Neuromorphic brain-computer emotion sensing method and device based on delayed sampling reserve pool

Abstract

The invention provides a neuromorphic brain-computer emotion perception method based on a delayed sampling reserve pool and also provides corresponding equipment, the method provided by the invention comprises the following steps: sampling electric signal data of a preset part of a brain through a graphene-silicon heterostructure sensor according to a preset frequency, establishing the delayed sampling reserve pool, and sending the delayed sampling reserve pool to a computer; the reading matrix unit performs preliminary operation on the electric signal data in the delay sampling reserve pool to obtain a weighted output connection weight, and outputs the weighted output connection weight to computer equipment, so that the computer equipment performs emotion judgment according to the weighted output connection weight and outputs an emotion judgment result; not only is the actual resolution improved by arranging the graphene-silicon heterostructure sensor, but also data acquisition of virtual multi-neuron nodes under a single-node neuron condition is realized; the hardware structure is simplified, and the operation efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of brain-computer perception technology, and specifically to a neuromorphic brain-computer emotion perception method based on a delayed sampling reserve pool, and corresponding equipment. Background Art

[0002] Highly dynamic autonomous decision-making is the core of the bionic intelligent perception system. How to construct efficient information acquisition and processing capabilities, break through the bottleneck of modern computers using the von Neumann architecture, and change the discrete architecture of signal sensing and computing are the research hotspots of neuromorphic computing and brain-computer interfaces.

[0003] The brains of higher organisms have strong asynchronous and parallel characteristics. Compared with artificially constructed neural networks, biological brains show super high energy utilization advantages. In recent years, with the research on distillation and quantization of artificial neural networks, it has also been verified that there is a large redundancy and room for improvement in artificial neural networks. In the field of traditional brain-computer interface and artificial intelligence, the classic research is to use artificial neural networks to analyze and understand the brain-computer interface signals that have been sampled and preprocessed. On the one hand, this scheme is limited by the destruction of the complexity of the signal space by structures such as the skull, and on the other hand, it is limited to the data labels of supervised learning, and it is difficult to give explanatory analysis and response to unknown physiological signal microstructures. Therefore, how to imitate the higher biological nervous system and construct a neuromorphic network with ultra-low energy consumption, support fast training with few samples, and highly imitate the structural characteristics of the biological nervous system has become an important direction of current neuromorphic brain-computer interface research. Summary of the invention

[0004] The purpose of this application is to solve the problem of how to imitate the higher biological nervous system and construct a neuromorphic network with ultra-low energy consumption, support fast training with a small number of samples, and highly imitate the structural characteristics of the biological nervous system.

[0005] In order to solve the above technical problems, the present application provides a neuromorphic brain-computer emotion perception method based on a delayed sampling reserve pool, comprising:

[0006] The graphene-silicon heterostructure sensor samples the electrical signal data of a preset part of the brain at a preset frequency to establish a delayed sampling reserve pool;

[0007] The readout matrix unit then performs a preliminary operation on the electrical signal data in the delayed sampling reserve pool to obtain a weighted output connection weight;

[0008] The weighted output connection weight is output to a computer device, so that the computer device performs emotion judgment according to the weighted output connection weight and outputs an emotion judgment result.

[0009] Furthermore, the method also includes an electrical signal data acquisition unit connected to the graphene-silicon heterostructure sensor, wherein the electrical signal data acquisition unit acquires electrical signal data of a preset part of the brain and transmits the electrical signal data to the graphene-silicon heterostructure sensor.

[0010] Furthermore, the step of performing preliminary calculations on the electrical signal data in the delayed sampling reserve pool by the readout matrix unit to obtain weighted output connection weights includes:

[0011] The readout network unit receives transient response values ​​of a plurality of virtual neuron nodes in a delayed sampling reserve pool;

[0012] Combining the transient response values ​​of the plurality of virtual neuron nodes into initial output connection values;

[0013] The initial output connection values ​​are weighted and summed to obtain weighted output connection weights.

[0014] Furthermore, the step of performing a weighted sum operation on the initial output connection values ​​to obtain weighted output connection weights is specifically as follows: the initial output connection values ​​form an N-dimensional output tensor, and the N-dimensional output tensor is summed with the weights (W T ) to obtain the weighted output connection weight.

