Nerve electrophysiology spike signal sorting method based on small-batch K-means clustering

Abstract

The invention discloses a neural electrophysiological spike signal sorting method based on small-batch K-means clustering, and the method comprises the steps: 1, collecting electrophysiological data, and carrying out the preprocessing of the electrophysiological data, so as to eliminate the noise and enhance the signal quality; 2, an optimized clustering algorithm is adopted for small-batch K mean values, a general template is obtained from data, principal component feature extraction is carried out on detected L channels with the nearest peaks based on the general template, and an updated general template is obtained; 3, based on a graph-based clustering algorithm, performing accurate clustering on the updated universal template so as to obtain a learning template; and 4, performing peak detection by using a learning template, and outputting a final peak clustering result in combination with the graph clustering method in the step 3. According to the method, the processing speed and the classification accuracy of the electrophysiological data can be remarkably improved, the neuron activity can be quickly and accurately recognized, an efficient data analysis tool is provided for neuroscience research, the time cost of data processing is remarkably reduced, and the research efficiency is improved.

Description

Technical Field

[0001] The invention belongs to the field of neuroscience, and specifically is a neural electrophysiological spike signal sorting method based on small batch K-means clustering. Background Art

[0002] Neuroelectrophysiological signal analysis is an important field in neuroscience research, which involves recording and sorting electrical signals generated by neuronal activity. With the development of neuroscience and biomedical engineering, the demand for accurate sorting of neuroelectrophysiological signals is growing. However, existing technologies still have significant deficiencies in processing speed and classification accuracy, which limits their effectiveness in practical applications.

[0003] First, traditional electrophysiological signal processing methods usually rely on manual operation and empirical judgment, which is not only time-consuming and labor-intensive, but also easily affected by the subjective factors of the operator, making it difficult to ensure the consistency and accuracy of the results. Especially when processing a large amount of electrophysiological data, the efficiency of manual analysis is extremely low and cannot meet the high requirements of modern neuroscience research on data processing speed. Secondly, the existing signal classification algorithms mostly use traditional clustering methods, such as K-means clustering. When processing high-dimensional data, these methods often face problems such as improper selection of cluster centers and local optimal solutions, which affects the accuracy of the classification results. In addition, when facing complex electrophysiological signals, traditional clustering algorithms are difficult to effectively distinguish different types of signals, especially when there is a high degree of similarity or noise interference between signals, the classification effect is often not ideal. In terms of signal preprocessing, the existing technology usually uses simple filtering and denoising methods. These methods may not be able to effectively eliminate noise when processing complex signals, resulting in a decrease in the accuracy of subsequent analysis. Especially in neural electrophysiological signals, the presence of noise can significantly affect the detection and classification of spike signals, thereby affecting the understanding of neuronal activity. In addition, with the advancement of electrophysiological data acquisition technology, the amount of data has exploded, and traditional methods are unable to cope with large-scale data and cannot quickly and accurately extract useful information. This not only increases the time cost of data processing, but also limits researchers' in-depth analysis and understanding of the data.

[0004] In summary, the existing technologies have significant deficiencies in the processing speed and classification accuracy of electrophysiological signals. These problems limit the in-depth research of neuroscience and the development of clinical applications. Therefore, it is urgent to develop a new method for sorting neuroelectrophysiological spike signals to improve the speed of data processing and the accuracy of classification, and to achieve rapid and accurate identification of neuronal activity. Summary of the invention

[0005] The present invention aims to solve the deficiencies of the above-mentioned prior art and proposes a neural electrophysiological spike signal sorting method based on small batch K-means clustering, in order to significantly improve the processing speed and classification accuracy of electrophysiological data, thereby realizing rapid and accurate identification of neuronal activity.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme:

[0007] The invention discloses a method for sorting neural electrophysiological spike signals based on small batch K-means clustering, which comprises the following steps:

[0008] Step 1: Collect the electrophysiological signals of the mouse visual cortex and perform preprocessing, including: common average reference, time filtering, channel whitening and drift correction, to obtain the corrected electrophysiological signals ;

[0009] Step 2: Use the mini-batch K-means clustering algorithm to Process and obtain the cluster center and serves as a general template;

