Massively Parallel Neural Signal Processing on a

T he past decade has brought remarkable advances in the processing of neural signals—that is, neural activity signatures1 such as electroencephalogram (EEG), magnetoencephalography (MEG), electrocorticography (ECoG), magnetic resonance imaging (MRI), and functional MRI (fMRI). Neural signal analysis is vital in detecting, diagnosing, and treating brain disorders and related diseases.2 Neural signals are naturally nonlinear and nonstationary. Prior to the introduction of nonlinear methods, linear algorithms—such as shortFourier transform, Wigner-Ville distribution, and wavelet filtering—had been extensively used in spectral analysis of EEG recordings.3 A major shortfall of linear approaches is that neural signals’ temporal patterns, such as instantaneous amplitude and phase or frequency, can’t be accurately estimated.4 The recent advent of ensemble empirical mode decomposition (EEMD),2 in conjunction with Hilbert-Huang transform (HHT),5 has revolutionized the study of neural signals, giving neuroscientists an unrivaled opportunity to understand the true physical and neurophysiological meanings of neural signals. However, the EEMD algorithm’s complexity and neural signals’ routinely massive size hamper EEMD application in neural science research and practice. Here, we present an approach to this problem that uses a many-core platform (a GPU for home entertainment) that enables massively parallel neural signal processing. By analyzing a multichannel EEG of absence seizures, we demonstrate our approach’s efficiency and effectiveness in identifying the seizure state.