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Review
. 2022 Apr 26:16:867281.
doi: 10.3389/fnhum.2022.867281. eCollection 2022.

A State-of-the-Art Review of EEG-Based Imagined Speech Decoding

Diego Lopez-Bernal  1 , David Balderas  1 , Pedro Ponce  1 , Arturo Molina  1
Affiliations
Review

A State-of-the-Art Review of EEG-Based Imagined Speech Decoding

Diego Lopez-Bernal et al. Front Hum Neurosci. .

Abstract

Currently, the most used method to measure brain activity under a non-invasive procedure is the electroencephalogram (EEG). This is because of its high temporal resolution, ease of use, and safety. These signals can be used under a Brain Computer Interface (BCI) framework, which can be implemented to provide a new communication channel to people that are unable to speak due to motor disabilities or other neurological diseases. Nevertheless, EEG-based BCI systems have presented challenges to be implemented in real life situations for imagined speech recognition due to the difficulty to interpret EEG signals because of their low signal-to-noise ratio (SNR). As consequence, in order to help the researcher make a wise decision when approaching this problem, we offer a review article that sums the main findings of the most relevant studies on this subject since 2009. This review focuses mainly on the pre-processing, feature extraction, and classification techniques used by several authors, as well as the target vocabulary. Furthermore, we propose ideas that may be useful for future work in order to achieve a practical application of EEG-based BCI systems toward imagined speech decoding.

Keywords: BCI; EEG; artificial intelligence; imagined speech; review.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Technology map of BCI applications.
See this image and copyright information in PMC

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