FPGA (Field Programmable Gate Array) based electroencephalogram signal collection system and method

Abstract

The invention discloses an FPGA (Field Programmable Gate Array) based electroencephalogram signal collection system and method. The FPGA based electroencephalogram signal collection system comprises a multichannel signal receiving module, a parallel processing module, a signal filtering module, a peak detection module and a signal extraction module, wherein an output end of the multichannel signal receiving module is connected with an input end of the signal extraction module through the parallel processing module, the signal filtering module and the peak detection module in turn. According to the FPGA based electroencephalogram signal collection system and method, electroencephalogram signals are received through an FPGA based processing platform, the mean filtering and the peak detection are performed through a shifting register, the signal extraction is performed through a storage queue and a counter, and accordingly the filtering effect is achieved through less resources, useful signals are extracted from every channel, the flexibility is high, the high-speed parallel computing can be achieved, a plurality of channels can be processed in parallel, and accordingly the real-time collection and extraction can be achieved so as to perform the downstream module analysis and processing. The FPGA based electroencephalogram signal collection system and method can be widely applied to the signal analysis field.

Description

technical field [0001] The invention relates to the field of signal analysis, in particular to an FPGA-based electroencephalogram signal acquisition system and method. Background technique [0002] Studying the activity of the cerebral cortex, so as to understand the working principle of the brain and treat brain diseases, has important scientific significance and broad application prospects. However, when the number of bioelectrical signal channels in the cerebral cortex is large and the amount of information is large, how to ensure the real-time acquisition and extraction of large-scale signals has become an urgent problem to be solved. [0003] Traditional large-scale EEG signal acquisition and extraction can be divided into two categories: software and hardware methods. The software method has the advantages of low cost and simple operation. The disadvantage is that due to the limitation of computing speed, it cannot meet the online processing requirements for large-sca...

AI Technical Summary

Problems solved by technology

The disadvantage is that due to the limitation of computing speed, it cannot meet the online processing requirements for large-scale EEG signals; the hardware method mostly uses high-performance computers as the main processing unit, which has the advantage of being able to meet the online processing requirements of signals, and the disadvantage is that its portability is very poor And the configuration is complicated, the price is expensive, and the power consumption is too high, so it is limited to experimental research and difficult to realize as an industrialization

Traditional EEG collection and extraction methods can only process up to 1,000 channels, which cannot meet the needs of the hundreds of millions of active cells in the cerebral cortex.

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