Research On Compressed Sensing Technology Of Multichannel Eeg Signal

With the development of microelectronics,biomedicine and BCI,monitoring physiological signal such as EEG and ECG remotely by utilizing wireless body network is gradually becoming normal.However,the long-term monitoring of the multi-channel EEG signal in the wireless network is still limited by the bandwidth of the data transmission node and the power consumption of the system.C onventional data compression methods are computationally complex and difficult to implement on sensor nodes with weak computing power.Fortunately,the front-end compression coding of compressed sensing theory is computationally simple,and the complex computation is mainly focused on the back-end reconstruction.This makes compressed sensing suitable for the front-end multi-sensor nodes which have weak computing power and high real-time requirements.Therefore,it is significant to study the multi-channel EEG-based compression sensing technology.The purpose of this paper is to design a FPGA-based EEG signal compression acquisition system on the basis of the compressive sensing theory and the characteristics of EEG signal.This system can achieve real-time acquisition and compression for multi-channel EEG signal,and solve problems such as low reconstruction precision,high hardware storage space and poor channel expansion in the current compressed sensing methods.Firstly,we study the multi-channel EEG-based compressed sensing method,including analyzing the principle and characteristics of compressed sensing,and the sparsity of EEG signal,designing simulation AIC model and digital measurement matrix model,and verifying OMP and SOMP reconstruction algorithm.Secondly,a FPGA-based compression acquisition system is designed and implemented to achieve multi-channel EEG signal real-time acquisition and compression.It is worth mentioning that hardware acquisition module in this system uses ADS1299 as EEG acquisition circuit and firmware design includes acquisition,storage,compression,transmission and other units.Thirdly,software for multi-channel EEG signal reconstruction is designed and realized by using Lab VIEW and MATLAB mixed programming method.This software which includes data reception and reconstruction module can achieve real-time reading FPGA compression data and reconstructing signal through the host computer.Finally,the functional modules of the system are debugged and validated,the performance and precision of the EEG compression system are evaluated,and the influence of the data compression on the accuracy of the EEG-based emotion recognition is discussed.The experimental results show that our FPGA-based EEG signal compression acquisition system can realize the real-time compression acquisition of multi-channel EEG signal.The average SNDR of the 2 times compression rate SOMP is 21.74 dB.For EEG-based emotion recognition classification,the original average accuracy is 52.2%,and the average classification result of 2 times compression and reconstruction data is 52.1%.Obviously,the 2 times compression reconstruction has little effect on the EEG-based emotion recognition classification.