Wang Zeyu, Juhasz Zoltan
Department of Electrical Engineering and Information Systems, University of Pannonia, 8200 Veszprem, Hungary.
Sensors (Basel). 2023 Oct 23;23(20):8654. doi: 10.3390/s23208654.
Time-frequency analysis of EEG data is a key step in exploring the internal activities of the human brain. Studying oscillations is an important part of the analysis, as they are thought to provide the underlying mechanism for communication between neural assemblies. Traditional methods of analysis, such as Short-Time FFT and Wavelet Transforms, are not ideal for this task due to the time-frequency uncertainty principle and their reliance on predefined basis functions. Empirical Mode Decomposition and its variants are more suited to this task as they are able to extract the instantaneous frequency and phase information but are too time consuming for practical use. Our aim was to design and develop a massively parallel and performance-optimized GPU implementation of the Improved Complete Ensemble EMD with the Adaptive Noise (CEEMDAN) algorithm that significantly reduces the computational time (from hours to seconds) of such analysis. The resulting GPU program, which is publicly available, was validated against a MATLAB reference implementation and reached over a 260× speedup for actual EEG measurement data, and provided predicted speedups in the range of 3000-8300× for longer measurements when sufficient memory was available. The significance of our research is that this implementation can enable researchers to perform EMD-based EEG analysis routinely, even for high-density EEG measurements. The program is suitable for execution on desktop, cloud, and supercomputer systems and can be the starting point for future large-scale multi-GPU implementations.
脑电图（EEG）数据的时频分析是探索人类大脑内部活动的关键步骤。研究振荡是该分析的重要组成部分，因为人们认为振荡为神经组件之间的通信提供了潜在机制。传统的分析方法，如短时傅里叶变换（Short-Time FFT）和小波变换，由于时频不确定性原理以及它们对预定义基函数的依赖，并不适合这项任务。经验模态分解（Empirical Mode Decomposition）及其变体更适合这项任务，因为它们能够提取瞬时频率和相位信息，但实际使用时耗时过长。我们的目标是设计并开发一种大规模并行且性能优化的基于图形处理器（GPU）的实现，该实现采用具有自适应噪声的改进完全集合经验模态分解（CEEMDAN）算法，可显著减少此类分析的计算时间（从数小时缩短至数秒）。最终得到的GPU程序已公开发布，通过与MATLAB参考实现进行对比验证，对于实际的EEG测量数据，加速比超过260倍；当有足够内存时，对于更长的测量数据，预测加速比在3000 - 8300倍之间。我们研究的意义在于，这种实现方式能够使研究人员常规地进行基于经验模态分解的EEG分析，即使是对于高密度EEG测量。该程序适用于在桌面、云端和超级计算机系统上执行，并且可以作为未来大规模多GPU实现的起点。
