Basel, Switzerland) · 2022Article
- [Key technology of brain-computer interaction based on speech imagery].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Article
- A State-of-the-Art Review of EEG-Based Imagined Speech Decoding.Frontiers in human neuroscience · 2022Review
- Silent EEG-Speech Recognition Using Convolutional and Recurrent Neural Network with 85% Accuracy of 9 Words Classification.Sensors (Basel, Switzerland) · 2021Article
- Recognition of EEG Signals from Imagined Vowels Using Deep Learning Methods.Sensors (Basel, Switzerland) · 2021Article
- Decoding Covert Speech From EEG-A Comprehensive Review.Frontiers in neuroscience · 2021Review
- EEG Correlates of Middle Eastern Music Improvisations on theFrontiers in psychology · 2021Article
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Retrieval of unintelligible speech is a basic need for speech impaired and is under research for several decades. But retrieval of random words from thoughts needs a substantial and consistent approach. This work focuses on the preliminary steps of retrieving vowels from Electroencephalography (EEG) signals acquired while speaking and imagining of speaking a consonant-vowel-consonant (CVC) word. The process, referred to as Speech imagery is imagining of speaking to oneself silently in the mind. Speech imagery is a form of mental imagery. Brain connectivity estimators such as EEG coherence, Partial Directed Coherence, Directed Transfer Function and Transfer Entropy have been used to estimate the concurrency and causal dependence (direction and strength) between different brain regions. From brain connectivity results it has been observed that the left frontal and left temporal electrodes were activated for speech and speech imagery processes. These brain connectivity estimators have been used for training Recurrent Neural Networks (RNN) and Deep Belief Networks (DBN) for identifying the vowel from the subject's thought. Though the accuracy level was found to be varying for each vowel while speaking and imagining of speaking the CVC word, the overall classification accuracy was found to be 72% while using RNN whereas a classification accuracy of 80% was observed while using DBN. DBN was found to outperform RNN in both the speech and speech imagery processes. Thus, the combination of brain connectivity estimators and deep learning techniques appear to be effective in identifying the vowel from EEG signals of subjects' thought.
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