High throughput wavelet coherence analysis of neural series. (21st November 2014) - Yan, Jiaqing
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.