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

• Published in: Frontiers in human neuroscience Vol. 16; p. 867281
• Main Authors: Lopez-Bernal, Diego, Balderas, David, Ponce, Pedro, Molina, Arturo
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 26.04.2022; Frontiers Media S.A
• Subjects: Accuracy; artificial intelligence; BCI; Brain; Classification; Communication channels; Computer applications; Decision making; EEG; Electroencephalography; Human Neuroscience; imagined speech; Implants; Interfaces; Neurological diseases; review; Reviews; Speech; Speech recognition; Voice recognition; imagined speech; BCI; EEG; review; artificial intelligence
• ISSN: 1662-5161; 1662-5161
• DOI: 10.3389/fnhum.2022.867281

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.