Neural sensor testing device and method

Abstract

This application belongs to the field of neural sensor testing technology, and specifically discloses a neural sensor testing device and method. The device includes: a standard neural signal simulation module, a neural sensor to be tested, a signal preprocessing unit and a computer control terminal connected in sequence; the computer control terminal is connected to the standard neural signal simulation module; the standard neural signal simulation module is used to output a standard neural signal according to a preset output mode, and apply the standard neural signal to the microelectrode area of ​​the neural sensor to be tested, so that the neural sensor to be tested outputs multiple neural electrical signals; the signal preprocessing unit is used to perform signal preprocessing on each neural electrical signal, and output the neural electrical signal to be tested corresponding to each neural electrical signal; the computer control terminal is used to compare the target discharge parameters of each neural electrical signal to be tested with the standard neural signal. Through this application, effective testing of MEMS neural sensors can be achieved.

Description

Technical Field

[0001] The present application belongs to the field of neural sensor testing technology, and more specifically, relates to a neural sensor testing device and method. Background Art

[0002] Micro-Electro-Mechanical Systems (MEMS) neural sensors are integrated chips that utilize micro-nanofabrication technology to arrange multiple micron-scale electrodes in an array, enabling high-throughput neural cell information detection. In recent years, with the rapid development of neuroscience, brain-computer interfaces, and other related sciences and technologies, MEMS neural sensors have entered the public eye and are finding widespread application in scientific research, healthcare, and other fields.

[0003] Currently, MEMS neural sensors are experiencing rapid development, but their manufacturing yield is relatively low. Furthermore, because the amplitude of neural electrical signals is very small, the use of these sensors can lead to errors in detection or inaccurate measurements. Furthermore, due to the high cost, time consumption, and difficulty of biological experiments, the use of unreliable MEMS neural sensors is bound to result in significant losses.

[0004] However, there is little research on MEMS neural sensor testing in the existing technology. How to achieve effective testing of MEMS neural sensors has become a technical problem that needs to be solved urgently in the industry. This is crucial to ensure the normal operation of MEMS neural sensors and to obtain neural information conveniently and reliably. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the purpose of this application is to achieve effective testing of MEMS neural sensors.

[0006] To achieve the above objectives, in a first aspect, the present application provides a neural sensor testing device, comprising:

[0007] A standard neural signal simulation module, a neural sensor to be tested, a signal preprocessing unit and a computer control terminal are connected in sequence; the computer control terminal is connected to the standard neural signal simulation module;

[0008] The standard neural signal simulation module is used to output a standard neural signal according to a preset output mode, and apply the standard neural signal to the microelectrode area of ​​the neural sensor to be tested, so that the neural sensor to be tested outputs multiple neural electrical signals;

[0009] The signal preprocessing unit is used to perform signal preprocessing on each channel of the neural electrical signal and output a neural electrical signal to be tested corresponding to each channel of the neural electrical signal;

[0010] The computer control terminal is used to compare the target discharge parameters of each of the neural electrical signals to be tested with the standard neural signal; the target discharge parameters are determined according to the output mode of the standard neural signal simulation module.

[0011] Optionally, the signal preprocessing unit includes an analog switch array, a neural electrical signal processing module, and a multi-channel data sampling module connected in sequence; the input end of the analog switch array is connected to the output end of the neural sensor to be tested, and the output end of the multi-channel data sampling module is connected to the input end of the computer control end;

[0012] The analog switch array is used to conduct the analog switch channels corresponding to the neural sensors to be tested, so as to transmit the multiple neural electrical signals to the neural electrical signal processing module;

[0013] The neural electrical signal processing module is used to amplify and filter each channel of the neural electrical signal to obtain a multi-channel processed neural electrical signal;

[0014] The multi-channel data sampling module is used to sample the processed neural electrical signals of each channel and output the corresponding neural electrical signals to be tested.

[0015] Optionally, the computer control terminal is connected to the analog switch array; the computer control terminal is used to configure the on / off state of each analog switch channel in the analog switch array.

[0016] Optionally, the computer control end is connected to the multi-channel data sampling module; the computer control end is used to configure sampling parameters of the multi-channel data sampling module.

[0017] Optionally, the neural electrical signal processing module includes multiple signal processing branches, each of which includes an amplifier, a filter circuit, and an analog-to-digital converter connected in sequence; the amplifier is used to perform differential amplification processing on the input neural electrical signal and the reference signal; the filter circuit is used to perform filtering processing on the output signal of the amplifier; and the analog-to-digital converter is used to perform analog-to-digital conversion processing on the output signal of the filter circuit;

[0018] Among them, the positive input end of the amplifier in each signal processing branch is connected to the corresponding analog switch channel in the analog switch array for inputting neural electrical signals; the negative input end of the amplifier in each signal processing branch is respectively connected to the reference signal output end of the neural sensor to be tested for inputting the reference signal output by the reference signal output end.

[0019] Optionally, the device further comprises:

[0020] A probe station; a probe array is provided on the probe station;

[0021] One side of the probe array is connected to the standard neural signal simulation module; the other side of the probe array is connected to the neural sensor to be tested.

