Attention regulation and control closed-loop system based on electroencephalogram signals

Abstract

The invention discloses an attention regulation closed-loop system based on electroencephalogram signals, the closed-loop system is composed of a hardware part and a software part, the hardware part comprises a head-mounted device which is provided with a high-sensitivity EEG electrode and is used for collecting the electroencephalogram signals of the forehead lobe; the collected signals are transmitted to a computer or mobile equipment in real time through a wireless communication technology for further processing and analysis; the multi-guide programmable electrical stimulation device is used for dynamically adjusting electrical stimulation parameters according to algorithm feedback and providing a personalized stimulation scheme; according to the personalized attention improvement scheme, by analyzing an electroencephalogram (EEG) signal of an individual in real time, especially the ratio of a theta wave to a beta wave (TBR), attention changes of the individual in different states can be accurately captured; using these data, the system can customize a personalized stimulation scheme to adapt to the current needs of an individual, rather than employing a universal approach suitable for all people.

Description

Technical Field

[0001] The present invention belongs to the field of brain-computer interface and artificial intelligence technology, and specifically relates to an attention regulation closed-loop system based on electroencephalogram signals. Background Art

[0002] Attention regulation is an important research direction in cognitive science and neuroengineering, especially in the treatment of attention deficit and cognitive impairment. Traditional attention diagnosis and regulation methods, such as behavioral scales and clinical assessments, have problems such as high misdiagnosis rate, strong subjectivity and lack of quantitative indicators. Brain-computer interface (BCI) technology provides a new way to objectively evaluate and regulate attention status by collecting and analyzing electroencephalogram (EEG) signals. Compared with other methods, BCI devices are lighter, cheaper and easier to operate, so they have greater application potential in clinical settings.

[0003] Although the existing EEG attention regulation technology can monitor and regulate the attention state to a certain extent, it still faces many challenges in practical application:

[0004] Real-time and accuracy of signal processing: Existing methods still do not have high enough real-time and accuracy when processing EEG signals. Complex EEG signals are easily affected by environmental noise and motion artifacts, resulting in unstable processing results.

[0005] Limited personalized adjustment capabilities: The current EEG control system is not good enough in dealing with individual differences. Different users have different EEG signal characteristics, and fixed control schemes are difficult to meet the needs of each user, affecting the control effect.

[0006] There is a lot of room for optimization of the feedback mechanism: the existing feedback and stimulation methods are mostly fixed modes, which are difficult to dynamically adjust according to the user's real-time status. Stimulation methods with fixed parameters may lead to poor stimulation effects or adverse reactions. Summary of the invention

[0007] The purpose of the present invention is to provide an attention control closed-loop system based on EEG signals to solve the limitations of the traditional attention control methods proposed in the above background technology, such as low diagnostic accuracy, insufficient personalized adjustment and poor real-time feedback effect.

[0008] To achieve the above purpose, the present invention provides the following technical solution: a closed-loop system for attention regulation based on EEG signals, the closed-loop system consists of a hardware part and a software part, wherein

[0009] The hardware part includes:

[0010] The head-mounted device is equipped with highly sensitive EEG electrodes to collect EEG signals from the frontal lobe. The collected signals are transmitted to a computer or mobile device in real time via wireless communication technology for further processing and analysis.

[0011] A multi-channel programmable electrical stimulation device that dynamically adjusts the parameters of electrical stimulation based on algorithm feedback to provide a personalized stimulation program;

[0012] The software part includes:

[0013] The signal preprocessing module filters and denoises the collected EEG signals to remove environmental noise and artifacts to ensure signal purity;

[0014] Feature extraction module, extracts attention-related features from the preprocessed EEG signal;

[0015] The attention state classification module uses the residual domain generalization model to classify the extracted features and determine the user's attention state;

[0016] The feedback control module adjusts the parameters of the electrical stimulation device in real time according to the classification results to ensure the effectiveness and pertinence of the stimulation.

[0017] As a preferred technical solution in the present invention, the residual domain generalization model includes:

[0018] Residual network: By stacking multiple residual blocks to extract the deep features of the signal, the residual network can effectively capture the complex features in the EEG signal;

[0019] Domain generalization model: By learning shared features between different domains to improve the robustness of classification, the domain generalization model can adapt to the EEG signal characteristics of different users and provide personalized attention assessment;

[0020] Algorithm design: Accurately evaluate and regulate the user's attention state through EEG signals; through real-time attention state evaluation and feedback control.