[0010] Step 3: Use a common template right Perform peak detection and obtain K peaks closest to the detected peaks The peak signals of each channel are extracted and the principal component features are extracted to obtain the corresponding The peak features of the channels are collected, and the peak feature with the largest amplitude is assigned to the corresponding cluster center, so as to obtain the updated general template ,in, Indicates electrophysiological spike features, and , Indicates The electrophysiological spike features are The value on each channel, Indicates the number of channels to which the electrophysiological spike signal is extended;

[0011] Step 4: Based on the graph clustering algorithm Process and obtain the learning template ;

[0012] Step 5: Based on the learning template right After the spike detection, clustering is performed according to the process of step 4 to output the final electrophysiological spike signal clustering set. ,in, Indicates Electrophysiological spike signal clusters, , .

[0013] The neural electrophysiological spike signal sorting method based on small batch K-means clustering described in the present invention is also characterized in that step 1 includes the following steps:

[0014] Step 1.1: Collect electrophysiological signals from the mouse visual cortex , ,in, Indicates The electrophysiological signals at each time point, and , , Indicates The time point Electrophysiological signals on channels, Represents the total duration of the electrophysiological signal, Represents the total number of channels of electrophysiological signals, represents transpose;

[0015] Step 1.2: Calculate the average value of the electrophysiological signal of the i-th channel ;

[0016] Step 1.3: Construct an average reference signal ;

[0017] Step 1.4: Calculate the electrophysiological signal after common mean reference ;

[0018] Step 1.5: Perform time filtering to obtain the filtered electrophysiological signal ;

[0019] Step 1.6: Perform channel whitening to obtain the whitened electrophysiological signal ;

[0020] Step 1.7: Perform drift correction to obtain the corrected electrophysiological signal .

[0021] Furthermore, step 2 includes the following steps:

[0022] Step 2.1: Setup is the number of cluster centers, and the cluster centers are initialized using the K-Means++ algorithm , ,in, Indicates cluster centers, and ,in, Indicates The cluster center is in The value on the dimensional channel;

[0023] Step 2.2: From Randomly select a dimension of size A subset of electrophysiological signals , ,in, Indicates The electrophysiological signal after correction at each time point, and , , Indicates The time point The corrected electrophysiological signal on each channel is represents the total duration of the electrophysiological signal subset Z, , represents the total number of channels of the electrophysiological signal subset Z, ;

[0024] Step 2.3: Calculation The distance from each cluster center and Assign to the cluster center corresponding to the minimum distance, so that Each electrophysiological signal in is assigned to the corresponding cluster center;

[0025] Step 2.4: Update the kth cluster center using formula (1) , get the updated kth cluster center :

[0026] (1)

[0027] In formula (1), yes The corresponding electrophysiological signal clustering, yes The number of electrophysiological signals in represents the adaptive learning rate;

[0028] Step 2.5: If and The difference between them meets the threshold , it means that the cluster center does not change, and the updated cluster center is output This is the general template for electrophysiological signal spike detection, where , Indicates the updated Cluster Centers In the The value on the dimension channel; if the threshold is not met , then Assignment Then, return to step 2.2 and execute sequentially.

[0029] Furthermore, step 4 includes the following steps:

[0030] Step 4.1: From Randomly select a dimension of size A subset of electrophysiological spike features , ,in, Indicates electrophysiological spike features, and , , Indicates The electrophysiological spike features are The value on each channel, represents the number of spike features in the selected electrophysiological spike subset, , The number of channels representing the subset of electrophysiological spikes, ;

[0031] Step 4.2: The corresponding cluster set is recorded as ,in, for Clustering of spike signals;

[0032] Step 4.3: Calculate clusters based on correlation coefficient method The similarity between any two electrophysiological spike signals in is calculated. If the similarity is greater than the set threshold, the corresponding two electrophysiological spike signals are connected by edges; otherwise, the edges are not connected.