[0015] In order to solve the above technical problems, the present application also provides a neuromorphic brain-computer emotion perception method based on a delayed sampling reserve pool, comprising:

[0016] The computer device receives the weighted output connection weights output by the graphene-silicon heterostructure sensor;

[0017] Substituting the weighted output connection weight into the trained emotion prediction model to perform emotion judgment;

[0018] Output the emotion judgment result.

[0019] Furthermore, the step of substituting the weighted output connection weight into the trained emotion prediction model for emotion judgment includes:

[0020] Substitute the weighted output connection weights into the trained emotion prediction model to determine the activation state of the membrane potential, and obtain the activation state of the membrane potential of the delayed sampling reserve pool at the set time;

[0021] If the membrane potential at the set moment is in an activated state, the trained ridge regression algorithm model is used to estimate and judge the weighted output connection weights to obtain the emotion prediction result;

[0022] The emotion prediction result is outputted.

[0023] Furthermore, the step of using the trained ridge regression algorithm model to estimate and judge the weighted output connection weights includes:

[0024] Comparing the weighted output connection weight with a trained emotion dataset, wherein the emotion dataset includes a plurality of emotion data;

[0025] Obtaining the closest emotion data and obtaining the emotion data label;

[0026] The emotion prediction result is determined according to the label.

[0027] In order to solve the above technical problems, the present application also provides a neuromorphic brain-computer emotion perception device based on a delayed sampling reserve pool, comprising:

[0028] A graphene-silicon heterostructure sensor, wherein the graphene-silicon heterostructure sensor comprises a readout matrix unit and a silicon-based graphene field effect sensor;

[0029] The graphene-silicon heterostructure sensor is used to perform the method according to any one of claims 1, 3 and 4.

[0030] Furthermore, the device also includes an electrical signal data acquisition unit, which includes an EEG signal probe, and the electrical signal data acquisition unit is used to execute the method described in claim 2.

[0031] In order to solve the above technical problems, the present application also provides a neuromorphic brain-computer emotion perception device based on a delayed sampling reserve pool, wherein the device includes a computer device, and the computer device is used to execute the method described in any one of claims 5 to 7.

[0032] The neuromorphic brain-computer emotion perception method based on delayed sampling reserve pool provided in the present application not only has the characteristics of high-speed response, but also greatly improves the actual resolution and is very suitable for EEG signal processing by setting the graphene-silicon heterostructure sensor in the brain-computer emotion perception device; it also realizes the construction of a delayed sampling reserve pool of a single-node neuron, thereby realizing the data acquisition of virtual multi-neuron nodes under the condition of a single-node neuron; it not only greatly simplifies the hardware network structure on the data acquisition side, but also performs simple and effective data calculations on the device side after acquiring the data, thereby greatly improving the computing efficiency.

[0033] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following specifically cites the preferred embodiments and describes them in detail with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a data flow diagram of the neuromorphic brain-computer emotion perception method based on a delayed sampling reserve pool in an embodiment of the present application.

[0035] Figure 2 Schematic diagram of a graphene-silicon heterojunction sensor in a neuromorphic brain-computer emotion perception method based on a delayed sampling reserve pool in an embodiment of the present application.

[0036] Figure 3 Schematic diagram of the framework of a neuromorphic brain-computer emotion perception device based on a delayed sampling reserve pool according to an embodiment of the present application.

[0037] Figure 4 This is another data flow diagram of the neuromorphic brain-computer emotion perception method based on a delayed sampling reserve pool according to an embodiment of the present application. DETAILED DESCRIPTION

[0038] In order to further explain the technical means and effects adopted by the present application to achieve the intended application purpose, the present application is described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0039] Through the description of the specific implementation methods, a deeper and more specific understanding can be obtained of the technical means and effects adopted by the present application to achieve the predetermined purpose. However, the attached drawings are only for reference and illustration purposes and are not used to limit the present application.

[0040] Embodiment 1:

[0041] See also Figure 1 , Figure 2 and Figure 3 , an embodiment of the neuromorphic brain-computer emotion perception method based on a delayed sampling reserve pool of the present invention includes:

[0042] Figure 3 This is a schematic diagram of the hardware framework of the neuromorphic brain-computer emotion perception method based on the delayed sampling reserve pool of the present invention; in this embodiment, the hardware part includes a single-node neuron 10, a computer device 20 data-connected to the single-node neuron 10, and the single-node neuron 10 includes an electrical signal data acquisition unit 11 and a graphene-silicon heterostructure sensor 12.

