A Real Time EEG Analysis System

Electroencephalographic (EEG) data modeling is useful for developing applications in the areas of healthcare, as well as in the design of brain-computer interface (BCI). We built a system for brain state modeling, which includes a web server that can process uploaded electroencephalographic (EEG) data, store the data in a local database, and perform data analysis on the stored EEG data. This paper introduces a mobile application that is able to interact with the web server to render selected data and display analysis results from the web server. We aim to build an efficient self-adjusting brain wave modeling system that can seamlessly capture and analyze EEG brainwave data. The platform provides user friendly interface with secure data storage and analytics capabilities for wave analysis, statistical analysis, and categorical classification using a number of well-established machine learning algorithms. We also present a systematic method to understand how the variation of raw data sets used in training models affects the accuracy of machine learning algorithms, and then analyze the performance of machine learning algorithms under various computational implementations. Overall, the study describes a successfully built incorporated data analysis platform, and provides preliminary insights into the performance of common machine learning algorithms on the brain wave data sets.