Study On EEG Analysis Methods Based On Parelle Computing

The human brain is a complex nonlinear system, nowadays, the research on EEG hasbecome an important frontier of life science. EEG signal processing is important forbrain-related disease detection, diagnosis and treatment, however, EGG studies involvingcollection and calculate a large number of EEG data, both storage and effectivemanagement of the complex and massive experimental data remains a grand challenge.The application of cutting-edge high performance computing techniques in addressingcompute and data intensive neuroscience problems is still in its infancy. This paper mainlyresearch on high-performance parallel computing application in EEG analysis based onstuding the different EEG singnal processing method. The high performance parallelcomputing technology in CPU thread pool and GPU multi-thread technology have beenselected to study parallel computing-intensive EEG signal processing methods to improvethe calculation accuracy and scale. The parallel design algorithm for EEG signalprocessing, assisted EEG signals research and analysis, to reduce researchers workload,shorten research cycle.First of all, the design approach and the design purpose of parallel computing havebeen studied. By investigating and surveying the methods and platform ofhigh-performance computing, this study chooses the suitable technical platform andproposes a proper solution: thread pool of CPU multi-thread technology and CUDA ofGPU multi-thread technology, respectively study the principle and performance of thesetwo methods, and develop the corresponding procedures.And then, this paper research the parallelization of single channel EEG signalprocessing method——EEMD, EEMD is very suitable for non-stationary, non-linear EEGsignals analysis, however, the EEMD is compute-intensive, the algorithm contains parallelcomposition, this article propose the parallelization in different levels of this algorithm,mainly using multi-thread technic of CPU and multi-thread parallel technic of GPU, usingthread pool technology and CUDA technology also. Compared to the previous parallelalgorithms, parallel algorithm execution efficiency has been greatly improved. Meanwhile, integrate the HHT method to analyze epilepsy signal, more physical properties has beenextracted and provided a basis for the epileptic prediction and diagnosis.At last, the parallelization of EEG signal analysis research extends to dual-channel,multi-channel, and the dissertation research a way of NLI algorithm parallelization. NLI isa non-symmetric and provides information about the direction of interdependence. It isclosely related to recent attempts to generalized synchronization. The NLI algorithm needsa large amount of computing to achieve the result. This paper using GPGPU technologywhich based on CUDA design parallel NLI algorithm of multi-channel EEG signal forvery fine-grained data and thread parallelism. NLI parallel study improves the efficiencyof the algorithm execution and the expansion of the algorithm application scales.Parallelization NLI algorithm can be applied in the direction of the coupling of themulti-channel EEG analysis and multi-channel synchronous intensity of the epileptic EEGanalysis integrating the S-estimator method. The method provides important informationfor studying the seizures and spreading mechanism.