OAP: An efficient online principal component analysis algorithm for streaming EEG data

Abstract: Data processing on streaming data poses computational as well as statistical challenges. Streaming data requires that data processing algorithms are able to process a new data point within micro-seconds. This is especially challenging on dimension reduction, where traditional methods as Principal Component Analysis (PCA) require eigenvectors decomposition of a matrix based on the complete dataset. So a proper online version of PCA should avoid this computational involved step in favor for a more efficient update rule. This is implemented by an algorithm named Online Angle Preservation (OAP), which is able to handle large dimensions in the required time limitations. This project presents an application of OAP in the case of Electroencephalography (EEG). For this, an interface was coded from an openBCI EEG device, through a Java API to a streaming environment called Stream Analyzer (sa.engine). The performance of this solution was compared to a standard Windowised PCA solution, indicating its competitive performance. This report details this setup and details the results.