Online MEG Source Localization using High-Performance GPU Computing (OSL)

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Online localization of neuronal sources in the human brain from single trial magnetoencephalography (MEG) recently got into focus of neuroscience to clarify brain functionality, identify brain states and to improve real-time applications such as brain-computer interface (BCI) systems. By utilizing the graphics processing unit (GPU) and NVIDIAs computing engine CUDA, we aim to develop high performance optimized source localization algorithms allowing to perform online source localization on typically sized lead field matrices and noisy single trial data. First, we will optimize and parallelize the algorithms RAP-MUSIC, MNE and Beamformer. Secondly, we will develop a new multilayer hybrid inverse algorithm based on the algorithms mentioned above to reduce the computational effort and handle insufficient signal-to-noise ratio (SNR). Thirdly, we will include dynamic a priori information to increase accuracy and speed of online localization. We will provide an easy-to-use software framework and the developed algorithms in an open library, which will be a crucial requirement for online brain state classification in our partner project (proposal 'State-guided Perception', SGP). Patients will directly benefit from the new technology to improve adaptations of parameters of neuroimplants; improved rehabilitation outcome through online feedback in state guided learning and higher classification rates in brain computer interfaces.