Analyzing Electroencephalograms Using Cloud Computing Techniques

Brain Computer Interfaces (BCIs) allow users to interact with a computer via electroencephalogram (EEG) signals generated by their brain. The BCI application that we consider allows a user to initiate actions such as keyboard input or control the motion of their wheelchair. Our goal is to be able to train the neural network and classify the EEG signals from multiple users to infer their intended actions in a distributed environment. The processing is developed using the Map-Reduce framework. We use our cloud runtime, Granules, to classify these EEG streams. One of our objectives is to be able to process these EEG streams in real-time. The BCI software has been developed in R, which is an interpreted language designed for the fast computation of matrix multiplications, making it an effective language for the development of artificial neural networks. We contrast our approach of using Granules with a competing approach that uses an R package - Snowfall that simplifies execution of R computations in a distributed setting. We have performed experiments to evaluate the costs introduced by our scheme for training the neural networks and classifying the EEG signals. Our results demonstrate the suitability of using Granules to classify multiple EEG streams in a distributed environment.