Abstract: The field of speech decoding is rapidly evolving, presenting new challenges and new opportunities for people with disabilities such as amyotrophic lateral sclerosis (ALS), stroke, or paralysis, and for those who support them. However, speech decoding is complex: it requires analysing brain waves, across spatial and temporal dimensions, before translating them into speech. Recent work attempts to recreate speech that is never physically spoken by analysing the brain Artificial-intelligence methods offer a breakthrough because they can analyse complex data, including EEG signals. This paper aims to decode imagined speech through training CNN, RNN, and XGBoost models on a suitable dataset consisting of recorded EEG signals. EEG from 23 individuals is acquired from a public online dataset. These data are preprocessed, and the features are extracted using five different methods. After data acquisition, preprocessing is performed to ensure its readability to the proposed models. After that, five different feature extraction methods have been used and evaluated. Training and testing the proposed models are done after pre-processing and feature extraction to produce classification results. The proposed model involves CNN, LSTM, and XGBoost as classifiers to achieve an effective and robust speech decoding process. The ultimate result reflects on the accuracy with which the algorithms can regenerate speech from EEG signal analysis. The findings will advance speech-decoding research by showing the potential of hybrid deep-learning architectures for precise decoding of imagined speech from EEG signals. These advances have promising potential for creating non-invasive communication systems to assist people with severe speech and motor disorders, thereby improving their quality of life and increasing the application scope of brain-computer interfaces.

Salma Fahad Altharmani and Maha M. Althobaiti, “Speech Decoding from EEG Signals” International Journal of Advanced Computer Science and Applications(IJACSA), 16(4), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160485

@article{Altharmani2025,
title = {Speech Decoding from EEG Signals},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160485},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160485},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {4},
author = {Salma Fahad Altharmani and Maha M. Althobaiti}
}