Human intelligence-based electroencephalogram signal processing method and device, edge computing equipment and medium

Abstract

The invention discloses an electroencephalogram signal processing method and device based on human intelligence, edge computing equipment and a medium, and belongs to the technical field of signal processing. The method comprises the steps of collecting electroencephalogram signals of a target object; decomposing the electroencephalogram signal into a plurality of statistically independent signal components through blind source separation; inputting the plurality of signal components into a preset neural network model to obtain the category of each signal component output by the neural network model; and combining the signal components of the effective electroencephalogram signals to obtain the electroencephalogram signals after noise reduction. According to the embodiment of the invention, blind source separation and machine learning algorithms are fully utilized, and the accuracy and efficiency of electroencephalogram signal processing are greatly improved.

Description

Technical Field

[0001] The present application belongs to the field of signal processing technology, and in particular, relates to a method, device, edge computing device and medium for processing electroencephalogram (EEG) signals based on human intelligence. Background Art

[0002] Electroencephalography (EEG) is an important tool for studying brain activity and cognitive processes. However, EEG signals are often interfered by physiological processes such as eye movements, electrocardiogram, and electromyography, which affects the accuracy and reliability of EEG signals.

[0003] Among the related technologies, the technology for denoising EEG signals mainly relies on offline processing. Specifically, after the EEG signal collection is completed, researchers need to carefully check and analyze the collected EEG signals to identify interference components caused by physiological factors such as eye movements, electrocardiograms, and electromyography. Although this offline processing method can improve the signal-to-noise ratio of EEG signals, it often requires a lot of manual intervention and is inefficient. Summary of the invention

[0004] The present application aims to solve at least one of the technical problems existing in the prior art. To this end, the present application proposes a method, device, edge computing device and medium for processing EEG signals based on human intelligence to reduce the noise of EEG signals under low latency conditions and improve the efficiency of EEG signal processing.

[0005] In a first aspect, the present application provides a method for processing EEG signals based on human intelligence, comprising:

[0006] Collecting EEG signals of the target object;

[0007] Decomposing the EEG signal into multiple statistically independent signal components by blind source separation;

[0008] Inputting a plurality of the signal components into a preset neural network model to obtain the category of each signal component output by the neural network model;

[0009] The signal components classified as valid EEG signals are combined to obtain a denoised EEG signal.

[0010] According to the EEG signal processing method based on human factor intelligence of the present application, the EEG signal of the target object is collected; the EEG signal is decomposed into multiple statistically independent signal components by blind source separation; the multiple signal components are input into a preset neural network model to obtain the categories of each signal component output by the neural network model; the signal components classified as valid EEG signals are combined to obtain the EEG signal after noise reduction. The embodiment of the present application uses blind source separation to decompose the collected EEG signal into multiple statistically independent signal components, realizes the separation of noise signals from statistical inference, inputs the decomposed signal components into a preset neural network model, uses a deep learning algorithm to classify the signal, distinguishes between valid EEG signals and noise signals, and makes full use of blind source separation and machine learning algorithms, thereby completing the automated processing of EEG signals, and can efficiently reduce the noise of EEG signals under low latency, greatly improving the accuracy and efficiency of EEG signal processing.

[0011] According to one embodiment of the present application, before decomposing the EEG signal into a plurality of statistically independent signal components by blind source separation, the method includes:

[0012] The collected EEG signal is segmented to obtain multiple EEG signals with a preset length.

[0013] In this embodiment, for application scenarios such as brain-computer interfaces that require real-time processing, too long signal segments will increase the overall processing delay, while the use of short segment analysis with a preset duration can significantly reduce the delay in EEG signal processing and provide support for real-time feedback and control. In addition, short-duration signal segment processing can also improve the parallelization capability of the algorithm, thereby further improving the overall EEG signal processing efficiency.

[0014] According to one embodiment of the present application, the blind source separation includes independent component analysis.

[0015] According to one embodiment of the present application, decomposing the EEG signal into multiple statistically independent signal components by blind source separation includes:

[0016] Preprocessing the EEG signal, wherein the preprocessing includes at least one of denoising, filtering, centering, and whitening;

[0017] Independent component analysis is performed on the preprocessed EEG signals to obtain multiple statistically independent signal components.

[0018] In this embodiment, the EEG signal is preprocessed by blind source separation, including denoising, filtering, centering, whitening and other steps, which can improve the signal-to-noise ratio of the EEG signal. The preprocessed EEG signal is subjected to independent component analysis to obtain multiple statistically independent signal components, which can effectively separate the mixed EEG signal into neural activity signals from different sources. These signals include brain activity signals and various noise sources. Further analysis of these signals can identify effective EEG signals and noise, thereby improving the EEG signal noise reduction effect.

[0019] According to one embodiment of the present application, decomposing the EEG signal into multiple statistically independent signal components by blind source separation includes:

[0020] Determining the number of channels of the EEG signal;

[0021] The number of the statistically independent signal components is determined according to the number of channels; wherein the number of the signal components is less than or equal to the number of channels.

[0022] In this embodiment, by determining the number of channels of the EEG signal, a reference can be provided for the blind source separation algorithm, thereby reducing the overfitting problem and enabling the algorithm to restore independent source signals as accurately as possible under limited information.

