In-vivo multichannel electroneurographic signal acquisition system and signal separator

Abstract

The invention provides an in-vivo multichannel electroneurographic signal acquisition system and a signal separator. The in-vivo multichannel electroneurographic signal acquisition system comprises at least one 2 # imgabs0 # 10 integrated interface, at least two electronic elements, at least four conductive commutators and at least four free electrode groups, wherein the free electrode groups comprise free needles as electrodes; wherein the 2 # imgabs1 # 10 integrated interface is divided into a module I, a module II, a module III and a module IV. A 32-to-16 electrode adapter configured for the pre-amplifier is divided into four independent modules through a separation circuit, so that the purpose that one set of in-vivo multichannel recording system can simultaneously record the electroneurographic signals of a plurality of areas of a plurality of freely moving rats and mice is achieved. The free needle is used as an electrode, so that implantation of different brain areas can be flexibly realized, and an instrument basis is provided for constructing a multi-layer sensing mechanism for stress response type identification based on a heterograph neural network.

Description

Technical Field

[0001] The present invention relates to the field of signal acquisition, and particularly to an in-vivo multi-channel neural electrical signal acquisition system and a signal separator. Background Art

[0002] The in-vivo multi-channel neural electrical signal recording technology is a method for studying the neural electrical activities of animals in a living state. A metal electrode is implanted into a certain cerebral cortex or region of an animal, and the rear end of the electrode is connected to a high-sensitivity preamplifier and a recording system. The recorded analog signal is converted into a digital signal, and the discharge signals of neurons can be observed on a supporting recording software. By applying this technology, the electrical activities of multiple neurons in multiple brain regions can be synchronously recorded, and high-frequency signals spike and low-frequency signals LFP, EEG, and EMG can be simultaneously displayed. It is one of the powerful tools for analyzing the neural information coding of the brain. At present, there are already very mature systems and products for in-vivo multi-channel recording, such as the OmniPlex-D system of Plexon, the Cerebus system of Blackrock Microsystems, the Digital Lynx system of Neuralynx, the Intan RHD system of Intan Technologies, the Grapevine system of Ripple Neuro, etc. These systems all have characteristics such as high sampling rate and A / D conversion rate, ultra-low noise, and easy-to-use software, which have brought great help to researchers in neuroscience research.

[0003] However, in these in-vivo multi-channel neural electro-signal recording systems, most of the configured preamplifiers take 32 channels as a module. For example, a 32-channel recording system is configured with 1 preamplifier, a 64-channel recording system is configured with 2 preamplifiers, and so on. However, in many animal experiments, such a large number of channels are not needed. Therefore, the company often configures a 32-channel adapter to simplify the operation of implanting electrodes in animals. Nevertheless, most of these systems can only be used for recording in-vivo neural electro-signals of one animal at a time. It can synchronously record multiple brain regions of one animal, but cannot synchronously record multiple brain regions of multiple animals. However, in many animal experiments, such as screening of epilepsy models of rats and mice, monitoring of sleep electroencephalogram and electromyogram of rats and mice, etc., the number of channels required for each animal generally does not exceed 4 channels, but long-term monitoring is required. If only 1 set of system is used to complete the above experiments, it will take a long time. In addition, the neural electro-signals of freely moving animals are extremely vulnerable to factors such as the external environment and animal behavior. If these same-group experiments are completed on different dates, it is very difficult to ensure the consistency of experimental results. Of course, multiple sets of systems can be used to start recording simultaneously, but the cost of purchasing systems is very high in this way, and installing multiple sets of systems also requires a large space, and most channels of each set of systems are not used, which will cause great waste.