[0021] As a preferred technical solution in the present invention, the head-mounted device uses a residual domain generalization model to extract multi-scale features related to attention from the preprocessed EEG signal, and performs accurate classification to determine the user's attention state, thereby realizing multi-level feature extraction and classification; the multi-channel programmable electrical stimulation device adjusts the parameters of the electrical stimulation device in real time according to the classification results, including waveform, frequency, current size and stimulation duration, so as to provide a personalized electrical stimulation plan, ensure the effectiveness and pertinence of the stimulation, and realize real-time feedback and dynamic regulation.

[0022] As a preferred technical solution in the present invention, the head mounted device is also provided with a wireless communication module connected to the multi-channel programmable electrical stimulation device and the host end, and the wireless communication module integrates the functions of data collection and data transmission.

[0023] As a preferred technical solution of the present invention, the specific use steps of the closed-loop system are as follows:

[0024] Signal acquisition:

[0025] By collecting 1000Hz EEG signals, the real-time EEG signals are sent to the host device via WiFi communication to obtain the original EEG data; each real-time judgment will take 2s as a basis for judgment;

[0026] Data preprocessing:

[0027] The data preprocessing process requires the use of bandpass filters and bandstop filters; the bandstop will filter out the 50Hz power frequency interference signal; at the same time, because the attention EEG signal is related to theta waves and beta waves, the bandpass filter is used to filter and retain the theta wave and alpha signal;

[0028] Feature extraction:

[0029] After data preprocessing, the frequency domain features will be calculated using the Fourier transform algorithm as follows, and then converted into power spectrum features; the power spectrum features of EEG will be used for algorithmic features of EEG attention for training and detection;

[0030]

[0031] Where N is the number of sample points, x[n] is the discrete time signal, X[k] is the discrete frequency domain signal, (where If it is a negative function, it can be omitted; if Σ is a sum function, it can also be omitted); the power spectrum P(f) represents the change of the power of the frequency domain signal with the frequency. The power spectrum can be obtained by calculating the square of the amplitude spectrum. The following formula x(f) represents the amplitude of the corresponding frequency, and its square value is the power spectrum, that is, P(f);

[0032] p(f)=|x(f)| 2

[0033] As above, the frequency domain signal of the EEG is processed to convert it into a power spectrum signal, which is used as the EEG state characteristic signal for algorithm processing;

[0034] Attention status assessment:

[0035] State evaluation is divided into two parts. The first is the model pre-training process; the second is the data evaluation process; the model pre-training process requires the collection of attention EEG data, and an attention state evaluation model is obtained through the algorithm model to classify the attention state and the non-attention state; the residual domain generalization model, the model formula is as follows:

[0036] y=f(x)+x

[0037] Feature y is the sum of input feature x after feature transformation function f and original input feature xx;

[0038]

[0039] P(y|x) represents the probability that the output feature y belongs to category k when the input feature x is given; f k (x) is the feature transformation result for category k, and the denominator is for all possible categories j The sum of

[0040] Through different convolution kernels, the input EEG power spectrum density features are convolved to different degrees, and three layers of convolution are used. The final results are fused to obtain the classification features, that is, the EEG feature extraction model.

[0041] As a preferred technical solution in the present invention, when performing classification feature processing, due to the large individual differences, the model used is a domain generalization model to reduce the differences between individuals.

[0042] As a preferred technical solution in the present invention, the software part is an EEG feedback system built with Python as the core framework language; the system interface uses the pyqt5 library to build the software framework, and the interface development utilizes web technology, specifically, JavaScript and HTML5 are used to build the system's user interface and its interactive functions, and in order to achieve communication between Python and JavaScript, the system uses JSBridge technology; after the data is collected, the EEG signal is transmitted to the computer side in real time through the websocket communication protocol, and on the computer side, a real-time model is responsible for processing these signals and performing judgment and analysis, and the output results of the model are then fed back to the user interface in real time, and users can instantly see their EEG activity status and make corresponding adjustments based on the feedback provided by the system.