[0023] According to one embodiment of the present application, the method further includes:

[0024] Obtaining EEG signals of different subjects as training data;

[0025] Decomposing the training data into a plurality of statistically independent data components by independent component analysis;

[0026] Labeling the categories of each of the data components to obtain a training set;

[0027] The training set is used to train the neural network model to obtain a trained neural network model.

[0028] In this embodiment, by collecting EEG signals of different objects as training data, the diversity of model training is increased, so that the model can be generalized to the EEG signals of different individuals. The independent component analysis algorithm is used to decompose the training data into multiple statistically independent components, and the training set is constructed by labeling the categories of these components. The decomposition ability of the independent component analysis algorithm is utilized to extract useful EEG activities and various noise sources from the mixed EEG signals. The neural network model is trained with these labeled data, so that the model can learn how to identify and distinguish different EEG signal components, thereby improving the accuracy and efficiency of EEG signal processing.

[0029] According to an embodiment of the present application, the inputting of the plurality of signal components into a preset neural network model to obtain the category of each signal component output by the neural network model includes:

[0030] In the case where the neural network model includes a binary classifier, the categories of each signal component output by the neural network model include signal components that are valid EEG signals and signal components that are invalid EEG signals; and / or,

[0031] In the case where the neural network model includes multiple classifiers, the categories of each signal component output by the neural network model include signal components being at least one of valid electroencephalogram signals, electrocardiogram signals, electrooculogram signals, electromyography signals, and unknown signals.

[0032] In this embodiment, the learning ability of the neural network model is used to achieve automatic classification of signal components. In the case of a binary classifier, the model can distinguish between valid EEG signals and invalid EEG signals, and in the case of a multi-classifier, the model further refines the classification and identifies multiple signals such as valid EEG signals, ECG signals, EOG signals, EMG signals, and unknown signals, greatly improving the accuracy and efficiency of EEG signal processing.

[0033] In a second aspect, the present application provides an electroencephalogram signal processing device based on human intelligence, comprising:

[0034] An acquisition module, used for acquiring EEG signals of a target object;

[0035] A decomposition module, used for decomposing the EEG signal into a plurality of statistically independent signal components by blind source separation;

[0036] A classification module, used for inputting the plurality of signal components into a preset neural network model to obtain the category of each signal component output by the neural network model;

[0037] The combination module is used to combine the signal components classified as valid EEG signals to obtain the EEG signals after noise reduction.

[0038] According to the EEG signal processing device based on human factor intelligence of the present application, the EEG signal of the target object is collected; the EEG signal is decomposed into multiple statistically independent signal components by blind source separation; the multiple signal components are input into a preset neural network model to obtain the category of each signal component output by the neural network model; the signal components classified as valid EEG signals are combined to obtain the EEG signal after noise reduction. The embodiment of the present application uses blind source separation to decompose the collected EEG signal into multiple statistically independent signal components, realizes the separation of noise signals from statistical inference, inputs the decomposed signal components into a preset neural network model, uses a deep learning algorithm to classify the signal, distinguishes between valid EEG signals and noise signals, and makes full use of blind source separation and machine learning algorithms, thereby completing the automated processing of EEG signals, and can efficiently reduce the noise of EEG signals under low latency, greatly improving the accuracy and efficiency of EEG signal processing.

[0039] In a third aspect, the present application provides an edge computing device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for processing EEG signals based on human intelligence as described in the first aspect above is implemented.

[0040] In a fourth aspect, the present application provides a computer-readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the method for processing EEG signals based on human intelligence as described in the first aspect above is implemented.

[0041] In a fifth aspect, the present application provides a chip, comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the EEG signal processing method based on human intelligence as described in the first aspect above.

[0042] In a sixth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the EEG signal processing method based on human intelligence as described in the first aspect above.

[0043] The above one or more technical solutions in the embodiments of the present application have at least one of the following technical effects:

[0044] According to the EEG signal processing method based on human factor intelligence of the present application, the EEG signal of the target object is collected; the EEG signal is decomposed into multiple statistically independent signal components by blind source separation; the multiple signal components are input into a preset neural network model to obtain the categories of each signal component output by the neural network model; the signal components classified as valid EEG signals are combined to obtain the EEG signal after noise reduction. The embodiment of the present application uses blind source separation to decompose the collected EEG signal into multiple statistically independent signal components, realizes the separation of noise signals from statistical inference, inputs the decomposed signal components into a preset neural network model, uses a deep learning algorithm to classify the signal, distinguishes between valid EEG signals and noise signals, and makes full use of blind source separation and machine learning algorithms, thereby completing the automated processing of EEG signals, and can efficiently reduce the noise of EEG signals under low latency, greatly improving the accuracy and efficiency of EEG signal processing.

[0045] Furthermore, in some embodiments, for application scenarios such as brain-computer interfaces that require real-time processing, overly long signal segments will increase the overall processing delay, and the use of short segment analysis with a preset duration can significantly reduce the delay in EEG signal processing and provide support for real-time feedback and control. In addition, short-duration signal segment processing can also improve the parallelization capability of the algorithm, thereby further improving the overall EEG signal processing efficiency.