[0004] The prior art such as the invention patent application publication CN104524692A discloses an implantable multi-channel neural electro-signal acquisition circuit, as Figure 1 shown, which includes a motor group for sensing weak neural electro-signals generated by the electro-activity of cells in biological tissues; an amplifier group for amplifying the sensed weak electro-signals to obtain amplified neural electro-signals; a reference signal generator for generating a sine wave signal as a measurement reference; a modulator group for performing amplitude modulation on the sine wave signal and the amplified neural electro-signals to output an amplitude-modulated wave voltage signal; a signal adder for linearly adding the amplitude-modulated wave voltage signals output by each modulator in the modulator group to obtain an added electro-signal; an up-converter for performing up-conversion on the added electro-signal to output a radio frequency signal; a radio frequency transmitter for power-amplifying and antenna-transmitting the radio frequency signal; a multi-channel carrier generator for providing carrier signals for the modulator group and the up-converter; the output end of the motor group is sequentially connected to the input end of the radio frequency transmitter through the amplifier group, the modulator group, the signal adder and the up-converter, the output end of the reference signal generator is connected to the input end of the modulator group, and the output end of the multi-channel carrier generator is respectively connected to the input end of the modulator group and the input end of the up-converter. Each amplifier in this implantable multi-channel neural electro-signal acquisition circuit only corresponds to one biological tissue, and most of the channels of the amplifier are not used, thus causing great waste and extremely high cost.

[0005] In addition, the prior art realizes multi-channel signal acquisition through a needle row. Since different parts of the animal brain represent different neuron activity functions, the fixed needle row arrangement cannot flexibly achieve the free spatial implantation distribution of each channel needle in the brain region. The signal loss of a two-stage multi-pin interface cannot be ignored either.

[0006] Finally, the electronic components and the integrated interface are uniformly wrapped with epoxy resin, which cannot guarantee the insulation between circuits and the risk of short-circuit lap during the wrapping process. Summary of the Invention

[0007] To solve the above problems, the present invention provides a signal separator for an in-vivo multi-channel neural electrical signal acquisition system, which divides the 32-to-16 electrode adapter configured by the preamplifier into 4 independent modules, thereby achieving the purpose that a set of in-vivo multi-channel recording system can simultaneously record the neural electrical signals of multiple regions of multiple freely moving mice and rats.

[0008] A signal separator for an in-vivo multi-channel neural electrical signal acquisition system includes at least 1 two 10 integrated interfaces, at least 2 electronic components, at least 4 conductive commutators, and at least 4 free electrode groups; Among them, 1 two 10 integrated interfaces are divided into Module I, Module II, Module III, and Module IV; Two Module I of the 10 integrated interfaces, 2 electronic components, 1 conductive commutator, and 1 free electrode group are connected to form the first component separation circuit; Two Module II of the 10 integrated interfaces, 2 electronic components, 1 conductive commutator, and 1 free electrode group are connected to form the second component separation circuit; Two Module III of the 10 integrated interfaces, 2 electronic components, 1 conductive commutator, and 1 free electrode group are connected to form the third component separation circuit; Two Module IV of the 10 integrated interfaces, 2 electronic components, 1 conductive commutator, and 1 free electrode group are connected to form the fourth component separation circuit, where the at least 1 two 10 integrated interfaces and at least 2 electronic components are integrally wrapped in an independent and separated manner through a plastic housing with relatively independent chambers.

[0009] Preferably, each of the at least 1 two 10 integrated interfaces includes 20 pins, and the 20 pins are divided into 6 groups, among which 2 groups are respectively named ① and ②, each having 2 pins, and 4 groups are respectively named Module I, Module II, Module III, and Module IV, each having 4 pins.

[0010] Preferably, each of at least two electronic components has five pins, a pitch of 2.54 mm, and a resistance value of 5.1 KΩ.

[0011] Preferably, each of at least four conductive commutators includes six wires, one housing, one 2 ×3 pin header, and one threaded sleeve.

[0012] Preferably, the housing includes a stator side and a rotor side.

[0013] Preferably, each of at least four free electrode groups includes six wires, one 2 ×3 female header, six free pins, one spring sleeve, and one threaded cap. Each free pin is disposed at the end of a corresponding one of the six wires. The 2 ×3 female header can be detachably connected to the 2 ×3 pin header.

[0014] More preferably, the six wires are wrapped with insulating jackets of different colors or insulating jackets of the same color provided with markers for mutual distinction.

[0015] The present invention also provides an in-vivo multi-channel neural electrical signal acquisition system, including a signal separator of the in-vivo multi-channel neural electrical signal acquisition system as described above.

[0016] Preferably, it further includes a preamplifier, an analog-to-digital converter, and a data processing unit. The data processing unit is provided with a storage medium capable of performing artificial intelligence model processing, for establishing a heterogeneous graph neural network model according to signals of different brain regions, and constructing animal stress response recognition by using an attention mechanism.