[0022] Optionally, the characteristic size of the microelectrode of the neural sensor to be tested ranges from 1 μm to 100 μm, and the characteristic size of the tip of each probe in the probe array ranges from 10 μm to 50 μm.

[0023] Optionally, the output mode of the standard neural signal simulation module includes a single-point random discharge mode; the target discharge parameters include a discharge rate, a discharge amplitude, and a discharge timestamp;

[0024] The computer control terminal is used for:

[0025] Determining the discharge rate information, discharge amplitude information and discharge timestamp information of each channel of the neural electrical signal to be tested;

[0026] Comparing the discharge rate information of each channel of the neural electrical signal to be tested with the discharge rate information of the standard neural signal;

[0027] Comparing the discharge amplitude information of each channel of the neural electrical signal to be tested with the discharge amplitude information of the standard neural signal;

[0028] The discharge timestamp information of each channel of the neural electrical signal to be tested is compared with the discharge timestamp information of the standard neural signal.

[0029] Optionally, the output mode of the standard neural signal simulation module includes a cluster discharge mode; the target discharge parameters include discharge rate information, discharge amplitude information and peak potential interval;

[0030] The computer control terminal is used for:

[0031] Determining the discharge rate information, discharge amplitude information and peak potential interval information of each channel of the neural electrical signal to be tested;

[0032] Comparing the discharge rate information of each channel of the neural electrical signal to be tested with the discharge rate information of the standard neural signal;

[0033] Comparing the discharge amplitude information of each channel of the neural electrical signal to be tested with the discharge amplitude information of the standard neural signal;

[0034] The peak potential interval information of each channel of the neural electrical signal to be tested is compared with the peak potential interval information of the standard neural signal.

[0035] In a second aspect, the present application provides a neural sensor testing method applied to any of the aforementioned neural sensor testing devices, comprising:

[0036] Outputting a standard neural signal according to a preset output mode of the standard neural signal simulation module, and applying the standard neural signal to the microelectrode area of ​​the neural sensor to be tested, so that the neural sensor to be tested outputs multiple neural electrical signals;

[0037] Performing signal preprocessing on each channel of the neural electrical signal, and outputting a neural electrical signal to be measured corresponding to each channel of the neural electrical signal;

[0038] Each of the neural electrical signals to be tested is subjected to a comparison test of target discharge parameters with the standard neural signal; the target discharge parameters are determined according to the output mode of the standard neural signal simulation module.

[0039] In general, the above technical solutions conceived by this application have the following beneficial effects compared with the existing technologies:

[0040] The present application provides a neural sensor testing device and method, which includes a standard neural signal simulation module, a neural sensor to be tested, a signal preprocessing unit and a computer control terminal connected in sequence; a standard neural signal is output through the standard neural signal simulation module, and the standard neural signal is applied to the microelectrode area of ​​the neural sensor to be tested, so as to stimulate the neural sensor to be tested to output multiple neural electrical signals; at the same time, each neural electrical signal is subjected to preprocessing processes such as signal amplification and filtering through the signal preprocessing unit, and a neural electrical signal to be tested with a high signal-to-noise ratio is output; each neural electrical signal to be tested is then input into the computer control terminal, and each neural electrical signal to be tested is subjected to a target discharge parameter comparison test with the standard neural signal, thereby realizing effective testing of MEMS neural sensors, filling the gap in the current MEMS neural sensor testing field, and saving economic and time costs for the scientific research and application of MEMS neural sensors. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is one of the structural diagrams of the neural sensor testing device provided in the embodiments of the present application;

[0042] Figure 2 This is the second structural diagram of the neural sensor testing device provided in an embodiment of the present application;

[0043] Figure 3 Schematic diagram of the circuit structure of the signal processing branch in the neural electrical signal processing module provided in an embodiment of the present application;

[0044] Figure 41 is a flow chart of a neural sensor testing method provided in an embodiment of the present application;

[0045] Figure 5 It is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0047] In the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0048] In the description of the embodiments of the present application, unless otherwise specified, "multi-channel" means two or more channels. For example, multi-channel neural electrical signals refer to neural electrical signals transmitted through two or more channels; multi-channel signal processing branches refer to two or more circuit branches used for neural electrical signal processing.

[0049] The embodiments of the present application are described below in conjunction with the drawings in the embodiments of the present application.

[0050] Figure 1 This is one of the structural diagrams of the neural sensor testing device provided in the embodiment of the present application. Figure 1 As shown, the device includes:

[0051] A standard neural signal simulation module 1, a neural sensor to be tested 2, a signal preprocessing unit 3 and a computer control terminal 4 are connected in sequence; the computer control terminal 4 is connected to the standard neural signal simulation module 1;

[0052] The standard neural signal simulation module 1 is used to output a standard neural signal according to a preset output mode, and apply the standard neural signal to the microelectrode area of ​​the neural sensor 2 to be tested, so that the neural sensor 2 to be tested outputs multiple neural electrical signals;

[0053] The signal preprocessing unit 3 is used to perform signal preprocessing on each neural electrical signal and output the neural electrical signal to be tested corresponding to each neural electrical signal;

[0054] The computer control terminal 4 is used to compare the target discharge parameters of each neural electrical signal to be tested with the standard neural signal; the target discharge parameters are determined according to the output mode of the standard neural signal simulation module 1.

