Massively Parallel Signal Processing Using the Graphics Processing Unit for Real-Time Brain-Computer Interface Feature Extraction

• Published in: Frontiers in neuroengineering Vol. 2; p. 11
• Main Author: Wilson, J. Adam
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 2009
• Subjects: Neuroscience; CUDA; brain–computer interface; BCI2000; NVIDIA; parallel processing
• ISSN: 1662-6443; 1662-6443
• DOI: 10.3389/neuro.16.011.2009

The clock speeds of modern computer processors have nearly plateaued in the past 5 years. Consequently, neural prosthetic systems that rely on processing large quantities of data in a short period of time face a bottleneck, in that it may not be possible to process all of the data recorded from an electrode array with high channel counts and bandwidth, such as electrocorticographic grids or other implantable systems. Therefore, in this study a method of using the processing capabilities of a graphics card [graphics processing unit (GPU)] was developed for real-time neural signal processing of a brain-computer interface (BCI). The NVIDIA CUDA system was used to offload processing to the GPU, which is capable of running many operations in parallel, potentially greatly increasing the speed of existing algorithms. The BCI system records many channels of data, which are processed and translated into a control signal, such as the movement of a computer cursor. This signal processing chain involves computing a matrix-matrix multiplication (i.e., a spatial filter), followed by calculating the power spectral density on every channel using an auto-regressive method, and finally classifying appropriate features for control. In this study, the first two computationally intensive steps were implemented on the GPU, and the speed was compared to both the current implementation and a central processing unit-based implementation that uses multi-threading. Significant performance gains were obtained with GPU processing: the current implementation processed 1000 channels of 250 ms in 933 ms, while the new GPU method took only 27 ms, an improvement of nearly 35 times.