[0046] Furthermore, in some embodiments, the EEG signals are preprocessed through blind source separation, including denoising, filtering, centering, whitening and other steps, which can improve the signal-to-noise ratio of the EEG signals. Independent component analysis is performed on the preprocessed EEG signals to obtain multiple statistically independent signal components, which can effectively separate the mixed EEG signals into neural activity signals from different sources. These signals include brain activity signals and various noise sources. Further analysis of these signals can identify effective EEG signals and noise, thereby improving the EEG signal noise reduction effect.

[0047] Furthermore, in some embodiments, by determining the number of channels of the EEG signal, a reference can be provided for the blind source separation algorithm, thereby reducing the overfitting problem and enabling the algorithm to restore independent source signals as accurately as possible with limited information.

[0048] Furthermore, in some embodiments, by collecting EEG signals of different objects as training data, the diversity of model training is increased, so that the model can be generalized to the EEG signals of different individuals. The independent component analysis algorithm is used to decompose the training data into multiple statistically independent components, and the training set is constructed by labeling the categories of these components. The decomposition ability of the independent component analysis algorithm is utilized to extract useful EEG activities and various noise sources from the mixed EEG signals. The labeled data are used to train the neural network model, so that the model can learn how to identify and distinguish different EEG signal components, thereby improving the accuracy and efficiency of EEG signal processing.

[0049] Furthermore, in some embodiments, the learning ability of the neural network model realizes the automatic classification of signal components. In the case of a binary classifier, the model can distinguish between valid EEG signals and invalid EEG signals, and in the case of a multi-classifier, the model further refines the classification and identifies multiple signals such as valid EEG signals, ECG signals, EOG signals, EMG signals, and unknown signals, greatly improving the accuracy and efficiency of EEG signal processing.

[0050] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0052] Figure 1 It is a flowchart of a method for processing EEG signals based on human intelligence provided in an embodiment of the present application;

[0053] Figure 2 is a schematic diagram of signal components provided by an embodiment of the present application;

[0054] Figure 3 It is a schematic diagram of the training process of the neural network model provided in the embodiment of the present application;

[0055] Figure 4 It is a schematic diagram of the testing process of the neural network model provided in the embodiment of the present application;

[0056] Figure 5 is a schematic diagram of a scenario example provided in an embodiment of the present application;

[0057] Figure 6It is a structural schematic diagram of a brain electrical signal processing device based on human intelligence provided in an embodiment of the present application;

[0058] Figure 7 It is a structural diagram of the edge computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application belong to the scope of protection of this application.

[0060] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0061] EEG signals refer to electrophysiological signals recorded from the cerebral cortex, which reflect the electrical activity of neurons in the cerebral cortex. When a large number of cortical neurons emit potentials at the same time, detectable electric field signals are generated. By attaching electrodes to the scalp, these weak electric field signals, namely EEG signals, can be recorded. EEG signals provide real-time data on the functional state of the brain. By analyzing EEG signals, we can understand information such as human cognitive processes and emotional states, providing valuable data for a deep understanding of brain functions. In addition, EEG signals can also be used to realize non-invasive brain-computer interfaces, allowing humans to directly control external devices through electrical signals from the brain, which has broad application prospects in medical rehabilitation, entertainment interaction, neuroscience research and other fields.

[0062] In the process of collecting EEG signals, the signals are often interfered by various noises. These noise sources include electrical signals generated by eye movements, electrocardiographic interference caused by heartbeats, myoelectric noise generated by muscle activity, and environmental noise such as power line interference. The presence of these noises not only reduces the quality of EEG signals, but may also mask or distort the real signals of brain activity, bringing challenges to data analysis and interpretation.

[0063] In order to improve the accuracy and availability of EEG signals, related technologies mainly use offline methods for denoising. In this process, researchers need to conduct detailed inspection and analysis of the original EEG signals to identify and mark interference signals caused by physiological factors such as eye movements, electrocardiograms, and electromyography. Manual inspection is a time-consuming process that requires a high degree of expertise. Researchers must carefully distinguish which signals are generated by brain activity and which are interference caused by noise. Although this offline processing method can improve the signal-to-noise ratio of EEG signals, it often requires a lot of manpower and is inefficient. Therefore, how to effectively remove noise from EEG signals, especially without sacrificing real-time performance, is an important challenge facing the current field of EEG signal processing.

[0064] It should be noted that in the optional embodiments of the present application, the object information and other related data involved, when the embodiments in the present application are applied to specific products or technologies, need to obtain the permission or consent of the object, and the collection, use and processing of the relevant data need to comply with the relevant laws, regulations and standards of the relevant countries and regions. In other words, if the embodiments of the present application involve data related to the object, it needs to be obtained with the authorization and consent of the object, the authorization and consent of the relevant departments, and in compliance with the relevant laws, regulations and standards of the country and region. If personal information is involved in the embodiments, the acquisition of all personal information needs to obtain the consent of the individual. If sensitive information is involved, the separate consent of the information subject needs to be obtained. The embodiments also need to be implemented with the authorization and consent of the object.

[0065] In the following, in combination with the accompanying drawings, the EEG signal processing method, device, edge computing device and medium based on human intelligence provided by the embodiments of the present application are described in detail through specific embodiments and their application scenarios.

[0066] Among them, the EEG signal processing method based on human intelligence can be applied to the terminal, and can be specifically executed by hardware or software in the terminal.