[0055] Specifically, the standard neural signal simulation module described in the embodiments of this application refers to a module for simulating communication and signal transmission between neurons. It can specifically use a simulator based on an artificial neuron model and can output a standard nerve cell action potential waveform signal, i.e., a standard neural signal. The amplitude of the standard neural signal ranges from 90mV to 130mV, which includes stages such as depolarization, repolarization, and hyperpolarization, and the duration of a single waveform can reach 0.5ms to 2.0ms.

[0056] The output mode described in the embodiment of the present application refers to the output mode of a standard neural signal simulation module, which can specifically include two output modes, namely, a single-point random discharge mode and a cluster discharge mode.

[0057] The neural sensor to be tested described in the embodiments of the present application refers to a MEMS neural sensor that needs to be tested, which can include two types: implantable microelectrode arrays and ex vivo microelectrode arrays, and the number of channels can be 128 or other configurable channel numbers.

[0058] The neural electrical signal to be tested described in the embodiment of the present application refers to the neural electrical signal generated by the neural sensor to be tested when it is stimulated by the standard neural signal, and is obtained through pre-processing processes such as signal amplification and filtering.

[0059] The target discharge parameters described in the embodiments of the present application refer to the discharge attribute parameters of the neural electrical signal, which can be specifically determined based on the output mode of the standard neural signal simulation module. For example, when the standard neural signal simulation module outputs a standard neural signal in a single-point random discharge mode, the target discharge parameters may include parameters such as discharge rate and discharge amplitude; when the standard neural signal simulation module outputs a standard neural signal in a clustered discharge mode, the target discharge parameters may include not only discharge rate and discharge amplitude parameters but also peak potential interval parameters.

[0060] Furthermore, in an embodiment of the present application, a neural sensor testing device is constructed using a standard neural signal simulation module 1, a neural sensor to be tested 2, a signal preprocessing unit 3 and a computer control terminal 4 connected in sequence, wherein the computer control terminal 4 is connected to the standard neural signal simulation module 1 for receiving standard neural signals for testing and analysis.

[0061] Optionally, the standard neural signal simulation module 1 includes a control terminal, which can be connected to the computer control terminal 4. Therefore, the output mode of the standard neural signal simulation module 1 can be directly configured through the computer control terminal 4, which facilitates the simplification of the circuit structure.

[0062] In the embodiments of the present application, the output mode of the standard neural signal simulation module can be pre-set based on actual test requirements, selecting either a single-point random discharge mode or a clustered discharge mode. Furthermore, the standard neural signal simulation module outputs a standard neural signal according to the preset output mode and applies the standard neural signal to the microelectrode region of the neural sensor 2 to be tested. After being stimulated by the standard neural signal, the microelectrode region of the neural sensor 2 to be tested can output multiple neural electrical signals through internal signal channels.

[0063] In an embodiment of the present application, the multiple neural electrical signals output by the neural sensor 2 to be tested will be further transmitted to the signal preprocessing unit 3. The signal preprocessing unit 3 performs preprocessing processes such as signal acquisition, amplification and filtering on each neural electrical signal through an internal switching circuit, amplification circuit, filtering circuit, etc. with multiple channel branches, and finally outputs the neural electrical signal to be tested corresponding to each neural electrical signal.

[0064] Furthermore, in an embodiment of the present application, the neural electrical signal to be measured output by the signal preprocessing unit 3 will be transmitted to the computer control terminal 4. The computer control terminal 4 can display and selectively store the measured signal in real time and process and analyze the signal data according to a pre-set program.

[0065] Specifically, the computer control terminal 4 compares the target discharge parameters of each neural electrical signal under test with the received standard neural signal. For example, in the single-point random discharge mode, the computer control terminal 4 can compare and analyze the discharge rate, discharge amplitude, and other parameters of each neural electrical signal under test with the standard neural signal; in the cluster discharge mode, the computer control terminal 4 can compare and analyze the discharge rate, discharge amplitude, and inter-peak interval of each neural electrical signal under test with the standard neural signal. Furthermore, based on the comparison results of each target discharge parameter and combined with empirical indicators, the quality of the neural signal sensing performance of the neural sensor 2 under test can be determined, and ultimately the manufacturing quality of the neural sensor 2 under test can be determined.

