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An EEG Signals based Brain-Computer Interface for Silent Speech Recognition: Literature Review
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  • Sudalaimani C,
  • Subodh PS,
  • Binu PJ,
  • Deepu SS,
  • Asha SA,
  • Dhanya MR,
  • Anusree v,
  • Rashmi Sinha,
  • Naveen Kumar Jain,
  • Priyanka Jain,
  • Thomas Iype,
  • Praveen Panicker,
  • Reshma R S,
  • Athira B K,
  • * Archana,
  • Devanand P
Sudalaimani C
Centre for Development of Advanced Computing - Thiruvananthapuram

Corresponding Author:[email protected]

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Subodh PS
Centre for Development of Advanced Computing - Thiruvananthapuram
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Binu PJ
Centre for Development of Advanced Computing - Thiruvananthapuram
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Deepu SS
Centre for Development of Advanced Computing - Thiruvananthapuram
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Asha SA
Centre for Development of Advanced Computing - Thiruvananthapuram
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Dhanya MR
Centre for Development of Advanced Computing - Thiruvananthapuram
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Anusree v
Centre for Development of Advanced Computing - Thiruvananthapuram
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Rashmi Sinha
Centre for Development of Advanced Computing - Thiruvananthapuram
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Naveen Kumar Jain
Centre for Development of Advanced Computing
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Priyanka Jain
Centre for Development of Advanced Computing
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Thomas Iype
Government Medical College Thiruvananthapuram
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Praveen Panicker
Government Medical College Thiruvananthapuram
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Reshma R S
Government Medical College Thiruvananthapuram
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Athira B K
Government Medical College Thiruvananthapuram
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* Archana
Government Medical College Thiruvananthapuram
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Devanand P
Centre for Development of Advanced Computing - Thiruvananthapuram
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Abstract

Electroencephalogram (EEG) based Brain-computer interface (BCI) is a viable technology. It can enable people with temporal voice impairment to communicate to the world directly from their brain. The electrical signals generated by the human brain during sub-vocalized speech are captured, analyzed, and interpreted as speech using BCI. This review offers an introduction and overview of different modalities in EEG-based BCI applications for silent speech, imagined speech, and inner speech recognition. It briefly presents various techniques and methods from pre-processing techniques to feature classification involved in the development of EEG-based BCI applications for silent speech recognition.