[0043] Among them, the electrical signal data acquisition unit 11 of the neuromorphic brain-computer emotion perception device based on the delayed sampling reserve pool is attached to a certain emotional function part of the head of the tester who needs to perform brain-computer emotion perception. The electrical signal data acquisition unit 11 includes an electroencephalogram signal probe, and the electrical signal data acquisition unit 11 obtains electrical signal data of a preset part of the brain through the electroencephalogram signal probe.

[0044] The graphene-silicon heterostructure sensor 12 is data-connected to the electrical signal data acquisition unit 11. The graphene-silicon heterostructure sensor 12 includes a silicon-based graphene field effect sensor and an electrode array; wherein the electrode array forms a readout matrix unit.

[0045] See also Figure 2 Graphene-silicon heterostructure sensor is a field effect coupling device, which is often used in the field of broadband photoelectric sensors. It uses a probe to connect the field effect structure as an electrode to realize the delayed sense internal reserve pool structure of a single neuron. The field effect coupling device uses a deeply depleted silicon well to realize charge accumulation, and the single-layer graphene structure can directly read the result without loss. The whole structure is a typical graphene field effect tube. The reason for choosing silicon as a deep well is to take advantage of its high resistivity. Holes and excited electrons are captured by the silicon well and the graphene readout gate respectively. The hole capture process of the silicon well is linear, and the drain current of the p-doped graphene will be reduced, thereby reading the detection results. The high-speed response frequency of the device structure is above 1MHz, with high time resolution, which is very suitable for EEG signal processing. All sensors in the array are read randomly, which can well support asynchronous operations.

[0046] Specifically, Figure 2 The graphene-silicon heterostructure sensor structure shown in can be used as a single-node leaky integrated-and-fire (LIF) neuron. The specific integration process is: when the electrical signal stimulation is input, the potential will gradually accumulate in the well; the leakage process is: when the electrical signal stimulation is withdrawn, the accumulated potential will be gradually released, and the accumulated potential can be directly read from the graphene structure as the neuron sampling at that moment. When the threshold is exceeded, the two ends can be short-circuited to quickly release the potential as a neuron activation signal. In the delayed sampling reservoir structure of the single-node neuron, the activation effect can be simplified, so that the single-node neuron works in an unsaturated state, and the delayed sampling reservoir of the single-node neuron is made by the nonlinearity of the integration and leakage process. The graphene-silicon heterostructure sensor is used as a single-node LIF neuron to apply electrical stimulation and read out at a fixed sampling rate, and the well potential is freely leaked between two sampling intervals to simulate the leakage process of the biological neuron membrane potential.

[0047] See also Figure 1 , Figure 1 It is a data flow diagram of the neuromorphic brain-computer emotion perception method based on the delayed sampling reserve pool of the present invention;

[0048] 101. Obtaining electrical signal data;

[0049] The electrical signal data acquisition unit 11 acquires the electrical signal data of a preset part of the brain through the EEG signal probe and transmits the data to the graphene-silicon heterostructure sensor.

[0050] 102. Storing electrical signal data and establishing a delayed sampling reserve pool;

[0051] When the graphene-silicon heterostructure sensor samples the electrical signal data at a preset location in the brain, it samples at a predetermined frequency, temporarily stores the sampled electrical signal data locally, and establishes a delayed sampling reserve pool.

[0052] 103. Outputting the stored electrical signal data to a readout matrix unit;

[0053] When the electrical signal data in the delayed sampling reserve pool reaches a predetermined amount, an N-dimensional output tensor is formed and output to the readout matrix unit.

[0054] 104. Preliminary operations;

[0055] The readout matrix unit receives the N-dimensional output tensor output by the delayed sampling reservoir, and the readout matrix unit performs a matrix calculation on the input N-dimensional output tensor and the preset parameter matrix W of the readout matrix unit. T Perform multiplication to obtain the weighted output connection weight.

[0056] 105. Output the weighted output connection weight to the computer device;

[0057] After obtaining the weighted output connection weight, the weighted output connection weight is output to the computer device 20, wherein the computer device 20 is pre-set with a trained neuromorphic brain-computer emotion perception model based on a delayed sampling reserve pool, and the weighted output connection weight is substituted into the pre-trained emotion perception model for calculation and analysis, and the emotion judgment result is predicted and output.

