The primary goal of this research was to develop a system capable of predicting a given user's imagined language in real time using their brainwave data. This research analyzed both FPGA-based and software based approaches to real-time classification of imagined language. The classification was binary between English and Japanese, and a dataset containing imagined speech from both languages was also created. Another goal of this research was to consider the effects of quantization of the network weights in order to examine the resulting utilization of the FPGA to allow for other applications to run in conjunction with our proposed system. With test accuracies over 95% but real-time accuracies only barely approaching 60%, it can be considered partly successful. Real-time approaches to predicting imagined EEG words are rare, and attempts at predicting imagined language are even rarer. Such a system could be beneficial in helping multi-lingual environments that standard natural language processing systems have difficulty in noticing changes in language, especially those that occur in real time. Further iterations on this proposed system could also assist those who have difficulty articulating speech and would benefit from having a brainwave-based system that is portable and works in real-time. It is hopeful that this work can lead to future iterations and advancements in the realm of real-time imagined speech classification both through their own attempts or perhaps with the help of the English/Japanese imagined speech dataset created through this research.

Library of Congress Subject Headings

Electroencephalography--Data processing; Language and languages--Classification

Publication Date


Document Type


Student Type


Degree Name

Computer Engineering (MS)

Department, Program, or Center

Computer Engineering (KGCOE)


Cory E. Merkel

Advisor/Committee Member

Minoru Nakazawa

Advisor/Committee Member

Andres Kwasinski

JZonghiSupplement.zip (62851 kB)


RIT – Main Campus

Plan Codes