[0033] Step 4.4: Use equation (2) to construct a modular cost function :

[0034] (2)

[0035] In formula (2), It is clustering The total number of edges connecting all pairs of spike signals in , It is clustering The total number of edges connecting pairs of inner spikes, It is clustering The sum of the number of edges corresponding to each electrophysiological spike signal in is a parameter that controls the number of clusters;

[0036] Step 4.5: Calculate electrophysiological spike characteristics based on the correlation coefficient method Clustering with electrophysiological spike signals The number of edges connected to the remaining spike signals in and Electrophysiological spike features Clustering of spike signals The number of edges connected to the remaining spike signals in ;

[0037] like Make , then the electrophysiological spike signal Join Otherwise, remain unchanged, thus obtaining the updated electrophysiological signal clustering set ,in, Indicates Updated electrophysiological signal clustering, Indicates An updated electrophysiological signal clustering;

[0038] Step 4.6: Calculate according to formula (3) and Modularity cost function value before merging :

[0039] (3)

[0040] In formula (3), It is clustering The total number of edges connecting pairs of inner spikes, express The total number of edges connecting pairs of inner spikes, yes The sum of the number of edges corresponding to each electrophysiological spike signal in yes The sum of the number of edges corresponding to each electrophysiological spike signal in Represents a fixed constant;

[0041] Step 4.7: Calculate according to formula (4) and The modular cost function value after the merger :

[0042] (4)

[0043] In formula (4), It is clustering and the total number of edges connecting pairs of inner spikes;

[0044] Step 4.8: Calculate the difference before and after merging according to formula (5) :

[0045] (5)

[0046] Step 4.9: When When and Merge into a new cluster;

[0047] Step 4.10: Follow the process from Steps 4.1 to 4.9 to The clusters containing the electrophysiological spike features in the merging are merged to obtain the new clusters recorded as ,in, is the new cluster after the mth merger, The corresponding cluster center is recorded as , thus The corresponding cluster center and serve as a learning template; , ; M represents the number of new clusters after merging.

[0048] The electronic device of the present invention includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the neural electrophysiological spike signal sorting method, and the processor is configured to execute the program stored in the memory.

[0049] The present invention provides a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and the computer program executes the steps of the neural electrophysiological spike signal sorting method when the computer program is executed by a processor.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] 1. Step 2 of the present invention optimizes the electrophysiological signal data by adopting a small batch K-means clustering algorithm. Compared with the traditional K-means clustering algorithm, the small batch K-means clustering algorithm can process large-scale data sets more quickly because it only processes a subset of the data in each iteration, rather than the entire data set. This method reduces the demand for computing resources and increases the speed of data processing. At the same time, by introducing an adaptive learning rate, the present invention can more accurately update the cluster center, thereby improving the accuracy of electrophysiological signal classification. This method overcomes the problems of slow processing speed and insufficient classification accuracy in the prior art, and significantly improves the processing efficiency of electrophysiological data and the reliability of the results.

[0052] 2. Step three of the present invention further optimizes the sorting process of electrophysiological spike signals by combining graph clustering algorithms and principal component feature extraction technology. A universal template is used for spike detection, and principal component feature extraction is performed on the detected spike signal. This step helps to extract more representative signal features, enhance the signal discrimination, and update the universal template. A graph clustering algorithm is used to accurately cluster the updated universal template to obtain a learning template. This method effectively solves the problem of difficulty in distinguishing highly similar signals in the prior art, realizes rapid and accurate identification of neuronal activity, and provides an efficient data analysis tool for neuroscience research. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 The present invention is a flowchart of a method for sorting neural electrophysiological spike signals based on small batch K-means clustering. DETAILED DESCRIPTION

[0054] In this embodiment, a neural electrophysiological spike signal sorting method based on small batch K-means clustering is provided. Figure 1 As shown, the following steps are included:

[0055] Step 1: Collect the electrophysiological signals of the mouse visual cortex and perform preprocessing, including: common average reference, time filtering, channel whitening and drift correction, to obtain the corrected electrophysiological signals :

[0056] Step 1.1: Collect electrophysiological signals from the mouse visual cortex , ,in, Indicates The electrophysiological signals at each time point, and , , Indicates The time point Electrophysiological signals on channels, Represents the total duration of the electrophysiological signal, Represents the total number of channels of electrophysiological signals, Indicates transpose.

[0057] Step 1.2: Calculate the average value of the electrophysiological signal of the i-th channel ;

[0058] Step 1.3: Construct an average reference signal ;

[0059] Step 1.4: Calculate the electrophysiological signal after common mean reference ;

[0060] Step 1.5: Use equation (1) to calculate the second-order Butterworth filter impulse response and its transfer function , thus calculating the filtered signal through the convolution operation of formula (2) :

[0061] (1)

[0062] In formula (1), represents the cut-off frequency, represents the unit step function.

