Method for evaluating signal recording performance of nerve microelectrode

Abstract

The invention discloses a method for evaluating signal recording performance of a nerve microelectrode. The method comprises the following steps: putting the nerve microelectrode into artificial cerebrospinal fluid, and carrying out noise and signal level detection; carrying out surface cleaning on the nerve microelectrode, drying the nerve microelectrode, implanting the nerve microelectrode intoa specific brain area of a subject, and recording a local field potential of the subject, wherein data is the sum of the detected noise and signal levels of the nerve microelectrode; preprocessing a collected electric physiological signal so as to obtain a time domain waveform, and superposing and averaging detection signals so as to respectively obtain the noise level and the signal level; and quantizing a detection signal to noise ratio of the nerve microelectrode, so as to finish the evaluation of the performance of the nerve microelectrode. The invention relates to recording of a spontaneous potential of the nerve microelectrode in the electric physiological signal of an electroencephalogram nerve. The method can be applied to the evaluation of the performance of the nerve microelectrode and can be applied to comparison on the quality of detection signals of different nerve microelectrodes after the nerve microelectrodes are optimized or modified.

Description

technical field [0001] The invention belongs to the technical field of medical and physiological signal detection, processing and analysis, and more specifically relates to a method for evaluating the recording performance of nerve microelectrode signals. Background technique [0002] The development of neural microelectrodes has attracted the attention of the fields of biomedicine, neurobiology and related clinical medicine. For implantable neural microelectrodes, its biocompatibility and signal recording sensitivity are one of the key factors. Nerve electrophysiological activity is the basic functional attribute of all nerve cells, and it is also the physiological basis for the effective transmission of nerve information. Its waveform, amplitude, and frequency not only express the physiological state of a single nerve cell itself, but also carry the communication and interaction between cell groups. information. Therefore, the high-precision, high-throughput, and real-ti...

AI Technical Summary

Problems solved by technology

At present, some researchers have carried out quantitative research on the calculation method based on neuroelectrophysiological signal-to-noise ratio. Although this calculation method can extract the signal-to-noise ratio better, it must rely on a higher sampling rate, which improves the accuracy of the signal-to-noise ratio. The requirements of the acquisition system also increase the difficulty and amount of data in the data acquisition process, so there are still some limitations in the experiment process

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