English and Romanian Brain-to-Text Brain-Computer Interface Word Prediction System

• Published in: International journal of advanced computer science & applications Vol. 13; no. 10
• Main Authors: Ali, Haider Abdullah, Goga, Nicolae, Vasilateanu, Andrei, Muslim, Ali M., Ali, Khalid Abdullah, Dragoi, Marius-Valentin
• Format: Journal Article
• Language: English
• Published: West Yorkshire Science and Information (SAI) Organization Limited 2022
• Subjects: Classification; Disabilities; Discriminant analysis; Electrodes; Electroencephalography; English language; Feature extraction; Human-computer interface; Sentences; Support vector machines; Words (language)
• ISSN: 2158-107X; 2156-5570
• DOI: 10.14569/IJACSA.2022.0131003

Brain-Computer Interface (BCI) can recognise the thoughts of a human through various electrophysiological signals. Electrodes (sensors) placed on the scalp are used to detect these signals, or by using electrodes implanted inside the brain. Usually, BCI can detect brain activity through different neuroimage methods, but the most preferred is Electroencephalography (EEG) because it is a non-invasive and non-critical method. BCI systems applications are very helpful in restoring functionalities to people suffering from disabilities due to different reasons. In this study, a novel brain-to-text BCI system is presented to predict the word that the subject is thinking. This brain-to-text can assist mute people or those who cannot communicate with others due to different diseases to restore some of their abilities to interact with the surrounding environment and express themselves. In addition, brain-to-text may be used in different control or entertainment applications. EMOTIV™ Insight headset has been used to collect EEG signals from the subject’s brain. Feature extraction of EEG signals for BCI systems is very important to classification performance. Statistical-based feature extraction has been used in this system to extract valuable features to be used for classification. The datasets are sentences involving some commonly used words in English and Romanian languages. The results of the English language elucidated that K-Nearest neighbour (KNN) has a prediction accuracy of 86.7%, 86.1% for Support Vector Machine (SVM), and 79.2% for Linear discriminant analysis (LDA), while the Romanian language has a prediction accuracy of 96.1%, 97.1%, and 94.8% for SVM, LDA, and KNN respectively. This system is a step forward in developing advanced brain-to-text BCI prediction systems.