[0067] The terminal includes, but is not limited to, a portable communication device such as a mobile phone or tablet computer with a touch-sensitive surface (e.g., a touch screen display and / or a touch pad). It should also be understood that in some embodiments, the terminal may not be a portable communication device, but a desktop computer with a touch-sensitive surface (e.g., a touch screen display and / or a touch pad).

[0068] In the following various embodiments, a terminal including a display and a touch-sensitive surface is described. However, it should be understood that the terminal may include one or more other physical user interface devices such as a physical keyboard, a mouse and a joystick.

[0069] The embodiment of the present application provides an EEG signal processing method based on human intelligence. The execution subject of the EEG signal processing method based on human intelligence can be an edge computing device or a functional module or functional entity in the edge computing device that can implement the EEG signal processing method based on human intelligence. The edge computing device mentioned in the embodiment of the present application can be a portable device (such as a mobile phone, a tablet computer), a wearable device (such as a smart watch, a smart bracelet), an eye tracker, AR (Augmented Reality, augmented reality) / VR (Virtual Reality, virtual reality) / XR (Extended Reality, extended reality) / MR (Mixed Reality, mixed reality) device, an edge server, an edge gateway, a vehicle-mounted device, a cloud computing device, etc. The following takes the edge computing device as an example of the execution subject to illustrate the EEG signal processing method based on human intelligence provided in the embodiment of the present application.

[0070] like Figure 1 As shown, the EEG signal processing method based on human intelligence includes: step 110, step 120, step 130 and step 140.

[0071] Step 110: Collect the EEG signal of the target object.

[0072] In the embodiments of the present application, the target object may be different objects according to different application scenarios. For example, in a simulated driving scenario, the target object may be a driver, in a medical and health scenario, the target object may be a patient, and in a test scenario, the target object may be a person being tested.

[0073] In one example, in a simulated driving scenario, the driver's behavior can be effectively evaluated and monitored by analyzing the driver's EEG signals and eye movement signals. By using an eye tracker and EEG acquisition equipment, these two signals can be collected at the same time. Eye movement signals provide intuitive visual attention information, while EEG signals reveal the brain's deep cognitive processing. This data fusion enhances the richness of information and improves the accuracy of driver behavior analysis. Eye movement signals can be collected by an eye tracker to reflect the driver's visual attention, scanning patterns and other characteristics, so as to judge the driver's observation and reaction to road conditions. Eye movement signals help understand the driver's concentration, reaction speed and other key driving behaviors.

[0074] The collection of EEG signals can monitor the driver's fatigue, distraction or emotional state in real time and predict potential driving risks. However, the driver's EEG signals contain a large number of interference signals from physiological processes such as eye movement, electrocardiogram, and electromyography. Therefore, it is necessary to professionally filter and process the EEG signals to extract effective EEG signals that reflect the cognitive state. After analyzing and processing the EEG signals, the driver's attention allocation, emotional changes, decision-making process and other information can be identified, thereby evaluating his driving status.

[0075] In the embodiments of the present application, EEG signals are electrical signals generated by brain neuron activity. EEG signals reflect the superposition of postsynaptic potentials generated by a large number of neurons and are a mapping of brain activity. Neurons in the brain are connected to each other through synapses to form a complex neural network. When neurons are activated, bioelectric phenomena are generated. These electrical signals can be captured by placing electrodes on the scalp or implanted directly into the brain.

[0076] In some embodiments, an EEG acquisition device may be used to acquire EEG signals of the target object. Specifically, electrodes may be placed on the scalp of the target object to acquire EEG signals of the target object. The EEG acquisition device may be a head-mounted device, and after the target object correctly wears the head-mounted device, the EEG signals of the target object are acquired through the electrodes in the head-mounted device.

[0077] Step 120: Decompose the EEG signal into multiple statistically independent signal components by blind source separation.

[0078] Blind Source Separation (BSS) is a signal processing method that aims to recover the original independent signal sources from a mixed signal without any prior knowledge about the source signals or the mixing process. BSS is based on the assumption that the observed mixed signal is a linear combination of multiple independent source signals. These source signals are statistically independent, and nothing is known about the source signals except their independence. The goal of BSS is to find an unmixing matrix so that the signals transformed by this matrix are as independent as possible.

[0079] In the embodiment of the present application, BSS can be used to separate the signal generated by brain activity and the noise signal caused by other physiological or external factors. Through BSS, the mixed EEG signal can be decomposed into multiple statistically independent signal components, some of which may be signals generated by brain activity, that is, signal components classified as valid EEG signals, while other signal components may be noise signals or artifacts caused by processes such as eye movement, electrocardiogram, and electromyography, that is, invalid signal components.

[0080] In some embodiments, the BSS algorithm used may include independent component analysis (ICA), a method based on empirical mode decomposition (EMD), etc. Among them, ICA assumes that the source signals are statistically independent and at least some sources are non-Gaussian distributed. ICA finds independent components of the signal source by maximizing non-Gaussianity or minimizing mutual information.

[0081] In an embodiment of the present application, the EEG signal can be input into the BSS algorithm, and the BSS algorithm can find a set of unmixing matrices so that the signal components obtained after the EEG signal is transformed by the unmixing matrix are as independent as possible, that is, multiple statistically independent signal components are obtained. Among them, statistical independence means that there is no correlation or almost no correlation between two or more random variables, that is, the occurrence of one variable will not affect the probability distribution of another variable. In statistics, if the joint probability distribution of two random variables X and Y is equal to the product of their respective probability distributions, then X and Y are said to be statistically independent.

