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Review
. 2024 Dec;18(6):3565-3583.
doi: 10.1007/s11571-024-10167-0. Epub 2024 Sep 4.

Advances in brain-computer interface for decoding speech imagery from EEG signals: a systematic review

Nimra Rahman  1 , Danish Mahmood Khan  2   3 , Komal Masroor  1 , Mehak Arshad  1 , Amna Rafiq  1 , Syeda Maham Fahim  1
Affiliations
Review

Advances in brain-computer interface for decoding speech imagery from EEG signals: a systematic review

Nimra Rahman et al. Cogn Neurodyn. 2024 Dec.

Abstract

Numerous individuals encounter challenges in verbal communication due to various factors, including physical disabilities, neurological disorders, and strokes. In response to this pressing need, technology has actively pursued solutions to bridge the communication gap, recognizing the inherent difficulties faced in verbal communication, particularly in contexts where traditional methods may be inadequate. Electroencephalogram (EEG) has emerged as a primary non-invasive method for measuring brain activity, offering valuable insights from a cognitive neurodevelopmental perspective. It forms the basis for Brain-Computer Interfaces (BCIs) that provide a communication channel for individuals with neurological impairments, thereby empowering them to express themselves effectively. EEG-based BCIs, especially those adapted to decode imagined speech from EEG signals, represent a significant advancement in enabling individuals with speech disabilities to communicate through text or synthesized speech. By utilizing cognitive neurodevelopmental insights, researchers have been able to develop innovative approaches for interpreting EEG signals and translating them into meaningful communication outputs. To aid researchers in effectively addressing this complex challenge, this review article synthesizes key findings from state-of-the-art significant studies. It investigates into the methodologies employed by various researchers, including preprocessing techniques, feature extraction methods, and classification algorithms utilizing Deep Learning and Machine Learning approaches and their integration. Furthermore, the review outlines the potential avenues for future research, with the goal of advancing the practical implementation of EEG-based BCI systems for decoding imagined speech from a cognitive neurodevelopmental perspective.

Keywords: Brain computer interface (BCI); Deep learning; Electroencephalography (EEG); Imagined speech; Machine learning; Speech imagery.

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Conflict of interest statement

Conflict of interestThe authors declare no potential Conflict of interest.

Figures

Fig. 1
Fig. 1
Steps involved in the inclusion of articles in this systematic review steps involved in the inclusion of articles in this systematic review
Fig. 2
Fig. 2
Major brain regions involved in speech imagery
Fig. 3
Fig. 3
Illustration of the BCIs framework
Fig. 4
Fig. 4
Percent usage of feature extraction techniques in literature
Fig. 5
Fig. 5
Percent usage of classification techniques in literature
See this image and copyright information in PMC

References

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