[0063] (2)

[0064] In formula (2), Represents a convolution operation.

[0065] Step 1.6: Perform channel whitening to obtain the whitened electrophysiological signal ;

[0066] Step 1.6.1: Calculate the filtered signal according to equation (3) The covariance matrix of :

[0067] (3)

[0068] In formula (3), yes The transpose of .

[0069] Step 1.6.2: Construct a diagonal matrix according to formula (4) :

[0070] (4)

[0071] In formula (4), Represents the covariance matrix The characteristic value of .

[0072] Step 1.6.3: Calculate the diagonal matrix according to formula (5) The inverse square root of each eigenvalue in constructs a new diagonal matrix :

[0073] (5)

[0074] Step 1.6.4: Calculate the whitening matrix according to formula (6) :

[0075] (6)

[0076] In formula (4), Represents the covariance matrix The feature vector of .

[0077] Step 1.6.5: Calculate the whitened electrophysiological signal according to formula (7) :

[0078] (7)

[0079] Step 1.7: Perform drift correction to obtain the corrected electrophysiological signal .

[0080] Step 2: Use the mini-batch K-means clustering algorithm to Process and obtain the cluster center And as a general template:

[0081] Step 2.1: Setup is the number of cluster centers, and the cluster centers are initialized using the K-Means++ algorithm , ,in, Indicates cluster centers, and ,in, Indicates The cluster center is in The value on the dimensional channel.

[0082] Step 2.2: From Randomly select a dimension of size A subset of electrophysiological signals , ,in, Indicates The electrophysiological signal after correction at each time point, and , , Indicates The time point The corrected electrophysiological signal on each channel is represents the total duration of the electrophysiological signal subset Z, , represents the total number of channels of the electrophysiological signal subset Z, .

[0083] Step 2.3: Calculation The distance from each cluster center and Assign to the cluster center corresponding to the minimum distance, so that Each electrophysiological signal in is assigned to the corresponding cluster center;

[0084] Step 2.4: Update the kth cluster center using formula (1) , get the updated kth cluster center :

[0085] (8)

[0086] In formula (8), yes The corresponding electrophysiological signal clustering, yes The number of electrophysiological signals in Represents the adaptive learning rate.

[0087] Step 2.5: If and The difference between them meets the threshold , it means that the cluster center does not change, and the updated cluster center is output This is the general template for electrophysiological signal spike detection, where , Indicates the updated Cluster Centers In the The value on the dimension channel; if the threshold is not met , then Assignment Then, return to step 2.2 and execute sequentially.

[0088] Step 3: Use a common template right Perform peak detection and obtain K peaks closest to the detected peaks The peak signals of each channel are extracted and the principal component features are extracted to obtain the corresponding The peak features of the channels are collected, and the peak feature with the largest amplitude is assigned to the corresponding cluster center, so as to obtain the updated general template ,in, Indicates electrophysiological spike features, and , Indicates The electrophysiological spike features are The value on each channel, Indicates the number of channels to which the electrophysiological spike signal is expanded.

[0089] Step 4: Based on the graph clustering algorithm Processing, get the learning template :

[0090] Step 4.1: From Randomly select a dimension of size A subset of electrophysiological spike features , ,in, Indicates electrophysiological spike features, and , , Indicates The electrophysiological spike features are The value on each channel, represents the number of spike features in the selected electrophysiological spike subset, , The number of channels representing the subset of electrophysiological spikes, .

[0091] Step 4.2: The corresponding cluster set is recorded as ,in, for Clustering of spike signals;

[0092] Step 4.3: Calculate clusters based on correlation coefficient method The similarity between any two electrophysiological spike signals in is calculated. If the similarity is greater than the set threshold, the corresponding two electrophysiological spike signals are connected by edges; otherwise, the edges are not connected.

[0093] Step 4.4: Use equation (2) to construct a modular cost function :

[0094] (9)

[0095] In formula (9), It is clustering The total number of edges connecting all pairs of spike signals in , It is clustering The total number of edges connecting pairs of inner spikes, It is clustering The sum of the number of edges corresponding to each electrophysiological spike signal in is a parameter that controls the number of clusters.

