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

近年来，利用单次脑磁图（MEG）对人脑神经元源进行在线定位已成为神经科学研究的热点，其目的是阐明大脑功能、识别大脑状态以及改进脑机接口（BCI）系统等实时应用。通过利用图形处理单元（GPU）和nvidia的计算引擎CUDA，我们的目标是开发高性能优化的源定位算法，允许在典型大小的引线场矩阵和噪声单试验数据上执行在线源定位。首先，我们将优化和并行化RAP-MUSIC、MNE和Beamformer算法。其次，我们将在上述算法的基础上开发一种新的多层混合逆算法，以减少计算量并处理信噪比不足的问题。第三，我们将加入动态先验信息，以提高在线本地化的准确性和速度。我们将在开放库中提供易于使用的软件框架和开发的算法，这将是我们合作伙伴项目（提案“状态引导感知”，SGP）中在线大脑状态分类的关键要求。患者将直接受益于新技术，以提高神经植入物参数的适应性；状态引导学习在线反馈改善康复效果，提高脑机接口分类率。

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.