[0066] The neural sensor testing device of the embodiment of the present application includes a standard neural signal simulation module, a neural sensor to be tested, a signal preprocessing unit and a computer control terminal connected in sequence; a standard neural signal is output through the standard neural signal simulation module, and the standard neural signal is applied to the microelectrode area of ​​the neural sensor to be tested, so as to stimulate the neural sensor to be tested to output multiple neural electrical signals; at the same time, each neural electrical signal is subjected to preprocessing processes such as signal amplification and filtering through the signal preprocessing unit, and a neural electrical signal to be tested with a high signal-to-noise ratio is output; each neural electrical signal to be tested is then input into the computer control terminal, and each neural electrical signal to be tested is subjected to a comparison test of target discharge parameters with the standard neural signal, thereby realizing effective testing of MEMS neural sensors, filling the gap in the current MEMS neural sensor testing field, and saving economic and time costs for the scientific research and application of MEMS neural sensors.

[0067] Figure 2 This is the second structural diagram of the neural sensor testing device provided in the embodiment of the present application. Figure 2 As shown, as an optional embodiment, the signal preprocessing unit 3 includes an analog switch array 31, a neural electrical signal processing module 32, and a multi-channel data sampling module 33 connected in sequence; the input end of the analog switch array 31 is connected to the output end of the neural sensor 2 to be tested, and the output end of the multi-channel data sampling module 33 is connected to the input end of the computer control terminal 4;

[0068] The analog switch array 31 is used to conduct the analog switch channels corresponding to the neural sensors 2 to be tested, so as to transmit the multiple neural electrical signals to the neural electrical signal processing module;

[0069] The neural electrical signal processing module 32 is used to amplify and filter each neural electrical signal to obtain a multi-channel processed neural electrical signal;

[0070] The multi-channel data sampling module 33 is used to sample the processed neural electrical signals of each channel and output the corresponding neural electrical signals to be tested.

[0071] Specifically, in an embodiment of the present application, the signal preprocessing unit 3 includes an analog switch array 31, a neural electrical signal processing module 32, and a multi-channel data sampling module 33, which are connected in sequence. Among them, the analog switch array 31 may include multiple analog switch channels, such as 128 analog switch channels. One end of the analog switch channel is connected to the neural sensor 2 to be tested, and the other end is connected to the neural electrical signal detection module 32. Each analog switch channel includes a neural electrical signal switch and a reference ground. Of course, it can also only include a neural electrical signal switch to transmit and block neural electrical signals.

[0072] Thus, the analog switch array 31 can transmit the multiple neural electrical signals output by the neural sensor 2 to be tested to the neural electrical signal processing module by turning on the analog switch channels corresponding to the neural sensors 2 to be tested.

[0073] In addition, it should be noted that 128 analog switch channels are only a common setting method. Those skilled in the art can set the number of analog switch channels included in the analog switch array 31 according to their own testing needs, and this application does not make any specific limitations on this.

[0074] Optionally, the number of analog switch channels in the analog switch array 31 can be smaller than the number of signal channels in the neural sensor 2 under test. For example, if the neural sensor 2 under test has 64 signal channels and the analog switch array 31 has 32 signal channels, the test can be performed in batches, with the first 32 channels of the neural sensor 2 under test being tested, followed by the second 32 channels. Therefore, it can be understood that if the number of analog switch channels in the analog switch array 31 is not smaller than the number of signal channels in the neural sensor 2 under test, there is no need to perform multiple tests using the analog switch array 31.

[0075] Continue to refer to Figure 2 As an optional embodiment, the computer control terminal 4 is connected to the analog switch array 31; the computer control terminal 4 is used to configure the open and close states of each analog switch channel in the analog switch array 31.

[0076] Specifically, in an embodiment of the present application, the analog switch array 31 is provided with a control terminal, which can be connected to the computer control terminal 4. In this way, the opening and closing states of each analog switch channel in the analog switch array 31 can be conveniently configured through the computer control terminal 4, which is conducive to simplifying the circuit structure of the device and improving the convenience of neural sensor testing.

[0077] In an embodiment of the present application, the input end of the neural electrical signal processing module 32 is connected to the analog switch array 31, and is used to amplify and filter the neural electrical signals transmitted by the analog switch array 31 to improve the signal-to-noise ratio.

[0078] Based on the content of the above embodiment, as an optional embodiment, the neural electrical signal processing module 32 includes multiple signal processing branches, each signal processing branch includes an amplifier, a filter circuit and an analog-to-digital converter connected in sequence; the amplifier is used to perform differential amplification processing on the input neural electrical signal and the reference signal; the filter circuit is used to perform filtering processing on the output signal of the amplifier; and the analog-to-digital converter is used to perform analog-to-digital conversion processing on the output signal of the filter circuit;

[0079] Among them, the positive input end of the amplifier in each signal processing branch is connected to the corresponding analog switch channel in the analog switch array 31 for inputting neural electrical signals; the negative input end of the amplifier in each signal processing branch is respectively connected to the reference signal output end of the neural sensor 2 to be tested for inputting the reference signal output from the reference signal output end.

[0080] Specifically, in the embodiment of the present application, the circuit structure diagram of each signal processing branch of the neural electrical signal processing module 32 is as follows: Figure 3 As shown, it consists of three parts: an amplifier, a filter circuit, and an analog-to-digital converter (ADC). The positive input of the amplifier in each signal processing branch is connected to the corresponding analog switch channel in the analog switch array 31 to input the neural electrical signal; the negative input of the amplifier in each signal processing branch is connected to the reference signal output of the neural sensor 2 to input the reference signal (REF).

