High-performance DTW-based signals comparison for the brain electroencephalograms analysis

Automatic analysis of electroencephalograms (EEGs) is one of the promising areas of research, which results can be used, in particular, to build systems of mental control of objects. The Dynamic Time Warping procedure (DTW) is used in this work for comparing signals representing EEG. An important feature of the problem we are considering is the need for multiple comparison of signals at the stage of machine learning, which requires enormous computational costs. We propose a parallel algorithm that was implemented in C++ using the MPI technology and tested using the resources of the supercomputer complex of Moscow State University “Lomonosov”. The results of its testing on real data showed that the proposed method allows achieving an almost linear speedup and reducing the total calculation time from 29 days to 3.5 hours using 128 processes, which opens the possibility of improving the quality of automatic analysis of electroencephalograms.