Neuronal connectivity assessment for epileptic seizure prevention: Parallelizing the generalized partial directed coherence on many-core platforms

Abstract

Research on the prevention of epileptic seizures has led to approaches for future treatment techniques, which rely on the demanding computation of generalized partial directed coherence (GPDC) on electroencephalogram (EEG) data. A fast computation of such metrics is a key factor both for the off-line optimization of algorithmic parameters and for its real-time implementation. Aiming at speeding up the GPDC computations on EEG data, the current paper presents massively parallel computational strategies for implementing the GPDC on many-core architectures. We apply the proposed strategies on commercial and experimental many-core platforms and we compare the results of the computation time of a set of EEG data on the Bulldozer and Ivy Bridge x86_64 serial processors. We test the GPUs of nVidia GTX 550 Ti and GTX 670, which at the best case achieve a significant speedup of 190x and 460x respectively. Moreover, we apply the proposed parallelization strategies on the Single-Chip Cloud Computer (SCC), an experimental processor created by Intel Labs