Abdulghani, M.M.; Walters, W.L.; Abed, K.H. Inner Speech Recognition for Mutism and Speech Disorder Using Brain-Computer Interface. Preprints2024, 2024040239. https://doi.org/10.20944/preprints202404.0239.v1
APA Style
Abdulghani, M.M., Walters, W.L., & Abed, K.H. (2024). Inner Speech Recognition for Mutism and Speech Disorder Using Brain-Computer Interface. Preprints. https://doi.org/10.20944/preprints202404.0239.v1
Chicago/Turabian Style
Abdulghani, M.M., Wilbur L. Walters and Khalid H. Abed. 2024 "Inner Speech Recognition for Mutism and Speech Disorder Using Brain-Computer Interface" Preprints. https://doi.org/10.20944/preprints202404.0239.v1
Abstract
Brain-Computer Interface (BCI) systems can assist physically challenged people to interact with their surroundings and improve the quality of their lives. Decoding human thoughts is a powerful technique that can assist paralyzed people who have lost their speech production ability. Speaking is a combined process involving synchronizing the brain and the oral articulators. This paper proposed a high-accuracy brain wave pattern recognition based on inner speech using a novel feature extraction method. Only eight EEG electrodes were used in this study, and they were set on selected spots on the scalp. Support Vector Machine (SVM) was employed to decode the recorded EEG dataset into four internally spoken words which are: Up, Down, Left, and Right. The proposed approach achieved overall classification accuracy that ranges between 96.20% to 97.5%. In addition, more performance evaluation metrics were estimated to test the reliability of classifying the EEG-based inner speech data, and we obtained 97.61%, 97.50%, and 97.73% for F1-score, recall, and precision respectively. Furthermore, the Area Under Curve of the Receiver Operating Characteristic (AUC-ROC) proved the strength of the proposed approach for classifying the specified inner speech commands by achieving a macro-average amount of 99.32%. The inner speech classification method using electroencephalography proposed in this work can clinically help improve communication for patients with problems including speech disorder, mutism, cognitive development, executive function, and psychopathology.
Public Health and Healthcare, Physical Therapy, Sports Therapy and Rehabilitation
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