[0081] The filtering circuit may be a low-pass filter (LPF) and a high-pass filter (HPF) connected in sequence, or a high-pass filter and a low-pass filter connected in sequence.

[0082] More specifically, the amplifier can use a differential amplifier circuit, which can differentially amplify the neural electrical signal output by the analog switch array 31 and the reference signal output by the neural sensor 2 to be tested, and then transmit the differentially amplified neural electrical signal to the filter circuit for high-pass filtering and low-pass filtering. Subsequently, the analog-to-digital converter converts the filtered neural electrical signal into a digital signal. After each signal processing branch is processed in the above manner, the neural electrical signal processing module 32 can output multiple digital signals to pass these digital signals to the next-level multi-channel data acquisition module for data sampling while suppressing common-mode noise.

[0083] The device of the embodiment of the present application, by using an amplifier, a filtering circuit and an analog-to-digital converter to build each signal processing branch of the neural electrical signal processing module, can amplify, filter and perform analog-to-digital conversion on each neural electrical signal output by the analog switch array, thereby improving the signal-to-noise ratio of the neural electrical signal. At the same time, the circuit structure is simple, which can save circuit design costs.

[0084] Continue to refer to Figure 2 As an optional embodiment, the computer control terminal 4 is connected to the multi-channel data sampling module 33; the computer control terminal 4 is used to configure the sampling parameters of the multi-channel data sampling module 33.

[0085] Specifically, in the embodiment of the present application, the multi-channel data sampling module 33 may be provided with a control terminal, which is connected to the computer control terminal 4 and is used to configure sampling parameters such as the input connection method, sampling rate, and measurement range during sampling measurement. The I / O interface of the multi-channel data sampling module 33 is used to receive the various neural electrical signals transmitted by the neural electrical signal processing module 32 for data sampling.

[0086] The device of the embodiment of the present application is designed to establish a control connection between the computer control terminal 4 and the multi-channel data sampling module 33, and directly uses the computer control terminal 4 to configure the sampling parameters of the multi-channel data sampling module 33, which is conducive to further simplifying the circuit structure of the device and improving the convenience of neural sensor testing.

[0087] Furthermore, the multi-channel data sampling module can sample each channel of neural electrical signals after amplification, filtering, and analog-to-digital conversion according to the sampling parameters configured above, and output the corresponding neural electrical signals to be tested to the computer control terminal 4 for signal testing.

[0088] The device of the embodiment of the present application constructs a signal preprocessing unit by utilizing an analog switch array, a neural electrical signal processing module, and a multi-channel data sampling module connected in sequence, and performs signal selection, amplification, filtering, and analog-to-digital conversion on the neural electrical signal output by the neural sensor 2 to be tested, thereby providing reliable signal test data for the subsequent computer control terminal; at the same time, by introducing the analog switch array and adaptively adjusting the open and closed states of each analog switch channel, effective testing of MEMS neural sensors with different numbers of channels can be achieved.

[0089] Continue to refer to Figure 2 As an optional embodiment, the device further includes:

[0090] Probe station 5; a probe array is provided on the probe station 5;

[0091] The probe station 5 is connected to the standard neural signal simulation module 1 through one side of the probe array; the other side of the probe array is connected to the neural sensor 2 to be tested.

[0092] Specifically, in the embodiment of the present application, a probe array is provided on the probe station 5 for placing and installing the neural sensor 2 to be tested. One side of the probe array is connected to the standard neural signal simulation module 1, and the other side is connected to the neural sensor 2 to be tested, thereby realizing the connection between the neural sensor 2 to be tested and the standard neural signal simulation module 1. Thus, the standard neural signal generated by the standard neural signal simulation module 1 can be applied to the sensitive microelectrode area of ​​the neural sensor 2 to be tested through the probe array on the probe station 5, thereby stimulating the neural sensor 2 to output a neural electrical signal.

[0093] The device of the embodiment of the present application provides a precise testing platform by introducing a probe station and uses a probe array to connect the standard neural signal simulation module with the neural sensor to be tested, thereby ensuring the stability and precision of the signal transmission between the two, which is conducive to improving the accuracy of the MEMS neural sensor test results.

[0094] Based on the content of the above embodiment, as an optional embodiment, the characteristic size range of the microelectrode of the neural sensor 2 to be tested is 1 μm to 100 μm, and the characteristic size range of the tip of each probe in the probe array is 10 μm to 50 μm.

[0095] Specifically, in the embodiment of the present application, for the probe array used on the probe station 5, the characteristic size range of the probe tip can reach 10μm to 50μm, and the characteristic size range of the microelectrode of the neural sensor that can be tested can reach 1μm to 100μm, which is consistent with the characteristic size of the nerve cell. In this way, effective testing of MEMS neural sensors with different sensitive microelectrode characteristic sizes can be achieved.