[0043] As a preferred technical solution of the present invention, in the closed-loop system, it also includes:

[0044] Attention Regulation

[0045] The design of the software and hardware system can dynamically adjust the parameters of electrical stimulation and judge attention in multiple modes. First, the evidence of attention shows that the ratio of theta waves to beta waves can reflect the degree of attention. The higher the degree of attention, the higher the ratio of theta to beta. In the stage of attention regulation, the EEG data of eyes-open relaxation is first collected to calculate the ratio of theta to beta as the baseline of attention.

[0046] System design optimization description:

[0047] Baseline data collection: Before the experiment, let the subjects relax with their eyes open, and collect electroencephalogram (EEG) data at this time; calculate the ratio of theta waves to beta waves (TBR) as the baseline level of attention;

[0048] Basic stimulation settings: select one EEG channel in the prefrontal region as the basic channel, and set 0.8 mA direct current stimulation as the initial stimulation mode;

[0049] Task and stimulation synchronization: let the subjects alternate between attention tasks and relaxation tasks; during the performance of the attention task, basal direct current stimulation is applied synchronously;

[0050] Real-time TBR monitoring: During stimulation, TBR is calculated in real time and compared with the baseline TBR in the relaxed state;

[0051] Adaptive current regulation: If the subject is in an attentive state and the TBR is higher than the relaxed state, the system will automatically adjust the stimulation current according to the change of TBR; if the TBR does not rise, the current will be gradually increased in steps of 0.05mA until it does not exceed 2mA; if the TBR exceeds the relaxed state, the current intensity will be gradually reduced;

[0052] Relaxation stimulation: During the relaxation period, the alternating current stimulation frequency is used to induce alpha waves to promote rest and relaxation;

[0053] Feedback loop: The system continuously monitors TBR and dynamically adjusts stimulation parameters based on its changes, forming a closed-loop feedback mechanism;

[0054] Attention Improvement Assessment

[0055] The closed-loop system builds a more logical attention improvement assessment process, combining neural electrical stimulation and daily attention tasks, as well as systematic assessment using medical assessment scales:

[0056] First, a baseline assessment of the participants' attention level was conducted. Subsequently, a series of daily attention tasks were designed and required to be completed under controlled external stimulation conditions to test and induce the participants' attention ability. During the execution of the tasks, the attention model calculated the TBR indicator to monitor and intuitively display the participants' attention status in real time. Changes in this indicator directly guided the adjustment of personalized electrical stimulation plans to adapt to the real-time needs of the participants. The improvement in attention was systematically evaluated by tracking the participants' performance over a long period of time and regularly repeating the evaluation scale.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] Personalized attention enhancement program: The present invention can accurately capture the changes in an individual's attention under different states by analyzing the individual's electroencephalogram (EEG) signals in real time, especially the ratio of theta waves to beta waves (TBR). Using this data, the system can customize personalized stimulation programs to suit the individual's current needs, rather than adopting a one-size-fits-all approach. The implementation of personalized programs makes interventions to enhance attention more precise and effective, because each person's brain response and attention patterns are unique.

[0059] Dynamic electrical stimulation regulation: The closed-loop system design of the present invention allows for dynamic adjustment of electrical stimulation parameters, including intensity, frequency, and duration of stimulation, based on real-time feedback from EEG signals. This dynamic regulation mechanism ensures that electrical stimulation is always synchronized with the individual's current brain state and attention needs, thereby maximizing the stimulation effect. Compared with traditional fixed-mode electrical stimulation, dynamic regulation can more flexibly respond to various changes that may occur in individuals during task execution, such as fluctuations in attention or increases in task difficulty.

[0060] Real-time closed-loop monitoring and feedback: A key innovation of the present invention is the introduction of a real-time closed-loop monitoring and feedback mechanism, which is achieved by continuously tracking the ratio of theta waves to beta waves (TBR) in the electroencephalogram (EEG). The closed-loop system is able to respond instantly to monitored EEG changes and adjust the electrical stimulation parameters in real time through an automated algorithm to ensure that the stimulation always matches the individual's current state of attention. This real-time feedback not only improves the accuracy of the stimulation, but also allows the system to quickly adapt to any changes in attention levels, whether due to task difficulty, external interference, or individual physiological changes. Participants can obtain immediate feedback through real-time data visualization, which helps to enhance awareness of attention status and make manual interventions or adjustments when necessary. The real-time closed-loop monitoring and feedback mechanism significantly improves the adaptability and flexibility of the system, enabling it to cope with a variety of complex and dynamic real-world conditions, thereby effectively improving attention in a variety of environments and situations.