[0017] Optionally, the method for establishing a heterogeneous graph neural network model according to signals of different brain regions and constructing animal stress response recognition by using an attention mechanism includes: S1 Regarding each free pin as a node, establishing a heterogeneous graph H(V, E, X), where V is a set of nodes, E is a set of relationships, X is a set of bioelectrical information, and setting corresponding labels for each type of stress response type; S2 Constructing a multi-node path L, aggregating the nodes, and calculating the node relationship are respectively the representations of node i and any neighbor node j on the multi-node path L of node i, is a neural network for implementing the node attention mechanism, , for performing normalization processing to obtain , aggregating the aggregated representation of node i on the node path L , It refers to summing up the number of all neighbor nodes on the multi-node path L; S3 continues to construct the aggregation of the node path and calculates the path relationship of the specific path containing node i They are the attention vector, the weight matrix, and the bias vector respectively; It sums up the nodes on all V, and then It is normalized to to obtain the aggregated representation , is the specific path representation containing node i, which is calculated through the following path neural network is the neural network for implementing the path attention mechanism; S4 establishes a multi-layer perception mechanism for stress response recognition, trains the accuracy rate of stress response types, and establishes a loss function to optimize A NODE (·), A ROUTE (·), and the network parameters of the multi-layer perception mechanism.

[0018] The beneficial effects of the present invention include: The 32-to-16 electrode adapter of the preamplifier is separated into 4 independent modules through a separation circuit, and there is no interference between each module. It can realize a set of in-vivo multi-channel recording system to simultaneously record the neural electrical signals of multiple animals, greatly improving the experimental efficiency, ensuring the reliability of experimental results, and at the same time greatly reducing the experimental cost. At the same time, the free needle can be used as an electrode to flexibly implant different brain regions, providing an instrument basis for constructing a multi-layer perception mechanism for stress response type recognition based on heterogeneous graph neural networks. Description of the Drawings

[0019] Figure 1 is the structural framework diagram of an implantable multi-channel neural electrical signal acquisition circuit in the prior art; Figure 2 is the physical diagram of the signal separator of an in-vivo multi-channel neural electrical signal acquisition system of the present invention; Figure 3 is Figure 2 the circuit structure diagram of the signal separator of the in-vivo multi-channel neural electrical signal acquisition system shown in; Figure 4 is Figure 2 the 2 10 integrated interface schematic diagram of the signal separator of the in-vivo multi-channel neural electrical signal acquisition system shown in; Figure 5 is Figure 2 the schematic diagram of the electronic components of the signal separator of the in-vivo multi-channel neural electrical signal acquisition system shown in; Figure 6 is Figure 2 the structural schematic diagram of the conductive commutator of the signal separator of the in-vivo multi-channel neural electrical signal acquisition system shown in; Figure 7 yes Figure 2 The schematic diagram of the free electrode group of the signal separator of the in vivo multi-channel neural electrical signal acquisition system is shown; Figure 8 This is a schematic diagram of four types of free needles implanted in different areas of the left and right brain hemispheres of animals to form specific node pathways. DETAILED DESCRIPTION

[0020] The embodiments of the present invention are described below in conjunction with the accompanying drawings. Those skilled in the art should understand that these embodiments are only illustrative and not restrictive.

[0021] Figure 2 This is a physical diagram of a signal separator of an in vivo multi-channel neural electrical signal acquisition system of the present invention. As shown in the figure, a signal separator of an in vivo multi-channel neural electrical signal acquisition system of an embodiment of the present invention includes 1 2 10 integrated interfaces (11), 2 electronic components, 4 conductive commutators (12) and 4 free electrode groups (13). The 2 10 integrated interfaces (11) are packaged together with the 2 electronic components.

[0022] As shown in the figure, the plastic cover (14) is composed of two pieces buckled together, and the two pieces have three cavities a, b, and c after buckling together. The figure shows a cross-section of a cavity on a piece, cavity a is used to place an integrated interface, and cavities b and c are used to place one electronic component each, and the welding wires between the two are connected through a wiring channel (20). In this way, the three device units are separated, and after the arrangement of each device is completed, mutual interference and short circuit risks during assembly are avoided.