[0096] Step 4.5: Calculate electrophysiological spike characteristics based on the correlation coefficient method Clustering with electrophysiological spike signals The number of edges connected to the remaining spike signals in and Electrophysiological spike features Clustering of spike signals The number of edges connected to the remaining spike signals in ;

[0097] like Make , then the electrophysiological spike signal Join Otherwise, remain unchanged, thus obtaining the updated electrophysiological signal clustering set ,in, Indicates Updated electrophysiological signal clustering, Indicates An updated electrophysiological signal clustering.

[0098] Step 4.6: Calculate according to formula (3) and Modularity cost function value before merging :

[0099] (10)

[0100] In formula (10), It is clustering The total number of edges connecting pairs of inner spikes, express The total number of edges connecting pairs of inner spikes, yes The sum of the number of edges corresponding to each electrophysiological spike signal in yes The sum of the number of edges corresponding to each electrophysiological spike signal in Indicates a fixed constant.

[0101] Step 4.7: Calculate according to formula (4) and The modular cost function value after the merger :

[0102] (11)

[0103] In formula (11), It is clustering and The total number of edges connecting pairs of inner spikes.

[0104] Step 4.8: Calculate the difference before and after merging according to formula (12) :

[0105] (12)

[0106] Step 4.9: When When and Merge into a new cluster;

[0107] Step 4.10: Follow the process from Steps 4.1 to 4.9 to The clusters containing the electrophysiological spike features in the merging are merged to obtain the new clusters recorded as ,in, is the new cluster after the mth merger, M represents the number of new clusters after the merger; The corresponding cluster center is recorded as , thus The corresponding cluster center and serve as a learning template; , ;

[0108] Step 5: Based on the learning template right After the spike detection, clustering is performed according to the process of step 4 to output the final electrophysiological spike signal clustering set. ,in, Indicates Electrophysiological spike signal clusters, , .

[0109] In this embodiment, an electronic device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the above method, and the processor is configured to execute the program stored in the memory.

[0110] In this embodiment, a computer-readable storage medium stores a computer program on the computer-readable storage medium, and the computer program executes the steps of the above method when executed by a processor.

Claims

1. A neural electrophysiological spike signal sorting method based on small batch K-means clustering, characterized in that: The steps include: Step 1: Collect the electrophysiological signals of the mouse visual cortex and perform preprocessing, including: common average reference, time filtering, channel whitening and drift correction, to obtain the corrected electrophysiological signals ; Step 2: Use the mini-batch K-means clustering algorithm to Process and obtain the cluster center and as a general template; Step 3: Use a common template right Perform peak detection and obtain K peaks closest to the detected peaks The peak signals of each channel are extracted and the principal component features are extracted to obtain the corresponding The peak features of the channels are collected, and the peak feature with the largest amplitude is assigned to the corresponding cluster center, so as to obtain the updated general template ,in, Indicates electrophysiological spike features, and , Indicates The electrophysiological spike features are The value on each channel, Indicates the number of channels to which the electrophysiological spike signal is extended; Step 4: Based on the graph clustering algorithm Process and obtain the learning template ; Step 5: Based on the learning template right After the spike detection, clustering is performed according to the process of step 4 to output the final electrophysiological spike signal clustering set. ,in, Indicates Electrophysiological spike signal clusters, , .

2. According to claim 1, a neural electrophysiological spike signal sorting method based on small batch K-means clustering is characterized in that: Step 1 includes the following steps: Step 1.1: Collect electrophysiological signals from the mouse visual cortex , ,in, Indicates The electrophysiological signals at each time point, and , , Indicates The time point Electrophysiological signals on channels, Represents the total duration of the electrophysiological signal, Represents the total number of channels of electrophysiological signals, represents transpose; Step 1.2: Calculate the average value of the electrophysiological signal of the i-th channel ; Step 1.3: Construct an average reference signal ; Step 1.4: Calculate the electrophysiological signal after common mean reference ; Step 1.5: Perform time filtering to obtain the filtered electrophysiological signal ; Step 1.6: Perform channel whitening to obtain the whitened electrophysiological signal ; Step 1.7: Perform drift correction to obtain the corrected electrophysiological signal .

