Dynamic Sparse Matrix Compression Algorithms for Brain-computer Interface Systems
In brain-computer interface systems,multi-channel neural signal acquisition is a core functional module,which can col-lect much neural information in human brain for external computer equipment.In the multi-channel neural signal acquisition,because of the huge amount of original data,the generated original data is directly transferred and processed to take huge power consumption and increase the difficulty of hardware design.To solve this problem,an effective method is to compress the original neural signal data according to its characteristics before the data transmission and processing.The neuron action potential signal has the refractory peri-od property,that is,the effective signal has the very low ratio of time domain width to repetition period.This paper adopts this fea-ture to define the digital label output of the multi-channel neural signals as a sparse matrix in a certain time range,extracts the feature of this sparse matrix,and dynamically uses the optimization algorithm to compress the data according to its feature.The proposed al-gorithm is designed and implemented on a Xilinx platform using FPGA,and taking it as the central control hardware to pass the real-time verification on the 32 channels neural signal acquisition hardware system.Experiments show that the proposed dynamic sparse matrix compression algorithm can achieve a data compression rate of 83.4％.
neural signal acquisitionmulti-channelsparse matrixdata compression algorithmsFPGA
万方数据
维普