[0082] Step 130: Input multiple signal components into a preset neural network model to obtain the category of each signal component output by the neural network model.

[0083] In the embodiment of the present application, the categories of signal components may include valid EEG signals and invalid EEG signals. Valid EEG signals include signals generated by brain activity, and invalid EEG signals include noise signals caused by eye movement, electrocardiogram, electromyography, and other processes.

[0084] A neural network model can be pre-trained so that the neural network model has learned how to identify and distinguish different EEG signal components, so that the categories of each signal component can be output. For example, a deep learning model such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) can be selected to design the network structure of the neural network model, and a large number of labeled EEG signals can be used as training samples for end-to-end learning. Through multiple iterative learning, the neural network model can better extract features from signal components and perform accurate classification.

[0085] In an embodiment of the present application, after the training of the neural network model is completed, the application can be deployed to input the multiple signal components obtained from blind source separation into the neural network model for inference. Each signal component will obtain a category label in the output layer of the neural network model, and the category label is the classification result of the neural network model for the signal component.

[0086] Step 140: Combine the signal components classified as valid EEG signals to obtain a denoised EEG signal.

[0087] In the embodiment of the present application, the signal components classified as valid EEG signals can be combined, and other signal components can be removed to obtain a noise-reduced EEG signal. For example, the signal components classified as valid EEG signals can be linearly superimposed to reconstruct the EEG signal.

[0088] According to the EEG signal processing method based on human factor intelligence of the present application, the EEG signal of the target object is collected; the EEG signal is decomposed into multiple statistically independent signal components by blind source separation; the multiple signal components are input into a preset neural network model to obtain the categories of each signal component output by the neural network model; the signal components classified as valid EEG signals are combined to obtain the EEG signal after noise reduction. The embodiment of the present application uses blind source separation to decompose the collected EEG signal into multiple statistically independent signal components, realizes the separation of noise signals from statistical inference, inputs the decomposed signal components into a preset neural network model, and uses a deep learning algorithm to classify the signal to distinguish between valid EEG signals and noise signals, making full use of blind source separation and machine learning algorithms, thereby completing the automated processing of EEG signals, and being able to efficiently reduce the noise of EEG signals under low latency, greatly improving the accuracy and efficiency of EEG signal processing.

[0089] In some embodiments, before decomposing the EEG signal into a plurality of statistically independent signal components by blind source separation, the method comprises:

[0090] The collected EEG signal is segmented to obtain multiple EEG signals with a preset length.

[0091] In this embodiment, an EEG acquisition device can be used to collect the EEG signal of the target object. Since the continuous EEG signal is long, in order to realize the online processing of the EEG signal and reduce the computational complexity, the continuous EEG signal can be divided into multiple shorter time periods, and the length of each EEG signal is a preset duration. Among them, the preset duration can be predetermined, for example, it can be 5s, 10s, 15s, etc., and the embodiment of the present application does not limit the preset duration.

[0092] In this embodiment, for application scenarios such as brain-computer interfaces that require real-time processing, too long signal segments will increase the overall processing delay, while the use of short segment analysis with a preset duration can significantly reduce the delay in EEG signal processing and provide support for real-time feedback and control. In addition, short-duration signal segment processing can also improve the parallelization capability of the algorithm, thereby further improving the overall EEG signal processing efficiency.

[0093] In some embodiments, the EEG signal is decomposed into a plurality of statistically independent signal components by blind source separation, including:

[0094] Preprocessing the EEG signal, wherein the preprocessing includes at least one of denoising, filtering, centering, and whitening;

[0095] Independent component analysis is performed on the preprocessed EEG signals to obtain multiple statistically independent signal components.

[0096] In this embodiment, before decomposing the EEG signal, the EEG signal can be preprocessed, including denoising, filtering, centering, whitening and other operations. Among them, denoising can reduce the noise in the EEG signal, such as electromyographic interference, power line interference, etc., to improve the signal quality; the bandpass filter can reduce the unnecessary frequency signal in the EEG signal, such as removing low-frequency drift and high-frequency noise; centering is to adjust the mean of the observed data to zero; whitening is to convert the EEG signal into the form of a unit matrix with a covariance matrix.

[0097] In this embodiment, the preprocessed EEG signal may be decomposed using an independent component analysis algorithm, such as FastICA, Infomax, etc., to obtain a plurality of statistically independent signal components.

[0098] In this embodiment, the EEG signal is preprocessed by blind source separation, including denoising, filtering, centering, whitening and other steps, which can improve the signal-to-noise ratio of the EEG signal. The preprocessed EEG signal is subjected to independent component analysis to obtain multiple statistically independent signal components, which can effectively separate the mixed EEG signal into neural activity signals from different sources. These signals include brain activity signals and various noise sources. Further analysis of these signals can identify effective EEG signals and noise, thereby improving the EEG signal noise reduction effect.

[0099] In some embodiments, the EEG signal is decomposed into a plurality of statistically independent signal components by blind source separation, including:

[0100] Determine the number of channels of EEG signals;

[0101] The number of the statistically independent signal components is determined according to the number of channels; wherein the number of the signal components is less than or equal to the number of channels.