[0023] Figure 3 yes Figure 2 The circuit structure diagram of the signal separator of the in vivo multi-channel neural electrical signal acquisition system is shown in FIG. 10 The integrated interface (11) is divided into module I, module II, module III and module IV. 2 10. Module I of the integrated interface, 2 electronic components, 1 conductive commutator and 1 free electrode group are connected to form the first group of separation circuits; 2. 10 module II of the integrated interface, 2 electronic components, 1 conductive commutator and 1 free electrode group are connected to form the second group of separation circuits; 2 10 module III of the integrated interface, 2 electronic components, 1 conductive commutator and 1 free electrode group are connected to form the third group of separation circuits; 2 The fourth component separation circuit is composed of a module IV with a 10-integration interface, 2 electronic components, 1 conductive commutator, and 1 free electrode group. The 2 electronic components in the first, second, third, and fourth component separation circuits are the same electronic components, 1 conductive commutator is a different conductive commutator, and 1 free electrode group is a different free electrode group. The 32-to-16 electrode transfer interface configured by the preamplifier is separated into 4 independent modules through the four-component separation circuit, thus achieving the purpose that a set of in-vivo multi-channel recording system can simultaneously record the neural electrical signals of multiple regions of multiple freely moving mice and rats.

[0024] Figure 4 Yes Figure 2 2 of the signal separators of the in-vivo multi-channel neural electrical signal acquisition system shown Schematic diagram of the 10-integration interface. As shown in the figure, there are at least 1 2 Each integration interface in the 10-integration interface includes 20 pins with a pin pitch of 1.27 mm. The 20 pins are divided into 6 groups, among which 2 groups are respectively named ① and ②, each having 2 pins, and 4 groups are respectively named Module I, Module II, Module III, and Module IV, each having 4 pins.

[0025] Figure 5 Yes Figure 2 Schematic diagram of the electronic components of the signal separator of the in-vivo multi-channel neural electrical signal acquisition system shown. As shown in the figure, each of the at least 2 electronic components has 5 pins with a pin pitch of 2.54 mm and a resistance value of 5.1 KΩ. The 5 pins of electronic component 1 are named ① ’ , A, B, C, D (from left to right), and the 5 pins of electronic component 2 are named ② ’ , A ’ , B ’ , C ’ , D ’ (from left to right). 2 Pin ① of the 10-integration interface is welded to pin ①' of electronic component 1, and 2 Pin ② of the 10-integration interface is welded to pin ②' of electronic component 2.

[0026] Figure 6 Yes Figure 2 Schematic structural diagram of the conductive commutator of the signal separator of the in-vivo multi-channel neural electrical signal acquisition system shown. As shown in the figure, at least 4 conductive commutators are respectively named a, b, c, and d. Each conductive commutator includes 6 wires, 1 housing (40), and 1 2 A pin header with 3 rows (50) and 1 threaded sleeve (60). The wire is a multi-core 28 AWG Teflon-insulated wire with a length of 250 mm, and the maximum output current is 2 A. The housing (40) includes a stator side (41) and a rotor side (42). The contact material between the stator side and the rotor side is gold-gold. The housing (40) is made of ABS plastic, with a maximum rotational speed of 250 RPM, a contact resistance less than 150 mΩ, and an electrical noise less than 10 mΩ. The outer diameter of the stator side is 12.5 mm, and the outer diameter of the rotor side is 5 mm. The leads from the stator side (43) and the rotor side (44) are 6 wires. The 6 wires from the rotor side lead (44) are welded to the 6 pins of the 2 3-row pin header (50), and the 6 wires from the stator side lead (43) are welded to the pins of the 2 10 integrated interface modules and the pins of the electronic components; 1 threaded sleeve (60) is put on the 6 wires from the rotor side lead (44), with the threaded port facing the 2 3-row pin header (50). The conductive commutator can prevent the connection wires from being entangled when the animal moves freely.

