Online cranial nerve spike potential signal detection method, device and system based on adaptive threshold

Abstract

The invention discloses an online cranial nerve spike potential signal detection method, device and system based on an adaptive threshold, and the method comprises the steps: 1, determining an initial position, screening cranial nerve signals from the initial position according to an initialized signal minimum value and a signal difference value, obtaining a test sample, and determining a test period according to the length of the test sample; 2, taking the standard deviation of the first test sample in each test period as a noise band threshold value of each test period; 3, for each cranial neural signal in the test period, determining an adaptive threshold of the cranial neural signal according to the noise band threshold and a previous section of historical cranial neural signal until the current cranial neural signal; and 4, screening a reference brain neural signal point according to the brain neural signal and the corresponding adaptive threshold, and extracting a spike potential signal according to the reference brain neural signal point. Therefore, the detection accuracy is improved while the online detection calculation consumption is reduced.

Description

technical field [0001] The invention relates to the technical field of cranial nerve signal processing, in particular to an online cranial nerve spike signal detection method, device and system based on an adaptive threshold. Background technique [0002] In the field of brain neural signal processing technology, online processing of real-time sampled high-throughput signals is generally required. In neuroelectrophysiological experiments, a single electrode usually records the activity of more than one neuron, and due to the influence of noise signals such as myoelectric activity and electromagnetic interference, it is necessary for the spike signal detection algorithm to have strong anti-interference ability and low algorithm complexity And it can be calculated in real time, without using global information, and can process brain nerve signals online. [0003] The existing high-accuracy cranial nerve signal processing methods are mostly offline methods, common single-thres...

AI Technical Summary

Problems solved by technology

This method has weak anti-interference ability to large-value noise, and the ability to extract different neural signals is not strong

However, in order to combat the strong noise background and improve the signal-to-noise ratio, the energy-based method usually chooses to design a more complex energy operator, which takes a long time to calculate.

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