High throughput wavelet coherence analysis of neural series Online publication date: Wed, 19-Nov-2014
by Jiaqing Yan; Dan Chen; Yinghua Wang; Yao Wang; Gaoxiang Ouyang; Xiaoli Li
International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC), Vol. 17, No. 2/3, 2014
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.
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