[0027] Figure 7 Yes Figure 2 Schematic diagram of the free electrode group of the signal separator of the in-vivo multi-channel neural signal acquisition system as shown. As shown in the figure, each of at least 4 free electrode groups includes 6 wires, 1 2 3-row female header (70), 6 free pins (30) led out by wires, 1 spring sleeve (80) and 1 threaded cap (90). The wire is a single-strand silver-plated Teflon high-temperature wire, with its inner diameter outer diameter length = 0.15 0.30 450 mm; the spring sleeve (80) is made of 304 stainless steel, with its wire diameter outer diameter inner diameter length of 0.4 4 3.2 420 mm. The specific connection method is as follows: The 6 wires are integrally sleeved inside the spring sleeve (80), and the outside of the wires is uniformly covered with a red insulating sleeve. Then one end of the wires is welded to the 2 3-row female header (70), and this 2 3-row female header (70) is connected to the 2 Figure 6 3-row pin header of the conductive commutator in the other end is welded to the free pins (30), and then the threaded cap (90) is put on the outside of the spring sleeve (80), with the cap opening of the threaded cap (90) facing the 2 3-row female header (70) end. Markers (31) with different colors from each other are set at the welding parts of the 6 free pins.

[0028] Figure 8 It is a four - category distribution map of implanting free needles (30) as electrodes in the animal brain. In Figure 8 , a is the distribution of free needle implantation points in the left hemisphere, and its color corresponds one - to - one with the color of the marker (31) in Figure 2 and Figure 7 . In Figure 8 , b is the distribution of the right hemisphere, c and d are the distributions of the left and right hemispheres respectively. a - d give a specific path example with a certain node i as the research node. Among them, a is a specific path with branches (defined as the merger of two specific paths in the present invention), and c and d are respectively examples of the distribution of 1 asymmetric and 3 symmetric specific paths.

[0029] Thus, through the setting of multiple specific paths, corresponding labels are set according to the stress response type, and a bio - electrical information matrix is used to train a multi - layer perception mechanism model based on the heterogeneous graph neural network composed of these paths, so as to be used for identifying the stress type. The specific steps are as follows: S1: Regarding each free needle as a node, establish a heterogeneous graph H(V, E, X), where V is the set of nodes, E is the set of relationships (including the node relationships between nodes), X is the set of bio - electrical information (including the bio - electrical information matrix), and set corresponding labels for each type of stress response; S2: Construct a multi - node path L, and aggregate the nodes to calculate the node relationship are respectively the representations of node i and any neighbor node j on the multi - node path L of node i (such as Figure 8 a in is a neural network for implementing the node attention mechanism, . For , perform normalization processing to get , and aggregate the aggregated representation of node i on the node path L , where Σ means summing over all neighbor nodes on the multi - node path L; S3: Continue to construct the aggregation of the node path, and calculate the path relationship of the specific path (∈L) containing node i are respectively the attention vector, weight matrix, and bias vector, is the sum over all nodes on V, and then perform normalization processing to get , and obtain the aggregated representation , is the representation of the specific path containing node i, and is calculated through the following path neural network is a neural network for implementing the path attention mechanism; S4: Establish a multi - layer perception mechanism for stress response recognition, train the accuracy rate of the stress response type, and establish a loss function to optimize A NODE(·), A ROUTE (·), Multi-layer perception mechanism network parameters.

[0030] As described above, 2 Module I of the 10 integrated interfaces, 2 electronic components, 1 conductive commutator and 1 free electrode group are connected to form the first component separation circuit; 2 Module II of the 10 integrated interfaces, 2 electronic components, 1 conductive commutator and 1 free electrode group are connected to form the second component separation circuit; 2 Module III of the 10 integrated interfaces, 2 electronic components, 1 conductive commutator and 1 free electrode group are connected to form the third component separation circuit; 2 Module IV of the 10 integrated interfaces, 2 electronic components, 1 conductive commutator and 1 free electrode group are connected to form the fourth component separation circuit.

