EEG-based classification of bilingual unspoken speech using ANN

• Published in: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2017; pp. 1022 - 1025
• Main Authors: Balaji, Advait, Haldar, Aparajita, Patil, Keshav, Ruthvik, T. Sai, Ca, Valliappan, Jartarkar, Mayur, Baths, Veeky
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.07.2017
• Subjects: Electroencephalography; Radio frequency; Sensitivity; Sensors; Speech; Support vector machines; Training
• ISSN: 1557-170X
• DOI: 10.1109/EMBC.2017.8037000

The ability to interpret unspoken or imagined speech through electroencephalography (EEG) is of therapeutic interest for people suffering from speech disorders and `lockedin' syndrome. It is also useful for brain-computer interface (BCI) techniques not involving articulatory actions. Previous work has involved using particular words in one chosen language and training classifiers to distinguish between them. Such studies have reported accuracies of 40-60% and are not ideal for practical implementation. Furthermore, in today's multilingual society, classifiers trained in one language alone might not always have the desired effect. To address this, we present a novel approach to improve accuracy of the current model by combining bilingual interpretation and decision making. We collect data from 5 subjects with Hindi and English as primary and secondary languages respectively and ask them 20 `Yes'/`No' questions (`Haan'/`Na' in Hindi) in each language. We choose sensors present in regions important to both language processing and decision making. Data is preprocessed, and Principal Component Analysis (PCA) is carried out to reduce dimensionality. This is input to Support Vector Machine (SVM), Random Forest (RF), AdaBoost (AB), and Artificial Neural Networks (ANN) classifiers for prediction. Experimental results reveal best accuracy of 85.20% and 92.18% for decision and language classification respectively using ANN. Overall accuracy of bilingual speech classification is 75.38%.