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Classification of Phonemes Using EEG

  • R. Aiswarya Priyanka
  • G. Sudha SadasivamEmail author
Conference paper
  • 39 Downloads

Abstract

Artificial speech synthesis can be done using electroencephalography (EEG) and electrocorticography (ECoG) for the brain–computer interface (BCI). This paper focuses on using EEG to classify phonological categories. Although literature is available on the identification and classification of phoneme information in the electroencephalography signals, the classification accuracy of some phonological categories is high, while that of others is too low. Thus, this chapter focuses on identifying the correlation between imagined EEG and audio signals to select the appropriate EEG features. It also identifies the EEG channels that are best suited for imagined speech. Once features are selected, phonemes are classified as vowels and consonants using a support vector machine. Experimental results suggest good accuracy when using 49 features that correlated with audio signals.

Keywords

BCI ECoG EEG Phonology Speech synthesis Though to speech conversion Phoneme extraction Machine learning Deep learning 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringPSG College of TechnologyCoimbatoreIndia

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