[0031] The specific connection methods of the first component separation circuit, the second component separation circuit, the third component separation circuit and the fourth component separation circuit are as follows: First component separation circuit: Among the 6 wires leading out from the stator side of the conductive commutator A, 4 wires are respectively welded to the 4 pins of Module I of the 10 integrated interfaces, 1 wire is welded to the pin A of the electronic component 1, and 1 wire is welded to the pin A' of the electronic component 2; Second component separation circuit: Among the 6 wires leading out from the stator side of the conductive commutator b, 4 wires are respectively welded to the 4 pins of Module II of the 10 integrated interfaces, 1 wire is welded to the pin B of the electronic component 1, and 1 wire is welded to the pin B' of the electronic component 2; Third component separation circuit: Among the 6 wires leading out from the stator side of the conductive commutator c, 4 wires are respectively welded to the 4 pins of Module III of the 10 integrated interfaces, 1 wire is welded to the pin C of the electronic component 1, and 1 wire is welded to the pin C' of the electronic component 2; Fourth component separation circuit: Among the 6 wires leading out from the stator side of the conductive commutator d, 4 wires are respectively welded to the 4 pins of Module IV of the 10 integrated interfaces, 1 wire is welded to the pin D of the electronic component 1, and 1 wire is welded to the pin D' of the electronic component 2. Third component separation circuit: Among the 6 wires leading out from the stator side of the conductive commutator c, 4 wires are respectively welded to the 4 pins of Module III of the 10 integrated interfaces, 1 wire is welded to the pin C of the electronic component 1, and 1 wire is welded to the pin C' of the electronic component 2; Fourth component separation circuit: Among the 6 wires leading out from the stator side of the conductive commutator d, 4 wires are respectively welded to the 4 pins of Module IV of the 10 integrated interfaces, 1 wire is welded to the pin D of the electronic component 1, and 1 wire is welded to the pin D' of the electronic component 2. Fourth component separation circuit: Among the 6 wires leading out from the stator side of the conductive commutator d, 4 wires are respectively welded to the 4 pins of Module IV of the 10 integrated interfaces, 1 wire is welded to the pin D of the electronic component 1, and 1 wire is welded to the pin D' of the electronic component 2. Fourth component separation circuit: Among the 6 wires leading out from the stator side of the conductive commutator d, 4 wires are respectively welded to the 4 pins of Module IV of the 10 integrated interfaces, 1 wire is welded to the pin D of the electronic component 1, and 1 wire is welded to the pin D' of the electronic component 2.

[0032] Finally, use a plastic housing as Figure 3 shown, arrange and isolate the 10 integrated interfaces and 2 electronic components in each chamber, wrap them together as a whole, and for each conductive commutator, the 2 wires leading out from the rotor side 10 integrated interfaces and 2 electronic components are arranged and isolated in each chamber, wrapped together as a whole, and for each conductive commutator, the 2 wires leading out from the rotor side The 3-pin header is connected to 2 of the 1 free electrode groups and then the threaded sleeve of the conductive commutator is screwed together with the threaded cap of the free electrode group.

[0033] The functions of the 6 wires in each separation circuit are as follows: 2 The 4 wires soldered to the 4 pins of the 10 integrated interface modules serve as 4 free moving electrodes (32). The 1 wire soldered to the pin (A / B / C / D) of the electronic component 1 serves as the free ground electrode (33). The 1 wire soldered to the pin (A’ / B’ / C’ / D’) of the electronic component 2 serves as the free reference electrode (34).

[0034] The 4 channels are for the free moving electrodes (32) and can directly capture the electrical activities in the brain regions where the electrodes are located. The 1 channel is for the free reference electrode (34) and can provide a stable comparison benchmark, which is the reference point for measuring the potential difference of the free moving electrodes (32). The 1 channel is for the free ground electrode (33) and can help stabilize the electrical noise of the system and other external electrical interferences.

[0035] According to the present invention, there is also provided an in-vivo multi-channel neural electrical signal acquisition system, including the signal separator of the in-vivo multi-channel neural electrical signal acquisition system as described above.

[0036] The in-vivo multi-channel neural electrical signal acquisition system further includes a preamplifier, an analog-to-digital converter and a data processing unit, and the data processing unit is provided with a storage medium capable of performing artificial intelligence model processing, for establishing a heterogeneous graph neural network model according to the signals of different brain regions, and constructing an animal stress response recognition by using the attention mechanism.

[0037] The connection relationships between these components are well-known connection methods in the technical field to which they belong and will not be elaborated here.

[0038] As described above, it is only the specific implementation manners of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the technical field of the present invention within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A signal separator for an in-vivo multi-channel neural electrical signal acquisition system, characterized in that: Including at least 1 of 2 10 integrated interfaces, at least 2 electronic components, at least 4 conductive commutators, and at least 4 free electrode groups; One of them is 2 The 10 integrated interfaces are divided into Module I, Module II, Module III, and Module IV; 2 Module I of the integrated interface, two electronic components, a conductive commutator, and a free electrode group are connected to form the first component separation circuit; 2 Module II of the 10 integrated interfaces, 2 electronic components, 1 conductive commutator and 1 free electrode group are connected to form the second component separation circuit; 2 Module III of the integrated interface, 2 electronic components, 1 conductive commutator and 1 free electrode group are connected to form the third component separation circuit; 2 Module IV of the 10 integrated interfaces, 2 electronic components, 1 conductive commutator and 1 free electrode group are connected to form the 4th component separation circuit, wherein at least 1 of the 2 The 10 integrated interfaces and at least 2 electronic components are integrally wrapped in an independent separated manner through a plastic housing with relatively independent chambers.

