Analysis of a Parallel and Distributed BPSO Algorithm for EEG Classification: Impact on Energy, Time and Accuracy

Today, the amount of data generated each year is growing exponentially, directly affecting the time required for its analysis. This problem worsens with high-dimensional datasets, such as those used in electroencephalography, so a good feature selection method and techniques that improve algorithms’ efficiency are increasingly relevant. Consequently, computing time and energy consumption are reduced, which could be used to explore more solutions to the problem. However, it is also necessary to adapt the applications to take advantage of the hardware offered by high-performance computing systems. Therefore, in this work, a parallel and distributed binary particle swarm optimization algorithm has been implemented, used as a feature selection method, and applied to two real electroencephalography datasets: the University of Essex dataset and the well-known BCI Competition IV 2a dataset. The proposed method has been analyzed in a multi-node computing cluster, not only in terms of classification accuracy, but also from the energy-time point of view to study its impact depending on different experimental conditions and datasets used.