This paper presents a parallel algorithm using GPU for computer simulation of Electrocardiogram (ECG) based on a 3-dimensional (3D) whole-heart model. The computer heart model includes approximately 50,000 discrete elements (model cells) inside a torso model represented by 344 nodal points with 684 triangular meshes. After the excitation propagation is simulated in the heart model, the Poisson question is applied to the volume conductor for computing ECG, which involves a maximum of about 50,000 electric current dipole sources and four boundaries including the torso surface and three surfaces of heart model. The parallel algorithm was designed to solve this problem based on GPU. It accelerates the speed of calculation of ECG to 2.74 times with very low relative error compared with the serial algorithm. This study demonstrates a potential method using GPU for parallel computing in biomedical simulation study.
The concept of Brain-Computer Interface (BCI) has emerged over the last three decades as a promising alternative to the existing interface methods. However the BCI framework generally spoken only emphasizes on the aspects of BCI signal processing, lacking of the function of Visualization and Virtual Reality (VR) feedback. This paper designs a general and extendable framework which has the ability of offline, online analysis, visualization, and VR feedback. For the researchers, they can use it to analyze the online EEG signals, and observe the dynamic brain information of subjects. Meanwhile, the researchers can also do the offline analysis. For subjects, VR technology can provide a more secure and realistic environment for training and tuning neutrally controlled interfaces to real-world devices, such as wheelchairs. At last, the methods and algorithms used in the framework are also described.
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