[0058] In this embodiment, this method, by setting the use of graphene-silicon heterostructure sensors in the brain-computer emotion perception device of a single-node neuron, not only has the characteristics of high-speed response, greatly improves the actual resolution and is very suitable for EEG signal processing; it also realizes the construction of a delayed sampling reserve pool of a single-node neuron, thereby realizing data acquisition of virtual multi-neuron nodes under the condition of a single-node neuron; it not only greatly simplifies the hardware network structure on the data acquisition side, but also performs simple and effective data calculations on the device side after acquiring the data, thereby greatly improving the computing efficiency.

[0059] Embodiment 2:

[0060] The present invention discloses a neuromorphic brain-computer emotion perception method based on a delayed sampling reserve pool, specifically:

[0061] 201. Receive weighted output connection weights;

[0062] The computer device receives the weighted output connection weights output by the graphene-silicon heterostructure sensor;

[0063] 202. Substituting the weighted output connection weight into the trained emotion prediction model to perform emotion judgment;

[0064] The weighted output connection weight is substituted into the trained emotion prediction model to judge the activation state of the membrane potential, and the activation state of the membrane potential of the delayed sampling reserve pool at the set time is obtained; if the membrane potential at the set time is in the activated state, the trained ridge regression algorithm model is used to estimate and judge the weighted output connection weight, specifically including: comparing the weighted output connection weight with the trained emotion data set, wherein the emotion data set includes multiple emotion data; obtaining the emotion data closest to the weighted output connection weight, and obtaining the emotion data label; and then judging the emotion prediction result based on the label.

[0065] The specific training process of the emotion prediction model is described in detail in Example 3 and will not be repeated here.

[0066] 203. Output the emotion judgment result.

[0067] In this embodiment, by establishing and training the aforementioned emotion perception model on the computer side, the weighted output connection weights obtained by weighted summation operation of multiple electrical signal data acquired by the electrical signal data acquisition unit in the graphene-silicon heterostructure sensor are compared with the trained emotion data set, and the label closest to the emotion data is obtained to obtain the emotion prediction result; the response speed is greatly improved.

[0068] Embodiment three:

[0069] The present invention discloses an application example of a neuromorphic brain-computer emotion perception method based on a delayed sampling reserve pool, specifically:

[0070] An electrical signal data acquisition unit of a neuromorphic brain-computer emotion perception device based on a delayed sampling reserve pool is attached to a certain emotional function part of the head of a tester who needs to perform brain-computer emotion perception. The electrical signal data acquisition unit includes an electroencephalogram (EEG) signal probe, and the electrical signal data acquisition unit acquires electrical signal data of a preset part of the brain through the EEG signal probe.

[0071] The neuromorphic brain-computer emotion perception device based on the delayed sampling reserve pool also includes a graphene-silicon heterostructure sensor, which is data-connected to the electrical signal data acquisition unit. The graphene-silicon heterostructure sensor includes a silicon-based graphene field effect sensor and an electrode array; wherein the electrode array forms a readout matrix unit.

[0072] After the electrical signal data acquisition unit acquires the electrical signal data of the preset part of the brain through the EEG signal probe, the data is transmitted to the graphene-silicon heterostructure sensor; when the graphene-silicon heterostructure sensor samples the electrical signal data of the preset part of the brain, it samples according to a predetermined frequency, temporarily stores the sampled electrical signal data locally, and establishes a delayed sampling reserve pool; after the electrical signal data in the delayed sampling reserve pool reaches a predetermined number, an initial output connection weight is formed, and an N-dimensional output tensor is formed to the readout matrix unit, and the readout matrix unit performs a preliminary operation on the input N-dimensional output tensor to obtain a weighted output connection weight, wherein the preliminary operation includes: comparing the N-dimensional output tensor with W T A multiplication operation is performed to obtain a weighted output connection weight, and the weighted output connection weight is output to a computer device, wherein the computer device is pre-set with a trained neuromorphic brain-computer emotion perception model based on a delayed sampling reserve pool, and the weighted output connection weight is substituted into the pre-trained emotion perception model for calculation and analysis, and the emotion judgment result is predicted and output.

