Abstract
A data-driven prototype software is presented for EEG processing and visualization. The system relies on the GPU architecture for providing simultaneous processing and visualization of the EEG data. Two example brain imaging algorithms, the surface Laplacian and the spherical forward solution are used for illustrating the effective use of the massively parallel GPU hardware in speeding up computations. The paper describes the architecture of our system, the key design decisions, and the performance optimization of the parallel implementation. Using the CUDA-OpenGL interoperability, the computing subsystem can directly modify potential data in the OpenGL vertex memory, avoiding unnecessary GPU-Host data transfers. The system and our parallel implementations demonstrate that real-time processing and visualization is possible for a range of algorithms during EEG processing. We are confident that these results can pave the way for supercomputing-class implementations and open up new opportunities in the clinical practice and neuroscience research.
Published Version
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