[0061] By integrating real-time closed-loop detection feedback into the attention monitoring system, the present invention can not only provide continuous attention state assessment, but also ensure that the implementation of the electrical stimulation program is always centered on the actual needs of the individual, thereby maximizing the effectiveness and efficiency of the intervention. This real-time, adaptive monitoring and feedback method provides a new standard for personalized cognitive intervention. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is the overall flow chart of the present invention;

[0063] Figure 2 A flow chart for the overall use of the present invention;

[0064] Figure 3 Schematic diagram of residual model parameters of the present invention;

[0065] Figure 4 This is a classification diagram of the domain generalization model of the present invention. DETAILED DESCRIPTION

[0066] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0067] See also Figures 1 to 4 The present invention provides a technical solution: a closed-loop system for attention control based on EEG signals, which consists of a hardware part and a software part.

[0068] The hardware part includes:

[0069] The head-mounted device is equipped with highly sensitive EEG electrodes to collect EEG signals from the frontal lobe. The collected signals are transmitted to a computer or mobile device in real time via wireless communication technology for further processing and analysis.

[0070] The multi-channel programmable electrical stimulation device dynamically adjusts the parameters of electrical stimulation, such as current size, waveform frequency and stimulation duration, according to algorithm feedback, and provides personalized stimulation programs. When performing electrical stimulation regulation, the model of the present invention provides multiple stimulation modes, and controls each channel with two parameters, such as current size, voltage size, waveform control, and frequency control, to achieve multi-channel and multi-mode control. At the beginning of stimulation, the device of this invention can select a commonly used basic stimulation method, such as using a sine wave as a stimulation mode, and obtain a dynamically changing stimulation mode through EEG feedback; the purpose of hardware design is to achieve Real-time monitoring and regulation of user attention status improves the convenience of the device and user experience; when designing the circuit for EEG collection of head-mounted devices, the first thing to consider is the EEG parameter indicators. The amplitude of EEG is 5-200 microvolts and the frequency is below 70Hz. When designing, this design fully considers the relevant characteristics of EEG and adopts the chips design of ads1299. At the same time, in the hardware design, a filtering circuit based on the EEG characteristics is adopted, and a low-pass filtering circuit below 70Hz is used. Due to the interference of EEG, the problem of hardware circuit noise needs to be considered during the design. For example, the interference of PCB components and the impedance of PCB routing are all handled by circuits matching the chip in this design, which can effectively avoid the noise problems caused by the design of the circuit board itself. When designing EEG stimulation, the multi-channel programmable electrical stimulation device needs to fully consider the mode and size of EEG stimulation. Transcranial direct current stimulation generally uses current stimulation below 2mA, and transcranial alternating current stimulation generally uses sine waves and square waves. The chip model selected by the device can output a maximum current mode of 67mA and a variety of stimulation models. At the same time, the waveform mode can be customized and edited, and the communication delay of the chip is less than 1ms, which can provide real-time and fast data feedback.

[0071] The software part includes:

[0072] The signal preprocessing module filters and denoises the collected EEG signals to remove environmental noise and artifacts to ensure signal purity;

[0073] The feature extraction module extracts attention-related features, such as EEG frequency band power, from the preprocessed EEG signal;

[0074] The attention state classification module uses a residual domain generalization model to classify the extracted features and judge the user's attention state. It uses an advanced residual domain generalization model for EEG signal processing and attention state classification. By learning the characteristics of residual networks and domain generalization, the model can generalize between different users and provide consistent and accurate classification results.