[0102] In this embodiment, the EEG signal is a multi-channel signal obtained by placing electrodes on the scalp or in the skull, and the number of channels depends on the number and position of the electrodes used. Multiple electrodes, such as 32 electrodes, 64 electrodes, 128 electrodes, etc., are used to collect EEG signals. Each electrode records an EEG signal of one channel. The number of channels is the number of these electrodes, and the number of channels determines the dimension of the EEG signal that can be recorded.

[0103] In blind source separation, the number of signal components obtained by decomposing the EEG signal is less than the number of channels of the EEG signal. Figure 2 As shown, for an EEG signal with 32 channels, at most 32 signal components can be obtained after the EEG signal is processed by ICA.

[0104] In this embodiment, by determining the number of channels of the EEG signal, a reference can be provided for the blind source separation algorithm, thereby reducing the overfitting problem and enabling the algorithm to restore independent source signals as accurately as possible under limited information.

[0105] In some embodiments, the method further comprises:

[0106] Obtaining EEG signals of different subjects as training data;

[0107] Decompose the training data into multiple statistically independent data components through independent component analysis;

[0108] Label the categories of each data component to obtain a training set;

[0109] The training set is used to train the neural network model to obtain a trained neural network model.

[0110] In this embodiment, the training process of the neural network model is as follows: Figure 3 As shown. EEG signals from multiple different subjects can be collected as training data for the neural network model. When collecting these training data, it is necessary to cover as wide a range of EEG signals as possible to improve the diversity and representativeness of EEG signals so that the neural network model can learn the characteristics of EEG signals in different situations.

[0111] In this embodiment, the continuous EEG signal can be divided into multiple shorter time periods, and the length of each EEG signal is a preset duration. The preset duration can be predetermined, for example, 5s, 10s, 15s, etc., and the embodiment of the present application does not limit the preset duration.

[0112] The segmented EEG signals can be decomposed using the ICA algorithm to obtain multiple statistically independent signal components, which are then labeled with categories. For example, the category information labeled by developers for these signal components can be obtained. After the labeling is completed, these signal components and category labels constitute a training set.

[0113] In this embodiment, a suitable neural network architecture, such as CNN, can be selected, and the structure of the neural network can be designed according to the task requirements, including selecting the appropriate number of layers, the number of neurons in each layer, and the activation function, etc. The training set is input into the neural network model for training, and the difference between the model prediction and the actual label is measured by the loss function. In each iteration, the input data is forward propagated through the neural network, the loss is calculated, and then the gradient is calculated and the weight of the model is updated by the back propagation algorithm. During the training process, the neural network model will automatically learn the rules of feature extraction and classification, and finally obtain a trained model, that is, the ICA component judgment classifier.

[0114] In this embodiment, before the neural network model is deployed and applied, the neural network model needs to be tested. The testing process is as follows: Figure 4 As shown. First, the EEG signal of any object should be obtained as test data. The continuous EEG signal can be divided into multiple shorter time periods, and the length of each EEG signal is a preset time. The segmented EEG signal can be decomposed by the ICA algorithm to obtain multiple statistically independent signal components, and these signal components are input into the ICA component judgment classifier. The ICA component judgment classifier can output the category label of each signal component. When the category label is determined to be a valid signal, the signal component can be used for the combination of EEG signals. When the category label is determined to be an invalid signal, the signal component can be removed to finally obtain the denoised EEG signal.

[0115] In this embodiment, by collecting EEG signals of different objects as training data, the diversity of model training is increased, so that the model can be generalized to the EEG signals of different individuals. The independent component analysis algorithm is used to decompose the training data into multiple statistically independent components, and the training set is constructed by labeling the categories of these components. The decomposition ability of the independent component analysis algorithm is utilized to extract useful EEG activities and various noise sources from the mixed EEG signals. The neural network model is trained with these labeled data, so that the model can learn how to identify and distinguish different EEG signal components, thereby improving the accuracy and efficiency of EEG signal processing.

[0116] In some embodiments, multiple signal components are input into a preset neural network model to obtain the categories of each signal component output by the neural network model, including:

[0117] In the case where the neural network model includes a binary classifier, the categories of each signal component output by the neural network model include signal components that are valid EEG signals and signal components that are invalid EEG signals; and / or,

[0118] When the neural network model includes multiple classifiers, the categories of each signal component output by the neural network model include signal components that are valid EEG signals, and signal components that are at least one of valid EEG signals, EKG signals, EOG signals, EMG signals, and unknown signals.

[0119] In this embodiment, according to the requirements of the classification task, the neural network model can adopt a simple binary classification neural network; for multi-classification, a deeper or more complex network structure can be adopted.

[0120] During the training process, when the neural network model includes a binary classifier, the category labels for the signal components can be valid EEG signals and invalid EEG signals, so that the outputs of the neural network model are also valid EEG signals and invalid EEG signals.

[0121] When the neural network model includes multiple classifiers, the category labeling of the signal component can be effective EEG signal, ECG signal, EO signal, EMG signal, unknown signal, etc., so that the neural network model can learn the mapping relationship between the characteristics of the signal component and the category, and output effective EEG signal, ECG signal, EO signal, EMG signal, unknown signal, etc. Figure 2 As shown, Figure 2 The signal components ICA003, ICA010, and ICA011 are eye movement signals.

