Inferring imagined speech using EEG signals: A new approach using Riemannian manifold features

Chuong H. Nguyen, George K. Karavas, Panagiotis Artemiadis

Research output: Contribution to journalArticlepeer-review

49 Scopus citations

Abstract

Objective. In this paper, we investigate the suitability of imagined speech for brain-computer interface (BCI) applications. Approach. A novel method based on covariance matrix descriptors, which lie in Riemannian manifold, and the relevance vector machines classifier is proposed. The method is applied on electroencephalographic (EEG) signals and tested in multiple subjects. Main results. The method is shown to outperform other approaches in the field with respect to accuracy and robustness. The algorithm is validated on various categories of speech, such as imagined pronunciation of vowels, short words and long words. The classification accuracy of our methodology is in all cases significantly above chance level, reaching a maximum of 70% for cases where we classify three words and 95% for cases of two words. Significance. The results reveal certain aspects that may affect the success of speech imagery classification from EEG signals, such as sound, meaning and word complexity. This can potentially extend the capability of utilizing speech imagery in future BCI applications. The dataset of speech imagery collected from total 15 subjects is also published.

Original languageEnglish (US)
Article number016002
JournalJournal of neural engineering
Volume15
Issue number1
DOIs
StatePublished - Feb 2018

Keywords

  • BCI
  • EEG
  • relevance vector machines
  • speech imagery

ASJC Scopus subject areas

  • Biomedical Engineering
  • Cellular and Molecular Neuroscience

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