[0075] The feedback control module adjusts the parameters of the electrical stimulation device in real time according to the classification results to ensure the effectiveness and pertinence of the stimulation;

[0076] The overall user interface is divided into two types of interfaces, the acquisition interface and the stimulation interface. The data is collected through the electrical sensor, and the EEG signal is collected to the wearable device. Then the data is transmitted to the host through the wireless transmission module of the wearable device. The host obtains the state of attention through data analysis and model prediction. According to the state of attention, the parameter current, waveform, etc. are adjusted, and the corresponding parameters are sent to the wearable module. After the module receives the parameters, it adjusts the relevant indicators of the hardware electrical stimulation, and the stimulation feedback changes, thereby achieving the purpose of closed-loop attention regulation.

[0077] In this embodiment, the residual domain generalization model includes:

[0078] Residual network: By stacking multiple residual blocks to extract the deep features of the signal, the residual network can effectively capture the complex features in the EEG signal;

[0079] Domain generalization model: By learning shared features between different domains to improve the robustness of classification, the domain generalization model can adapt to the EEG signal characteristics of different users and provide personalized attention assessment;

[0080] Algorithm design: Accurately evaluate and regulate the user's attention state through EEG signals; through real-time attention state evaluation and feedback control, the present invention constructs an efficient closed-loop system, which significantly improves the accuracy and personalization level of attention state judgment.

[0081] In this embodiment, the head-mounted device uses the residual domain generalization model to extract multi-scale features related to attention from the preprocessed EEG signal, and performs accurate classification to determine the user's attention state, thereby realizing multi-level feature extraction and classification; the multi-channel programmable electrical stimulation device adjusts the parameters of the electrical stimulation device in real time according to the classification results, including waveform, frequency, current size and stimulation duration, to provide a personalized electrical stimulation plan, ensure the effectiveness and pertinence of the stimulation, and realize real-time feedback and dynamic regulation; the present invention provides a closed-loop system that can monitor and regulate the user's attention state in real time by integrating advanced hardware design and efficient algorithm models, significantly improving the effect of attention regulation and user experience. Through accurate attention evaluation and dynamic feedback regulation, the present invention provides users with an efficient, convenient and personalized attention regulation solution.

[0082] In this embodiment, the head-mounted device is also provided with a wireless communication module connected to the multi-channel programmable electrical stimulation device and the host end. The wireless communication module integrates the functions of data acquisition and data transmission. In this design, the data sampling rate must be considered to reach a sampling rate of 1000hz. Considering the power consumption of this system, it is determined to use the esp32 Bluetooth low power mode. Through the design of the module, a high sampling rate of EEG can be achieved, and there is no delay in Bluetooth transmission.

[0083] In this embodiment, the specific steps of using the closed-loop system are as follows:

[0084] Signal acquisition:

[0085] By collecting 1000Hz EEG signals, the real-time EEG signals are sent to the host device via WiFi communication to obtain the original EEG data; each real-time judgment will take 2s as a basis for judgment;

[0086] Data preprocessing:

[0087] The data preprocessing process requires the use of bandpass filters and bandstop filters; the bandstop will filter out the 50Hz power frequency interference signal; at the same time, because the attention EEG signal is related to theta waves and beta waves, the bandpass filter is used to filter and retain the theta wave and alpha signal;

[0088] Feature extraction:

[0089] After data preprocessing, the frequency domain features will be calculated using the Fourier transform algorithm as follows, and then converted into power spectrum features; the power spectrum features of EEG will be used for algorithmic features of EEG attention for training and detection;

[0090]

[0091] Where N is the number of sample points, x[n] is the discrete time signal, X[k] is the discrete frequency domain signal, (where If it is a negative function, it can be omitted; if Σ is a sum function, it can also be omitted); the power spectrum P(f) represents the change of the power of the frequency domain signal with the frequency. The power spectrum can be obtained by calculating the square of the amplitude spectrum. The following formula x(f) represents the amplitude of the corresponding frequency, and its square value is the power spectrum, that is, P(f);

[0092] p(f)=|x(f)| 2

[0093] As above, the frequency domain signal of the EEG is processed to convert it into a power spectrum signal, which is used as the EEG state characteristic signal for algorithm processing;

[0094] Attention status assessment:

[0095] State evaluation is divided into two parts. The first is the model pre-training process; the second is the data evaluation process; the model pre-training process requires the collection of attention EEG data, and an attention state evaluation model is obtained through the algorithm model to classify the attention state and the non-attention state; the residual domain generalization model, the model formula is as follows:

[0096] y=f(x)+x

[0097] Feature y is the sum of input feature x after feature transformation function f and original input feature xx;

[0098]

[0099] P(y|x) represents the probability that the output feature y belongs to category k when the input feature x is given; f k (x) is the feature transformation result for category k, and the denominator is for all possible categories j The sum of

[0100] Through different convolution kernels, the input EEG power spectrum density features are convolved to different degrees. Three layers of convolution are used, and the final results are feature fused to obtain the classification features. Figure 3 , that is, the EEG feature extraction model.