2. The signal separator of the in-vivo multi-channel neural electrical signal acquisition system according to claim 1, characterized in that: The at least one 2 Each of the 10 integrated interfaces includes 20 pins. The 20 pins are divided into 6 groups. Among them, 2 groups are respectively named ① and ②, each having 2 pins, and 4 groups are respectively named the module I, the module II, the module III, and the module IV, each having 4 pins.

3. The signal separator of the in-vivo multi-channel neural electrical signal acquisition system according to claim 2, characterized in that: Each of the two electronic components has 5 pins with a pitch of 2.54 mm and a resistance value of 5.1 kΩ.

4. The signal separator of the in-vivo multi-channel neural electrical signal acquisition system according to claim 3, wherein: Each of the at least four conductive commutators includes six wires, one housing, one 2 ×3 pin header, and one threaded sleeve.

5. The signal separator of the in-vivo multi-channel neural electrical signal acquisition system according to claim 4, characterized in that: The housing includes a stator side and a rotor side.

6. The signal separator of the in-vivo multi-channel neural electrical signal acquisition system according to claim 5, characterized in that: Each of the at least four free electrode groups includes six wires, one 2 -row female terminal, six free needles, one spring sleeve and one threaded cap.

7. The signal separator of the in-vivo multi-channel neural electrical signal acquisition system according to claim 5, characterized in that: Six free electrodes, four of which directly capture the electrical activity of the brain regions where the electrodes are located, and the remaining two are a free ground electrode that helps to stabilize the electrical noise and external electrical interference of the system and a free reference electrode that provides a reference point for measuring the potential difference of the free electrodes. Among the six wires in each separation circuit, 2 Four wires soldered to the four pins of the 10 integrated interface module serve as the four free electrodes, one wire soldered to the pin of the electronic component 1 serves as the free ground electrode, and one wire soldered to the pin of the electronic component 2 serves as the free reference electrode.

8. An in-vivo multi-channel neural electrical signal acquisition system, characterized in that: A signal separator of the in-vivo multi-channel neural electrical signal acquisition system according to any one of claims 1 to 7.

9. The in-vivo multi-channel neural electrical signal acquisition system according to claim 8, characterized in that: It further includes a preamplifier, an analog-to-digital converter and a data processing unit, and the data processing unit is provided with a storage medium capable of executing artificial intelligence model processing, for establishing a heterogeneous graph neural network model according to signals of different brain regions, and constructing animal stress response recognition by using an attention mechanism.

10. The in-vivo multi-channel neural electrical signal acquisition system according to claim 9, wherein: A method for establishing a heterogeneous graph neural network model according to signals of different brain regions and constructing animal stress response recognition by using an attention mechanism includes: S1 Regarding each free needle as a node, establishing a heterogeneous graph H(V, E, X), where V is a set of nodes, E is a set of relationships, X is a set of bioelectrical information, and setting corresponding labels for each type of stress response type; S2 constructs a multi-node path L, aggregates the nodes, and calculates the node relationships are the representations of node i and any neighbor node j on the multi-node path L of node i, respectively is a neural network for implementing the node attention mechanism , for perform normalization processing to be , aggregate the aggregated representation of node i on the node path L , refers to summing up all the neighbor node numbers on the multi-node path L; S3 continues to construct the aggregation of the node paths and calculates the path relationship of a specific path containing node i are the attention vector, weight matrix, and bias vector respectively is to sum over all nodes on V, and then is normalized to to obtain the aggregated representation , is the specific path representation containing node i, which is calculated by the following path neural network is the neural network for implementing the path attention mechanism S4 establishes a multi-layer perception mechanism for stress response recognition, trains the accuracy rate of stress response types, and establishes a loss function to optimize 、 and the network parameters of the multi-layer perception mechanism.

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