Supercomputer Supported Online Deep Learning Techniques for High Throughput EEG Prediction

Xiang Li, Yazhou Zhang, Jing Li

Abstract: Electroencephalogram (EEG) is a precise reflection of the brain activities and has been widely studied in clinical medicine, neuroscience, brain interface, etc. Intelligent prediction of the EEG’s evolution accurately plays important roles in several application areas, such as epilepsy seizure forecasting and neonatal brain monitoring. Nevertheless, when the prediction service is deployed as a business service on the Cloud and open for public usage, there are several problems that need to be resolved: (i) how to design a computation platform to process the high-throughput multi-source EEG data, which arrives sequentially and increases rapidly when the services are rapidly promoted, namely tackling the ‘high-throughput computing’ problem; (ii) how to develop a deep learning model to capture the complex EEG distribution as well as the anomaly patterns that could evolve dynamically, namely tackling the ‘concept drift’ problem for non-stationary EEG signals. To tackle these challenges, we propose an Evolutive Convolutional Neural Network (ECNN) and the corresponding supercomputer supported distributed computation system. The ECNN model can dynamically reweighting the sub-structure of the model from data streams in an online learning fashion, by which the capacity scalability and sustainability are introduced into the model. As far as we know, it is the first work that introduce supercomputer supported online deep learning techniques into EEG prediction research.