[0101] In this embodiment, when performing classification feature processing, due to the large individual differences, the model used is a domain generalization model to reduce the differences between individuals. Classification A and classification B are EEG classifications between different individuals. By reducing the loss of model training, individuals cannot be classified, thereby reducing individual differences and the model focuses more on attention features.

[0102] In this embodiment, the software part of the EEG feedback system is built with Python as the core framework language; the system interface uses the pyqt5 library to build the software framework, and the interface development utilizes web technology. Specifically, JavaScript and HTML5 are used to build the system's user interface and its interactive functions. In order to achieve communication between Python and JavaScript, the system uses JSBridge technology; after the data is collected, the EEG signal is transmitted to the computer in real time through the websocket communication protocol. On the computer side, a real-time model is responsible for processing these signals and performing judgment and analysis. The output results of the model are then fed back to the user interface in real time. Users can instantly see their EEG activity status and make corresponding adjustments based on the feedback provided by the system.

[0103] In this embodiment, the closed-loop system further includes:

[0104] Attention Regulation

[0105] The design of the software and hardware system can dynamically adjust the parameters of electrical stimulation and judge attention in multiple modes. First, the evidence of attention shows that the ratio of theta waves to beta waves can reflect the degree of attention. The higher the degree of attention, the higher the ratio of theta to beta. In the stage of attention regulation, the EEG data of eyes-open relaxation is first collected to calculate the ratio of theta to beta as the baseline of attention.

[0106] System design optimization description:

[0107] Baseline data collection: Before the experiment, let the subjects relax with their eyes open, and collect electroencephalogram (EEG) data at this time; calculate the ratio of theta waves to beta waves (TBR) as the baseline level of attention;

[0108] Basic stimulation settings: select one EEG channel in the prefrontal region as the basic channel, and set 0.8 mA direct current stimulation as the initial stimulation mode;

[0109] Task and stimulation synchronization: let the subjects alternate between attention tasks and relaxation tasks; during the performance of the attention task, basal direct current stimulation is applied synchronously;

[0110] Real-time TBR monitoring: During stimulation, TBR is calculated in real time and compared with the baseline TBR in the relaxed state;

[0111] Adaptive current regulation: If the subject is in an attentive state and the TBR is higher than the relaxed state, the system will automatically adjust the stimulation current according to the change of TBR; if the TBR does not rise, the current will be gradually increased in steps of 0.05mA until it does not exceed 2mA; if the TBR exceeds the relaxed state, the current intensity will be gradually reduced;

[0112] Relaxation stimulation: During the relaxation period, the alternating current stimulation frequency is used to induce alpha waves to promote rest and relaxation;

[0113] Feedback loop: The system continuously monitors TBR and dynamically adjusts stimulation parameters based on its changes, forming a closed-loop feedback mechanism;

[0114] Attention Improvement Assessment

[0115] The closed-loop system builds a more logical attention improvement assessment process, combining neural electrical stimulation and daily attention tasks, as well as systematic assessment using medical assessment scales:

[0116] First, a baseline assessment of the participants' attention level was conducted. Then, a series of daily attention tasks were designed, which were required to be completed under the condition of controlling external stimulation to test and induce the participants' attention ability. During the task execution, the attention model calculated the TBR index to monitor and intuitively display the participants' attention status in real time. The change of this index directly guided the adjustment of the personalized electrical stimulation plan to adapt to the real-time needs of the participants.