[0096] Based on the content of the above embodiment, as an optional embodiment, the output mode of the standard neural signal simulation module 1 includes a single-point random discharge mode; the target discharge parameters include discharge rate, discharge amplitude and discharge timestamp;

[0097] The computer control terminal 4 is used for:

[0098] Determine the discharge rate information, discharge amplitude information and discharge timestamp information of each channel of the neural electrical signal to be tested;

[0099] Compare the discharge rate information of each neural electrical signal to be tested with the discharge rate information of the standard neural signal;

[0100] Compare the discharge amplitude information of each channel of the neural electrical signal to be tested with the discharge amplitude information of the standard neural signal;

[0101] The discharge timestamp information of each neural electrical signal to be tested is compared with the discharge timestamp information of the standard neural signal.

[0102] Specifically, the single-point random discharge pattern described in the embodiments of this application is generally manifested as a neuron randomly and individually firing action potentials without specific or regular stimulation. This discharge pattern may have a low frequency and irregularity, reflecting the random activity of neurons in the resting state.

[0103] The discharge rate described in the embodiments of this application is used to characterize the average number of discharges of a neuron per unit time, which is an important indicator for measuring the level of neuronal activity.

[0104] The discharge amplitude described in the embodiments of this application is used to characterize the amplitude or magnitude of the electrical signal when a neuron discharges. It reflects the intensity or energy level of the neuron discharge and is an important indicator of the neuron's excitability.

[0105] The discharge timestamp described in the embodiments of the present application is used to represent the specific time point when the neuron discharge occurs, which is usually recorded in the form of a timestamp.

[0106] In an embodiment of the present application, when the output mode of the standard neural signal simulation module 1 is set to a single-point random discharge mode to output a standard neural signal, the target discharge parameters may include a discharge rate, a discharge amplitude, and a discharge timestamp.

[0107] At this time, after the computer control terminal 4 receives the various neural electrical signals to be tested output by the signal preprocessing module 3 and the standard neural signals output by the standard neural signal simulation module 1, it will use neural signal analysis means to determine the discharge rate information, discharge amplitude information and discharge timestamp information of each neural electrical signal to be tested, as well as the discharge rate information, discharge amplitude information and discharge timestamp information of the standard neural signal.

[0108] Furthermore, the computer control terminal 4 will further compare and test the discharge rate information of each neural electrical signal to be tested with the discharge rate information of the standard neural signal; at the same time, the discharge amplitude information of each neural electrical signal to be tested will be compared and tested with the discharge amplitude information of the standard neural signal, and the discharge timestamp information of each neural electrical signal to be tested will be compared and tested with the discharge timestamp information of the standard neural signal.

[0109] Furthermore, by comparing data on three parameters, namely discharge rate, discharge amplitude and discharge timestamp, the differences in discharge characteristics between each channel of the neural electrical signal to be tested and the standard neural signal are determined. The smaller the differences, or the closer they are to 0, the more normal the operation of each channel of the neural sensor to be tested can be determined, and further, the overall preparation quality of the neural sensor to be tested can be determined to be excellent.

[0110] The device of the embodiment of the present application considers the discharge characteristics of the neural electrical signal in the single-point random discharge mode, compares and tests the neural electrical signal output by the neural sensor to be tested with the standard neural signal from three parameter dimensions: discharge rate, discharge amplitude, and discharge timestamp, and can realize effective testing of MEMS neural sensors in the single-point random discharge mode.

[0111] Based on the content of the above embodiment, as an optional embodiment, the output mode of the standard neural signal simulation module 1 includes a cluster discharge mode; the target discharge parameters include discharge rate, discharge amplitude and peak potential interval;

[0112] The computer control terminal 4 is used for:

[0113] Determine the discharge rate information, discharge amplitude information and peak potential interval information of each channel of the neural electrical signal to be tested;

[0114] Compare the discharge rate information of each neural electrical signal to be tested with the discharge rate information of the standard neural signal;

[0115] Compare the discharge amplitude information of each channel of the neural electrical signal to be tested with the discharge amplitude information of the standard neural signal;

[0116] The peak potential interval information of each channel of the neural electrical signal to be tested is compared with the peak potential interval information of the standard neural signal.

[0117] Specifically, the clustered discharge pattern described in the embodiments of this application refers to a discharge pattern that occurs when a neuron is stimulated by a specific stimulus. In this pattern, the neuron fires multiple action potentials in a short period of time, forming a clustered discharge. This discharge pattern typically has a high frequency and regularity, reflecting the neuron's rapid response and excited state when stimulated.

[0118] The interspike interval described in the embodiments of this application refers to the time interval between two peak discharges. For neurons that discharge periodically, the interspike interval is stable; for neurons that discharge non-periodically, the interspike interval may vary. By analyzing the interspike interval, we can gain a deeper understanding of the firing patterns of neurons and the dynamic characteristics of neural networks.

[0119] In an embodiment of the present application, when the output mode of the standard neural signal simulation module 1 is set to a cluster discharge mode to output a standard neural signal, the target discharge parameters may include discharge rate information, discharge amplitude information, and peak potential interval.