[0122] In this embodiment, the learning ability of the neural network model is used to achieve automatic classification of signal components. In the case of a binary classifier, the model can distinguish between valid EEG signals and invalid EEG signals, and in the case of a multi-classifier, the model further refines the classification and identifies multiple signals such as valid EEG signals, ECG signals, EOG signals, EMG signals, and unknown signals, greatly improving the accuracy and efficiency of EEG signal processing.

[0123] The following is an example of a scenario to introduce the EEG signal processing method provided by the present application. Figure 5 As shown, in this scenario example, the ICA algorithm is used to decompose the EEG signal.

[0124] Specifically, the collected EEG signals can be first segmented, for example, 10 seconds per segment, to obtain EEG signal segments, and then the EEG signal segments are decomposed by ICA to obtain multiple statistically independent signal components, and these signal components are input into the neural network model to obtain classification results of each signal component. For example, the neural network model is a binary classifier, and the output of the neural network model can include category labels of signal components. For example, the category label "1" indicates that the category of the signal component is a valid EEG signal, and the category label "0" indicates that the category of the signal classification is an invalid EEG signal.

[0125] After obtaining the classification results of each signal component, the post-processing step is entered. Specifically, the post-processing step may include: removing the signal components classified as invalid EEG signals, combining the signal components classified as valid EEG signals, and obtaining a de-noised EEG signal.

[0126] The EEG signal processing method based on human intelligence provided in the embodiment of the present application can be executed by an EEG signal processing device based on human intelligence. In the embodiment of the present application, the EEG signal processing device based on human intelligence performs the EEG signal processing method based on human intelligence as an example to illustrate the EEG signal processing device based on human intelligence provided in the embodiment of the present application.

[0127] The embodiment of the present application also provides an electroencephalogram signal processing device based on human intelligence.

[0128] like Figure 6 As shown, the EEG signal processing device based on human intelligence includes:

[0129] The acquisition module 610 is used to acquire the EEG signal of the target object;

[0130] A decomposition module 620, for decomposing the EEG signal into a plurality of statistically independent signal components by blind source separation;

[0131] A classification module 630, used to input multiple signal components into a preset neural network model to obtain the category of each signal component output by the neural network model;

[0132] The combining module 640 is used to combine the signal components classified as valid EEG signals to obtain a noise-reduced EEG signal.

[0133] According to the EEG signal processing device based on human factor intelligence of the present application, the EEG signal of the target object is collected; the EEG signal is decomposed into multiple statistically independent signal components by blind source separation; the multiple signal components are input into a preset neural network model to obtain the categories of each signal component output by the neural network model; the signal components classified as valid EEG signals are combined to obtain the EEG signal after noise reduction. The embodiment of the present application uses blind source separation to decompose the collected EEG signal into multiple statistically independent signal components, realizes the separation of noise signals from statistical inference, inputs the decomposed signal components into a preset neural network model, uses a deep learning algorithm to classify the signal, distinguishes between valid EEG signals and noise signals, and makes full use of blind source separation and machine learning algorithms, thereby completing the automated processing of EEG signals, and can efficiently reduce the noise of EEG signals under low latency, greatly improving the accuracy and efficiency of EEG signal processing.

[0134] In some embodiments, the acquisition module 610 is further configured to:

[0135] The collected EEG signal is segmented to obtain multiple EEG signals with a preset length.

[0136] In some embodiments, the decomposition module 620 is further configured to:

[0137] Preprocessing the EEG signal, wherein the preprocessing includes at least one of denoising, filtering, centering, and whitening;

[0138] Independent component analysis is performed on the preprocessed EEG signals to obtain multiple statistically independent signal components.

[0139] In some embodiments, the decomposition module 620 is further configured to:

[0140] Determine the number of channels of EEG signals;

[0141] The number of the statistically independent signal components is determined according to the number of channels; wherein the number of the signal components is less than or equal to the number of channels.

[0142] In some embodiments, the classification module 630 is further configured to:

[0143] Obtaining EEG signals of different subjects as training data;

[0144] Decompose the training data into multiple statistically independent data components through independent component analysis;

[0145] Label the categories of each data component to obtain a training set;

[0146] The training set is used to train the neural network model to obtain a trained neural network model.

[0147] In some embodiments, the classification module 630 is further configured to:

[0148] In the case where the neural network model includes a binary classifier, the categories of each signal component output by the neural network model include signal components that are valid EEG signals and signal components that are invalid EEG signals;

[0149] In the case where the neural network model includes multiple classifiers, the categories of each signal component output by the neural network model include the signal component being at least one of a valid EEG signal, an EKG signal, an EOG signal, an EMG signal, and an unknown signal.