[0073] The process of establishing and training the aforementioned emotion perception model is as follows:

[0074] The establishment and training of the emotion perception model is divided into two parts, the first part is on the graphene-silicon heterostructure sensor side, and the second part is on the computer equipment side.

[0075] By fusing the classic dataset and the actual sampling dataset, an emotion dataset is established and dataset labels are set. The labels are used to classify the emotion data. Specifically: simulation is performed on a graphene-silicon heterostructure sensor, direct feeding is performed using the classic dataset, the readout network unit is trained to complete the emotion recognition task, an emotion dataset is established and dataset labels are set. The labels are used to classify the emotion data; training parameters are downloaded, and the emotion recognition task is verified using real data, an emotion dataset is established and dataset labels are set; and the parameters of the readout network unit are set according to the emotion dataset label, and the performance of the emotion recognition brain-computer interface is debugged as a whole.

[0076] On the graphene-silicon heterostructure sensor side, it includes a delay reservoir and a readout network unit.

[0077] Determine the membrane potential state of the delayed sampling reservoir at time t, specifically:

[0078] x(t)=sig(W x x(t-1)+u(t))……(1)

[0079] W X is the node in the reserve pool, W of a single-node neuron XThere is only one value. x(t) is the membrane potential state of the delayed sampling reservoir at time t. U(t) is the input value of the input sensor at time t. Sig is a sign function, used as an activation function. When the independent variable is greater than or equal to zero, the dependent variable is 1, and when the independent variable is less than zero, the dependent variable is 0. It is used to simulate the activation state of neurons.

[0080] Substitute the membrane potential state x(t) of the delayed sampling reserve pool at time t in formula (1) into formula (2) to calculate the emotional problem description data, that is, the weight W connected to the weighted output out (t) performing matrix multiplication operations;

[0081] Specific:

[0082] y(t)=W out x(t)+b……(2)

[0083] The readout network unit training of the delayed sampling reserve pool adopts the ridge regression algorithm, and finally adjusts the output connection weight. Specifically, the process of training the delayed sampling reserve pool calculation is to calculate the output connection weight W of the weighted sum of the transient responses of N virtual nodes at time t. out (t) is as close as possible to the target matrix y(t); and the emotional problem description data is obtained. In this embodiment, b=0.

[0084] Among them, read out the final output connection weight W of the network unit out (t) can be expressed by the following formula:

[0085] W out (t) = argmin || y(t) - W out x(t)|| 2 +λ||W out ||2……(3)

[0086] Where x(t) is the sum of the transient responses of all virtual nodes in the delayed sampling reserve pool, W out (t) is the weighted output connection weight W obtained by weighted summation of the transient responses of N virtual nodes at time t. out (t), since we only focus on the state at time t, we will out (t) is denoted as W out , λ is the ridge parameter in the ridge regression algorithm.

[0087] Finally, the weighted connection weight W calculated by the read network unit is out The data is sent to the computer for offline training. The mathematical formula of the training model objective function is:

[0088] ((X T X+λ) －1 X TY)……(4)

[0089] Where x is x(t) in formula (3), y is y(t) in formula (3), and I is the unit matrix. During training, the weighted connection weight W is adjusted by continuously adjusting the operation parameters. out Optimize so that the optimized weighted connection weight W out Getting closer and closer to the target weight connection weight, that is, the weighted connection weight W out The final estimate of .

[0090] After the emotion perception model is trained, the electrical signal data of the single-node neuron is sampled with a fixed step length. Each sampling is used as a virtual node, and multiple sampling nodes form a delayed sampling reserve pool. The electrical signal data of the delayed sampling reserve pool is then fed into the readout matrix unit for preliminary calculations, and the weighted connection weights are output. The trained emotion perception model is used for judgment, and finally the emotion judgment result is output. Specifically: In the operation of a single-node neural network, the membrane potential of a single-node neuron is collected N times as N virtual neuron nodes, and then the N sampling points are recorded as s1 s2 s3... sN, forming an output tensor as the state response output of the delayed sampling reserve pool. The N-dimensional output tensor is related to W T Perform matrix multiplication to obtain the final output of m-dimensional data (e.g., classification as epilepsy or normal, m=2). Reading the preset parameter matrix of the matrix unit is a matrix multiplication and an adder. In actual engineering use, a programmable logic device (Field-Programmable Gate Array, FPGA) is used for simulation. That is, the formula y(t)=W out The m-dimensional data in (t)+b is output to the pre-trained readout network unit for data judgment to obtain the emotional question description data, that is, the weighted output connection weight, and then the weighted output connection weight is brought into the emotional prediction model for analysis to finally obtain the emotional judgment result.