[0120] At this time, after the computer control terminal 4 receives the various neural electrical signals to be tested output by the signal preprocessing module 3 and the standard neural signals output by the standard neural signal simulation module 1, it will determine the discharge rate information, discharge amplitude information and peak potential interval information of the various neural electrical signals to be tested, as well as the discharge rate information, discharge amplitude information and peak potential interval information of the standard neural signals through neural signal analysis means.

[0121] Furthermore, the computer control terminal 4 further compares the discharge rate, discharge amplitude, and inter-peak interval information of each test neural electrical signal with the inter-peak interval information of the standard neural signal. By comparing these three parameters, the differences in discharge characteristics between each test neural electrical signal and the standard neural signal are determined. Similarly, the smaller the differences, or the closer they are to zero, the more likely each channel of the test neural sensor is functioning properly, and thus, the better the overall fabrication quality of the test neural sensor.

[0122] The device of the embodiment of the present application considers the discharge characteristics of the neural electrical signal in the cluster discharge mode, compares and tests the neural electrical signal output by the neural sensor to be tested with the standard neural signal from the perspective of discharge rate, discharge amplitude and peak potential interval parameters, and can realize effective testing of MEMS neural sensors in the cluster discharge mode.

[0123] The neural sensor testing method provided by the present invention is described below. The neural sensor testing method described below and the neural sensor testing device described above can be referenced to each other.

[0124] Figure 4 This is a flow chart of the neural sensor testing method provided in the embodiment of the present application. This method can be applied to any of the aforementioned neural sensor testing devices, such as Figure 4 As shown, the method includes:

[0125] Step S1, outputting a standard neural signal according to a preset output mode of the standard neural signal simulation module, and applying the standard neural signal to the microelectrode area of ​​the neural sensor to be tested, so that the neural sensor to be tested outputs multiple neural electrical signals;

[0126] Step S2, performing signal preprocessing on each neural electrical signal, and outputting the neural electrical signal to be measured corresponding to each neural electrical signal;

[0127] Step S3: Comparing target discharge parameters of each channel of the neural electrical signal to be tested with the standard neural signal. The target discharge parameters are determined according to the output mode of the standard neural signal simulation module.

[0128] It should be understood that the above method can be applied to the devices in the above embodiments, and its implementation principle and technical effects are similar to those described in the above device embodiments. The execution process of this method can refer to the execution process of the corresponding embodiment in the above device, and will not be repeated here.

[0129] The neural sensor testing method of the embodiment of the present application outputs a standard neural signal through a standard neural signal simulation module, and applies the standard neural signal to the microelectrode area of ​​the neural sensor to be tested, thereby stimulating the neural sensor to be tested to output multiple neural electrical signals. At the same time, a signal preprocessing unit performs preprocessing processes such as signal amplification and filtering on each neural electrical signal to output a neural electrical signal to be tested with a high signal-to-noise ratio. Each neural electrical signal to be tested is then input into a computer control terminal, and each neural electrical signal to be tested is subjected to a target discharge parameter comparison test with the standard neural signal. This can achieve effective testing of different discharge parameters of MEMS neural sensors, fill the gap in the current MEMS neural sensor testing field, and save economic and time costs for the scientific research and application of MEMS neural sensors.

[0130] Based on the method in the above embodiment, the embodiment of the present application provides an electronic device, such as Figure 5 As shown, the electronic device may include: a processor (Processor) 510, a communication interface (CommunicationsInterface) 520, a memory (Memory) 530 and a communication bus 540, wherein the processor 510, the communication interface 520, and the memory 530 communicate with each other via the communication bus 540. The processor 510 can call the logic instructions in the memory 530 to execute the method in the above embodiment.

[0131] In addition, the logic instructions in the above-mentioned memory 530 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application.

[0132] Based on the method in the above embodiment, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method in the above embodiment.

[0133] Based on the method in the above embodiment, an embodiment of the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the method in the above embodiment.

[0134] It is understood that the processor in the embodiments of the present application may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.

[0135] The method steps in the embodiments of the present application can be implemented by hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, and the software modules can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, mobile hard disks, CD-ROMs or any other form of storage medium well known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC.

[0136] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted via the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrations. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0137] It should be understood that expressions such as "include" and "may include" used in this application indicate the existence of the disclosed functions, operations, or constituent elements, and do not limit one or more additional functions, operations, and constituent elements. In this application, terms such as "include" and / or "have" may be interpreted as indicating specific characteristics, numbers, operations, constituent elements, components, or combinations thereof, but may not be interpreted as excluding the existence or possibility of adding one or more other characteristics, numbers, operations, constituent elements, components, or combinations thereof.

[0138] It will be understood that the various numerical numbers involved in the embodiments of the present application are merely distinctions for the convenience of description and are not intended to limit the scope of the embodiments of the present application.

