Title: High throughput wavelet coherence analysis of neural series

Authors: Jiaqing Yan; Dan Chen; Yinghua Wang; Yao Wang; Gaoxiang Ouyang; Xiaoli Li

Addresses: Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China ' School of Computer Science, China University of Geosciences, Wuhan 430074, China ' State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China ' State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China ' State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China ' State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China

Abstract: The real-time estimation of coherence amongst neural signals from different brain areas is a critical issue in understanding brain functions. The wavelet coherence based on Monte Carlo method (MC-WTC) is effective in measuring the time-frequency coherence of neural signals, but it generates large intermediate data and could not be applied in real-time neural signal analysis. We develop a parallelised MC-WTC method with general-purpose computing on the graphics processing unit (GPGPU), namely G-MC-WTC, which speeds up the calculations using the CUDA toolkit. Simulation data showed that it can improve the runtime performance by almost 200 times. This method has been applied to a visual-auditory EEG data and to obtain the coherence information between different brain areas in real time. The result revealed a coherence difference in θ band at left temporal lobe. This method may become a useful tool for studying the cooperation mechanisms of brain regions in cognitive processes.

Keywords: WTC; wavelet coherence; Monte Carlo method; GPGPU; general-purpose computing; graphics processing unit; GPU; electroencephalograms; visual-auditory EEG; neural networks; neural signals; brain functions; simulation; cognitive processes.

DOI: 10.1504/IJAHUC.2014.065777

International Journal of Ad Hoc and Ubiquitous Computing, 2014 Vol.17 No.2/3, pp.134 - 143

Received: 10 Jul 2013
Accepted: 08 Jan 2014
Published online: 19 Nov 2014 *

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