[0091] For example, in the monitoring scenario of neurodegenerative diseases, the emotion dataset is the classification label of EEG and neurodegenerative stages. Assuming that 10s is used as the time window, neurodegenerative diseases can be classified after training. In actual deployment, a 10s time window and a time step of sampling once every 1 minute are used to collect the electrical signal dataset in units of 10 windows. The classification labels are determined according to the model inference results, and the final prediction value of the model is determined by voting. Thus, the monitoring of neurodegenerative diseases is achieved. In actual use, parameter scanning or fine-tuning is performed on hyperparameters such as window length, sampling step, and the final number of sampling windows. The model inference accuracy is compared according to the model inference results and other medical indicators, and the optimal inference hyperparameters are selected to improve the inference strategy. Among them, 10s, 1min, and 10 windows are values ​​taken based on experience, which need to be adjusted in actual applications to improve the performance of the emotion recognition brain-computer interface.

[0092] In the embodiment of the present invention, by setting the use of a graphene-silicon heterostructure sensor in the brain-computer emotion perception device of a single-node neuron, it not only has the characteristics of high-speed response, greatly improves the actual resolution and is very suitable for EEG signal processing; it also realizes the construction of a delayed sampling reserve pool of a single-node neuron, thereby realizing data acquisition of a virtual multi-neuron node under the condition of a single-node neuron; it not only greatly simplifies the hardware network structure on the data acquisition side, but also performs simple and effective data calculations on the device side after acquiring the data, thereby greatly improving the computing efficiency.

[0093] Embodiment 4:

[0094] See also Figure 3 The neuromorphic brain-computer emotion perception device based on the delayed sampling reserve pool in this embodiment includes: a node neuron 10, and a single node neuron 10 includes an electrical signal data acquisition unit 11 and a graphene-silicon heterostructure sensor 12.

[0095] Among them, the electrical signal data acquisition unit 11 of the neuromorphic brain-computer emotion perception device based on the delayed sampling reserve pool is attached to a certain emotional function part of the head of the tester who needs to perform brain-computer emotion perception. The electrical signal data acquisition unit 11 includes an electroencephalogram signal probe, and the electrical signal data acquisition unit 11 obtains electrical signal data of a preset part of the brain through the electroencephalogram signal probe.

[0096] The graphene-silicon heterostructure sensor 12 is data-connected to the electrical signal data acquisition unit 11. The graphene-silicon heterostructure sensor 12 includes a silicon-based graphene field effect sensor and an electrode array; wherein the electrode array forms a readout matrix unit.

[0097] The node neuron 10 is used to execute the following Figure 1 The instructions described in are not repeated here.

[0098] In the embodiment of the present invention, by setting the use of a graphene-silicon heterostructure sensor in the brain-computer emotion perception device of a single-node neuron, it not only has the characteristics of high-speed response, greatly improves the actual resolution and is very suitable for EEG signal processing; it also realizes the construction of a delayed sampling reserve pool of a single-node neuron, thereby realizing data acquisition of a virtual multi-neuron node under the condition of a single-node neuron; it not only greatly simplifies the hardware network structure on the data acquisition side, but also performs simple and effective data calculations on the device side after acquiring the data, thereby greatly improving the computing efficiency.

[0099] Embodiment five:

[0100] See also Figure 3 The neuromorphic brain-computer emotion perception device based on delayed sampling reserve pool in this embodiment includes: a computer device 20, in which an emotion perception model as in Example 3 is established and trained.

[0101] The computer device 20 is used to execute Figure 4 The instructions described in are not repeated here.

[0102] In the embodiment of the present invention, by setting the use of a graphene-silicon heterostructure sensor in the brain-computer emotion perception device of a single-node neuron, it not only has the characteristics of high-speed response, greatly improves the actual resolution and is very suitable for EEG signal processing; it also realizes the construction of a delayed sampling reserve pool of a single-node neuron, thereby realizing data acquisition of a virtual multi-neuron node under the condition of a single-node neuron; it not only greatly simplifies the hardware network structure on the data acquisition side, but also performs simple and effective data calculations on the device side after acquiring the data, thereby greatly improving the computing efficiency.