[0117] Improvements in attention were systematically assessed by tracking participants’ performance over time and using assessment scales repeatedly at regular intervals; this approach, which combines real-time monitoring, dynamic stimulation adjustment, and long-term assessment, not only validated the potential effects of electrical stimulation on improving attention, but also activated participants’ proactive attention and concentration abilities through the performance of attention tasks;

[0118] The methodological innovation of this study is that it no longer relies on a single fixed stimulation pattern, but provides each participant with a personalized attention enhancement program through real-time monitoring and dynamic adjustment; the implementation of this program is expected to significantly improve the performance of participants in long-term concentration tasks, and provide new perspectives and tools for the intervention of attention deficits and related cognitive disorders.

[0119] Although embodiments of the present invention have been shown and described (see the above detailed description for details), it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A closed-loop attention control system based on EEG signals, characterized in that: The closed-loop system consists of hardware and software. The hardware part includes: The head-mounted device is equipped with highly sensitive EEG electrodes to collect EEG signals from the frontal lobe. The collected signals are transmitted to a computer or mobile device in real time via wireless communication technology for further processing and analysis. A multi-channel programmable electrical stimulation device that dynamically adjusts the parameters of electrical stimulation based on algorithm feedback to provide a personalized stimulation program; The software part includes: The signal preprocessing module filters and denoises the collected EEG signals to remove environmental noise and artifacts to ensure signal purity; Feature extraction module, extracts attention-related features from the preprocessed EEG signal; The attention state classification module uses the residual domain generalization model to classify the extracted features and determine the user's attention state; The feedback control module adjusts the parameters of the electrical stimulation device in real time according to the classification results to ensure the effectiveness and pertinence of the stimulation.

2. The closed-loop attention control system based on EEG signals according to claim 1, characterized in that: The residual domain generalization model includes: Residual network: By stacking multiple residual blocks to extract the deep features of the signal, the residual network can effectively capture the complex features in the EEG signal; Domain generalization model: By learning shared features between different domains to improve the robustness of classification, the domain generalization model can adapt to the EEG signal characteristics of different users and provide personalized attention assessment; Algorithm design: Accurately evaluate and regulate the user's attention state through EEG signals; through real-time attention state evaluation and feedback control.

3. The closed-loop attention control system based on EEG signals according to claim 1, characterized in that: The head-mounted device uses a residual domain generalization model to extract multi-scale features related to attention from the preprocessed EEG signal, and performs accurate classification to determine the user's attention state, thereby realizing multi-level feature extraction and classification; the multi-channel programmable electrical stimulation device adjusts the parameters of the electrical stimulation device in real time according to the classification results, including waveform, frequency, current size and stimulation duration, so as to provide a personalized electrical stimulation plan, ensure the effectiveness and pertinence of the stimulation, and realize real-time feedback and dynamic regulation.

4. The closed-loop attention control system based on EEG signals according to claim 1, characterized in that: The head mounted device is also provided with a wireless communication module connected to the multi-channel programmable electrical stimulation device and the host end, and the wireless communication module integrates the functions of data collection and data transmission.

5. The closed-loop attention control system based on EEG signals according to claim 1, characterized in that: The specific steps of using the closed-loop system are as follows: Signal acquisition: By collecting 1000Hz EEG signals, the real-time EEG signals are sent to the host device via WiFi communication to obtain the original EEG data; each real-time judgment will take 2s as a basis for judgment; Data preprocessing: The data preprocessing process requires the use of bandpass filters and bandstop filters; the bandstop will filter out the 50Hz power frequency interference signal; at the same time, because the attention EEG signal is related to theta waves and beta waves, the bandpass filter is used to filter and retain the theta wave and alpha signal; Feature extraction: After data preprocessing, the frequency domain features are calculated using the Fourier transform algorithm as follows, and then converted into power spectrum features; The power spectrum features of EEG will be used to characterize the algorithm of EEG attention for training and detection; Where N is the number of sample points, x[n] is the discrete time signal, X[k] is the discrete frequency domain signal, (where If it is a negative function, it can be omitted; if Σ is a sum function, it can also be omitted); the power spectrum P(f) represents the change of the power of the frequency domain signal with the frequency. The power spectrum can be obtained by calculating the square of the amplitude spectrum. The following formula x(f) represents the amplitude of the corresponding frequency, and its square value is the power spectrum, that is, P(f); p(f)=|x(f)| 2 As above, the frequency domain signal of the EEG is processed to convert it into a power spectrum signal, which is used as the EEG state characteristic signal for algorithm processing; Attention status assessment: State evaluation is divided into two parts. The first is the model pre-training process; the second is the data evaluation process; the model pre-training process requires the collection of attention EEG data, and an attention state evaluation model is obtained through the algorithm model to classify the attention state and the non-attention state; the residual domain generalization model, the model formula is as follows: y=f(x)+x Feature y is the sum of input feature x after feature transformation function f and original input feature xx; P(y|x) represents the probability that the output feature y belongs to category k when the input feature x is given; f k (x) is the feature transformation result for category k, and the denominator is for all possible categories j The sum of Through different convolution kernels, the input EEG power spectrum density features are convolved to different degrees, and three layers of convolution are used. The final results are fused to obtain the classification features, that is, the EEG feature extraction model.

