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Systematic review: progress in EEG-based speech imagery brain-computer interface decoding and encoding research.

Ke Su1, Liang Tian2

  • 1School of Art and Design, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China.

Peerj. Computer Science
|September 24, 2025
PubMed
Summary
This summary is machine-generated.

This review explores electroencephalogram (EEG)-based speech imagery brain-computer interfaces (SI-BCI), focusing on brain connectivity, neural encoding/decoding, and various speech paradigms. Future research should enhance brain mechanisms and algorithms for practical SI-BCI application.

Keywords:
Brain computer interfaceConnectivity of brain regionsDeep learningEEGMachine learningNeural decoding algorithmsNeural encoding technologySpeech imagerySpeech imagery brain-computer interface paradigms

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Computer Science

Background:

  • Brain-computer interfaces (BCIs) offer novel communication pathways for individuals with severe motor impairments.
  • Speech imagery BCIs (SI-BCIs) leverage brain signals related to imagined speech, showing promise for restoring communication.
  • Electroencephalogram (EEG) is a widely used, non-invasive modality for capturing brain activity in SI-BCIs.

Purpose of the Study:

  • To systematically review recent advancements in EEG-based SI-BCI technology.
  • To analyze the role of brain connectivity in neural encoding and decoding for SI-BCI.
  • To explore various speech imagery paradigms and the performance of machine learning and deep learning algorithms in SI-BCI.

Main Methods:

  • Systematic literature review of EEG-based SI-BCI research.
  • Analysis of neural encoding techniques, including EEG signal preprocessing and feature extraction.
  • In-depth examination of neural decoding approaches, focusing on machine learning and deep learning algorithms.

Main Results:

  • EEG-based SI-BCI research has progressed significantly, with advancements in understanding brain connectivity's role.
  • Various speech imagery paradigms (vowels, consonants, characters, words) have been investigated.
  • Machine learning and deep learning algorithms show promising performance in decoding imagined speech from EEG signals.

Conclusions:

  • Current EEG-based SI-BCI research requires further focus on brain region mechanisms and paradigm innovation.
  • Optimization of decoding algorithms is crucial for advancing practical SI-BCI applications.
  • Future directions include integrating advanced signal processing and AI for more robust SI-BCI systems.