[0103] The above is only a preferred embodiment of the present application and does not constitute any form of limitation to the present application. Although the present application has been disclosed as a preferred embodiment as above, it is not intended to limit the present application. Any technician familiar with the profession can make some changes or modifications to equivalent embodiments of the technical contents disclosed above without departing from the scope of the technical solution of the present application. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present application without departing from the content of the technical solution of the present application are still within the scope of the technical solution of the present application.

Claims

1. A neuromorphic brain-computer emotion perception method based on delayed sampling reserve pool, characterized in that: The method includes a graphene-silicon heterostructure sensor, wherein the graphene-silicon heterostructure sensor includes a readout matrix unit; the method includes: The graphene-silicon heterostructure sensor samples the electrical signal data of a preset part of the brain at a preset frequency to establish a delayed sampling reserve pool; The readout matrix unit then performs a preliminary operation on the electrical signal data in the delayed sampling reserve pool to obtain a weighted output connection weight; The weighted output connection weight is output to a computer device, so that the computer device performs emotion judgment according to the weighted output connection weight and outputs an emotion judgment result.

2. The method according to claim 1, characterized in that The method also includes an electrical signal data acquisition unit connected to the graphene-silicon heterostructure sensor, wherein the electrical signal data acquisition unit acquires electrical signal data of a preset part of the brain and transmits the electrical signal data to the graphene-silicon heterostructure sensor.

3. The method according to claim 1, characterized in that The step of performing preliminary calculations on the electrical signal data in the delayed sampling reserve pool by the readout matrix unit to obtain weighted output connection weights comprises: The readout network unit receives transient response values ​​of a plurality of virtual neuron nodes in a delayed sampling reserve pool; Combining the transient response values ​​of the plurality of virtual neuron nodes into initial output connection values; The initial output connection values ​​are weighted and summed to obtain weighted output connection weights.

4. The method according to claim 3, characterized in that The step of performing a weighted sum operation on the initial output connection values ​​to obtain weighted output connection weights is specifically as follows: the initial output connection values ​​form a multi-dimensional output tensor, and the multi-dimensional output tensor is summed with the weights (W T ) to obtain the weighted output connection weight.

5. A neuromorphic brain-computer emotion perception method based on delayed sampling reserve pool, characterized in that: include: The computer device receives the weighted output connection weights output by the graphene-silicon heterostructure sensor; Substituting the weighted output connection weight into the trained emotion prediction model to perform emotion judgment; Output the emotion judgment result.

6. The method according to claim 5, characterized in that The step of substituting the weighted output connection weight into the trained emotion prediction model for emotion judgment comprises: Substitute the weighted output connection weights into the trained emotion prediction model to determine the activation state of the membrane potential, and obtain the activation state of the membrane potential of the delayed sampling reserve pool at the set time; If the membrane potential at the set moment is in an activated state, the trained ridge regression algorithm model is used to estimate and judge the weighted output connection weights to obtain the emotion prediction result; The emotion prediction result is outputted.

7. The method according to claim 6, characterized in that The step of using the trained ridge regression algorithm model to estimate and judge the weighted output connection weights comprises: Comparing the weighted output connection weight with a trained emotion dataset, wherein the emotion dataset includes a plurality of emotion data; Obtaining the closest emotion data and obtaining the emotion data label; The emotion prediction result is determined according to the label.

8. A neuromorphic brain-computer emotion perception device based on a delayed sampling reserve pool, characterized in that: The device comprises: A graphene-silicon heterostructure sensor, wherein the graphene-silicon heterostructure sensor comprises a readout matrix unit and a silicon-based graphene field effect sensor; The graphene-silicon heterostructure sensor is used to perform the method according to any one of claims 1, 3 and 4.

9. The device according to claim 8, characterized in that The device also includes an electrical signal data acquisition unit, which includes an EEG signal probe, and the electrical signal data acquisition unit is used to execute the method according to claim 2.

10. A neuromorphic brain-computer emotion perception device based on a delayed sampling reserve pool, characterized in that: The device comprises a computer device, and the computer device is used to execute the method according to any one of claims 5 to 7.

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