6. The closed-loop attention control system based on EEG signals according to claim 5, characterized in that: When processing classification features, due to the large individual differences, the model used is the domain generalization model to reduce the differences between individuals.

7. The closed-loop attention control system based on EEG signals according to claim 1, characterized in that: The software part is an EEG feedback system built with Python as the core framework language; the system interface uses the pyqt5 library to build the software framework, and the interface development utilizes web technology. Specifically, JavaScript and HTML5 are used to build the system's user interface and its interactive functions. In order to achieve communication between Python and JavaScript, the system uses JSBridge technology; after the data is collected, the EEG signal is transmitted to the computer in real time through the websocket communication protocol. On the computer side, a real-time model is responsible for processing these signals and performing judgment and analysis. The output results of the model are then fed back to the user interface in real time. Users can instantly see their EEG activity status and make corresponding adjustments based on the feedback provided by the system.

8. The closed-loop attention control system based on EEG signals according to claim 1, characterized in that: In a closed-loop system, it also includes: Attention Regulation The design of the software and hardware system can dynamically adjust the parameters of electrical stimulation and judge attention in multiple modes. First, the evidence of attention shows that the ratio of theta waves to beta waves can reflect the degree of attention. The higher the degree of attention, the higher the ratio of theta to beta. In the stage of attention regulation, the EEG data of eyes-open relaxation is first collected to calculate the ratio of theta to beta as the baseline of attention. System design optimization description: Baseline data collection: Before the experiment, let the subjects relax with their eyes open, and collect electroencephalogram (EEG) data at this time; calculate the ratio of theta waves to beta waves (TBR) as the baseline level of attention; Basic stimulation settings: select one EEG channel in the prefrontal region as the basic channel, and set 0.8 mA direct current stimulation as the initial stimulation mode; Task and stimulation synchronization: let the subjects alternate between attention tasks and relaxation tasks; during the performance of the attention task, basal direct current stimulation is applied synchronously; Real-time TBR monitoring: During stimulation, TBR is calculated in real time and compared with the baseline TBR in the relaxed state; Adaptive current regulation: If the subject is in an attentive state and the TBR is higher than the relaxed state, the system will automatically adjust the stimulation current according to the change of TBR; if the TBR does not rise, the current will be gradually increased in steps of 0.05mA until it does not exceed 2mA; if the TBR exceeds the relaxed state, the current intensity will be gradually reduced; Relaxation stimulation: During the relaxation period, the alternating current stimulation frequency is used to induce alpha waves to promote rest and relaxation; Feedback loop: The system continuously monitors TBR and dynamically adjusts stimulation parameters based on its changes, forming a closed-loop feedback mechanism; Attention Improvement Assessment The closed-loop system builds a more logical attention improvement assessment process, combining neural electrical stimulation and daily attention tasks, as well as systematic assessment using medical assessment scales: First, a baseline assessment of the participants' attention level was conducted. Subsequently, a series of daily attention tasks were designed and required to be completed under controlled external stimulation conditions to test and induce the participants' attention ability. During the execution of the tasks, the attention model calculated the TBR indicator to monitor and intuitively display the participants' attention status in real time. Changes in this indicator directly guided the adjustment of personalized electrical stimulation plans to adapt to the real-time needs of the participants. The improvement in attention was systematically evaluated by tracking the participants' performance over a long period of time and regularly repeating the evaluation scale.

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