Inner Speech Recognition for Mutism and Speech Disorder Using Brain-Computer Interface

Author(s): Mokhles M. Abdulghani, Khalid H

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 ranged 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 method of classifying inner speech through EEG, as proposed in this study, has the potential to significantly enhance communication for patients experiencing conditions such as speech disorders, mutism, cognitive development issues, executive function impairments, and psychopathological disorders. Furthermore, this technology can be utilized as a control mechanism to assist individuals with physical disabilities in performing daily activities, such as maneuvering an AI-powered wheelchair.