3. The method for sorting neural electrophysiological spike signals based on small batch K-means clustering according to claim 2, characterized in that: Step 2 includes the following steps: Step 2.1: Setup is the number of cluster centers, and the cluster centers are initialized using the K-Means++ algorithm , ,in, Indicates cluster centers, and ,in, Indicates The cluster center is in The value on the dimensional channel; Step 2.2: From Randomly select a dimension of size A subset of electrophysiological signals , ,in, Indicates The electrophysiological signal after correction at each time point, and , , Indicates The time point The corrected electrophysiological signal on each channel is represents the total duration of the electrophysiological signal subset Z, , represents the total number of channels of the electrophysiological signal subset Z, ; Step 2.3: Calculation The distance from each cluster center and Assign to the cluster center corresponding to the minimum distance, so that Each electrophysiological signal in is assigned to the corresponding cluster center; Step 2.4: Update the kth cluster center using formula (1) , get the updated kth cluster center : (1) In formula (1), yes The corresponding electrophysiological signal clustering, yes The number of electrophysiological signals in represents the adaptive learning rate; Step 2.5: If and The difference between them meets the threshold , it means that the cluster center does not change, and the updated cluster center is output This is the general template for electrophysiological signal spike detection, where , Indicates the updated Cluster Centers In the The value on the dimension channel; if the threshold is not met , then Assignment Then, return to step 2.2 and execute sequentially.

4. The method for sorting neural electrophysiological spike signals based on small batch K-means clustering according to claim 3, characterized in that: Step 4 includes the following steps: Step 4.1: From Randomly select a dimension of size A subset of electrophysiological spike features , ,in, Indicates electrophysiological spike features, and , , Indicates The electrophysiological spike features are The value on each channel, represents the number of spike features in the selected electrophysiological spike subset, , The number of channels representing the subset of electrophysiological spikes, ; Step 4.2: The corresponding cluster set is recorded as ,in, for Clustering of spike signals; Step 4.3: Calculate clusters based on correlation coefficient method The similarity between any two electrophysiological spike signals in is calculated. If the similarity is greater than the set threshold, the corresponding two electrophysiological spike signals are connected by edges; otherwise, the edges are not connected. Step 4.4: Use equation (2) to construct a modular cost function : (2) In formula (2), It is clustering The total number of edges connecting all pairs of spike signals in , It is clustering The total number of edges connecting pairs of inner spikes, It is clustering The sum of the number of edges corresponding to each electrophysiological spike signal in is a parameter that controls the number of clusters; Step 4.5: Calculate electrophysiological spike characteristics based on the correlation coefficient method Clustering with electrophysiological spike signals The number of edges connected to the remaining spike signals in and Electrophysiological spike features Clustering of spike signals The number of edges connected to the remaining spike signals in ; like Make , then the electrophysiological spike signal Join Otherwise, it remains unchanged, thus obtaining the updated electrophysiological signal clustering set ,in, Indicates Updated electrophysiological signal clustering, Indicates An updated electrophysiological signal clustering; Step 4.6: Calculate according to formula (3) and Modularity cost function value before merging : (3) In formula (3), It is clustering The total number of edges connecting pairs of inner spikes, express The total number of edges connecting pairs of inner spikes, yes The sum of the number of edges corresponding to each electrophysiological spike signal in yes The sum of the number of edges corresponding to each electrophysiological spike signal in Represents a fixed constant; Step 4.7: Calculate according to formula (4) and The modular cost function value after the merger : (4) In formula (4), It is clustering and the total number of edges connecting pairs of inner spikes; Step 4.8: Calculate the difference before and after merging according to formula (5) : (5) Step 4.9: When When and Merge into a new cluster; Step 4.10: Follow the process from Steps 4.1 to 4.9 to The clusters containing the electrophysiological spike features in the merging are merged to obtain the new clusters recorded as ,in, is the new cluster after the mth merger, The corresponding cluster center is recorded as , thus The corresponding cluster center and serve as a learning template; , ; M represents the number of new clusters after merging.

5. An electronic device, comprising a memory and a processor, characterized in that: The memory is used to store a program that supports the processor to execute the neural electrophysiological spike signal sorting method according to any one of claims 1 to 4, and the processor is configured to execute the program stored in the memory.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the neural electrophysiological spike signal sorting method according to any one of claims 1 to 4 are executed.

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