[0150] The EEG signal processing device based on human intelligence in the embodiment of the present application can be an edge computing device, or a component in an edge computing device, such as an integrated circuit or a chip. The edge computing device can be a terminal, or it can be other devices other than a terminal. Exemplarily, the edge computing device can be a mobile phone, a tablet computer, a laptop computer, an eye tracker, a PDA, a vehicle-mounted electronic device, a mobile Internet device (Mobile Internet Device, MID), an augmented reality (augmented reality, AR) / virtual reality (virtual reality, VR) device, a robot, a wearable device, an ultra-mobile personal computer (ultra-mobile personal computer, UMPC), a netbook or a personal digital assistant (personal digital assistant, PDA), etc., and can also be a server, a network attached storage (Network Attached Storage, NAS), a personal computer (personal computer, PC), a television (television, TV), a teller machine or a self-service machine, etc., which is not specifically limited in the embodiment of the present application. In an optional example, an edge deployment solution in which a human intelligence-based EEG signal processing device is deployed as an edge computing device or component on the end side can migrate at least part of the computing tasks to the end-side device, leveraging the advantages of edge computing to improve data processing efficiency, reduce latency and network resource occupancy and dependence, and also facilitate data security and privacy protection.

[0151] The EEG signal processing device based on human intelligence in the embodiment of the present application may be a device having an operating system. The operating system may be a Microsoft (Windows) operating system, an Android (Android) operating system, an IOS operating system, or other possible operating systems, which are not specifically limited in the embodiment of the present application.

[0152] In some embodiments, Figure 7 As shown, an embodiment of the present application also provides an edge computing device 700, including a processor 701, a memory 702, and a computer program stored in the memory 702 and executable on the processor 701. When the program is executed by the processor 701, each process of the above-mentioned embodiment of the EEG signal processing method based on human intelligence is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0153] It should be noted that the edge computing devices in the embodiments of the present application include the above-mentioned mobile edge computing devices and non-mobile edge computing devices.

[0154] The embodiment of the present application also provides a computer-readable storage medium, on which programs or instructions are stored. When the programs or instructions are executed by the processor, the various processes of the above-mentioned embodiment of the EEG signal processing method based on human intelligence are implemented, and the same technical effect can be achieved. To avoid repetition, they will not be repeated here.

[0155] The processor is the processor in the edge computing device in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk.

[0156] An embodiment of the present application also provides a computer program product, including a computer program, which implements the above-mentioned EEG signal processing method based on human intelligence when executed by a processor.

[0157] The processor is the processor in the edge computing device in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk.

[0158] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, which are coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned embodiment of the EEG signal processing method based on human intelligence, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0159] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0160] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0161] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0162] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

[0163] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0164] Although the embodiments of the present application have been shown and described, those skilled in the art will appreciate that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present application, and that the scope of the present application is defined by the claims and their equivalents.

Claims

1. A method for processing EEG signals based on human intelligence, characterized in that: include: Collecting EEG signals of the target object; Decomposing the EEG signal into multiple statistically independent signal components by blind source separation; Inputting a plurality of the signal components into a preset neural network model to obtain the category of each signal component output by the neural network model; The signal components classified as valid EEG signals are combined to obtain a denoised EEG signal.

2. The method according to claim 1, characterized in that Before decomposing the EEG signal into multiple statistically independent signal components by blind source separation, it includes: The collected EEG signal is segmented to obtain multiple EEG signals with a preset length.

3. The method according to claim 1, characterized in that The blind source separation includes independent component analysis.

4. The method according to claim 1, characterized in that The step of decomposing the EEG signal into a plurality of statistically independent signal components by blind source separation comprises: Preprocessing the EEG signal, wherein the preprocessing includes at least one of denoising, filtering, centering, and whitening; Independent component analysis is performed on the preprocessed EEG signals to obtain multiple statistically independent signal components.

5. The method according to claim 1, characterized in that The step of decomposing the EEG signal into a plurality of statistically independent signal components by blind source separation comprises: Determining the number of channels of the EEG signal; The number of the statistically independent signal components is determined according to the number of channels; wherein the number of the signal components is less than or equal to the number of channels.

6. The method according to claim 1, characterized in that The method further comprises: Obtaining EEG signals of different subjects as training data; Decomposing the training data into a plurality of statistically independent data components by independent component analysis; Labeling the categories of each of the data components to obtain a training set; The training set is used to train the neural network model to obtain a trained neural network model.

7. The method according to claim 1, characterized in that The step of inputting the plurality of signal components into a preset neural network model to obtain the category of each signal component output by the neural network model comprises: In the case where the neural network model includes a binary classifier, the categories of each signal component output by the neural network model include signal components that are valid EEG signals and signal components that are invalid EEG signals; and / or, In the case where the neural network model includes multiple classifiers, the categories of each signal component output by the neural network model include signal components being at least one of valid electroencephalogram signals, electrocardiogram signals, electrooculogram signals, electromyography signals, and unknown signals.

8. An electroencephalogram signal processing device based on human intelligence, characterized in that: include: An acquisition module, used for acquiring EEG signals of a target object; A decomposition module, used for decomposing the EEG signal into a plurality of statistically independent signal components by blind source separation; A classification module, used for inputting the plurality of signal components into a preset neural network model to obtain the category of each signal component output by the neural network model; The combination module is used to combine the signal components classified as valid EEG signals to obtain the EEG signals after noise reduction.

9. An edge computing device, characterized in that: It includes a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the programs or instructions are executed by the processor, the method for processing EEG signals based on human intelligence as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores programs or instructions, and when the programs or instructions are executed by the processor, the method for processing EEG signals based on human intelligence as described in any one of claims 1 to 7 is implemented.

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