[0139] It is easy for those skilled in the art to understand that the above is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A neural sensor testing device, characterized in that: include: A standard neural signal simulation module, a neural sensor to be tested, a signal preprocessing unit and a computer control terminal are connected in sequence; the computer control terminal is connected to the standard neural signal simulation module; The standard neural signal simulation module is used to output a standard neural signal according to a preset output mode, and apply the standard neural signal to the microelectrode area of ​​the neural sensor to be tested, so that the neural sensor to be tested outputs multiple neural electrical signals; The signal preprocessing unit is used to perform signal preprocessing on each channel of the neural electrical signal and output a neural electrical signal to be tested corresponding to each channel of the neural electrical signal; The computer control terminal is used to compare the target discharge parameters of each of the neural electrical signals to be tested with the standard neural signal; the target discharge parameters are determined according to the output mode of the standard neural signal simulation module; The output mode of the standard neural signal simulation module includes a single-point random discharge mode; the target discharge parameters include discharge rate, discharge amplitude and discharge timestamp; The computer control terminal is used for: Determining the discharge rate information, discharge amplitude information and discharge timestamp information of each channel of the neural electrical signal to be tested; Comparing the discharge rate information of each channel of the neural electrical signal to be tested with the discharge rate information of the standard neural signal; Comparing the discharge amplitude information of each channel of the neural electrical signal to be tested with the discharge amplitude information of the standard neural signal; Comparing the discharge timestamp information of each channel of the neural electrical signal to be tested with the discharge timestamp information of the standard neural signal; or, The output mode of the standard neural signal simulation module includes a cluster discharge mode; the target discharge parameters include discharge rate, discharge amplitude and peak potential interval; The computer control terminal is used for: Determining the discharge rate information, discharge amplitude information, and peak potential interval information of each channel of the neural electrical signal to be tested; Comparing the discharge rate information of each channel of the neural electrical signal to be tested with the discharge rate information of the standard neural signal; Comparing the discharge amplitude information of each channel of the neural electrical signal to be tested with the discharge amplitude information of the standard neural signal; The peak potential interval information of each channel of the neural electrical signal to be tested is compared with the peak potential interval information of the standard neural signal.

2. The neural sensor testing device according to claim 1, characterized in that: The signal preprocessing unit includes an analog switch array, a neural electrical signal processing module, and a multi-channel data sampling module connected in sequence; the input end of the analog switch array is connected to the output end of the neural sensor to be tested, and the output end of the multi-channel data sampling module is connected to the input end of the computer control end; The analog switch array is used to conduct the analog switch channels corresponding to the neural sensors to be tested, so as to transmit the multiple neural electrical signals to the neural electrical signal processing module; The neural electrical signal processing module is used to amplify and filter each channel of the neural electrical signal to obtain a multi-channel processed neural electrical signal; The multi-channel data sampling module is used to sample the processed neural electrical signals of each channel and output the corresponding neural electrical signals to be tested.

3. The neural sensor testing device according to claim 2, characterized in that: The computer control terminal is connected to the analog switch array; the computer control terminal is used to configure the open and close states of each analog switch channel in the analog switch array.

4. The neural sensor testing device according to claim 2, characterized in that: The computer control end is connected to the multi-channel data sampling module; the computer control end is used to configure the sampling parameters of the multi-channel data sampling module.

5. The neural sensor testing device according to claim 2, characterized in that: The neural electrical signal processing module includes multiple signal processing branches, each of which includes an amplifier, a filter circuit, and an analog-to-digital converter connected in sequence; the amplifier is used to perform differential amplification processing on the input neural electrical signal and the reference signal; the filter circuit is used to perform filtering processing on the output signal of the amplifier; and the analog-to-digital converter is used to perform analog-to-digital conversion processing on the output signal of the filter circuit; Among them, the positive input end of the amplifier in each signal processing branch is connected to the corresponding analog switch channel in the analog switch array for inputting neural electrical signals; the negative input end of the amplifier in each signal processing branch is respectively connected to the reference signal output end of the neural sensor to be tested for inputting the reference signal output by the reference signal output end.

6. The neural sensor testing device according to any one of claims 1 to 5, characterized in that: Also includes: A probe station; a probe array is provided on the probe station; One side of the probe array is connected to the standard neural signal simulation module; the other side of the probe array is connected to the neural sensor to be tested.

7. The neural sensor testing device according to claim 6, characterized in that: The characteristic size of the microelectrode of the neural sensor to be tested ranges from 1 μm to 100 μm, and the characteristic size of the tip of each probe in the probe array ranges from 10 μm to 50 μm.

8. A neural sensor testing method applied to the neural sensor testing device according to any one of claims 1 to 7, characterized in that: include: Outputting a standard neural signal according to a preset output mode of the standard neural signal simulation module, and applying the standard neural signal to the microelectrode area of ​​the neural sensor to be tested, so that the neural sensor to be tested outputs multiple neural electrical signals; Performing signal preprocessing on each channel of the neural electrical signal, and outputting a neural electrical signal to be measured corresponding to each channel of the neural electrical signal; Each of the neural electrical signals to be tested is subjected to a comparison test of target discharge parameters with the standard neural signal; the target discharge parameters are determined according to the output mode of the standard neural signal simulation module.

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