Golf cap integrated real-time electroencephalogram concentration detection method and system

Abstract

The invention provides a golf cap integrated real-time electroencephalogram concentration detection method and system, and relates to the technical field of concentration detection.The method comprises the steps that a sensor on a golf cap is used for obtaining electroencephalogram signals in real time, and the electroencephalogram signals are preprocessed to obtain preprocessed electroencephalogram signals; extracting feature parameters of the electroencephalogram signals according to the preprocessed electroencephalogram signals; analyzing electroencephalogram concentration according to the characteristic parameters of the electroencephalogram signals and a concentration model to obtain an analysis result; judging the concentration level according to the analysis result and a preset frequency band to obtain a judgment result; according to the judgment result, the golf cap reminds the user through vibration or music playing; and acquiring and recording all the acquired electroencephalogram signal data in real time, and optimizing the concentration model to obtain an optimized model. The invention provides a golf cap integrated real-time electroencephalogram concentration detection method and system, and solves the problem that the concentration of a user is not concentrated when the user plays golf.

Description

Technical Field

[0002] The present invention relates to the field of concentration detection technology, and in particular to a golf cap integrated real-time EEG concentration detection method and system. Background Art

[0003] With the popularity of golf and the improvement of competitive level, athletes are paying more and more attention to training effects and competition performance. As one of the key factors affecting the performance of golf players, accurate detection and evaluation of concentration is particularly important. However, traditional concentration evaluation methods often rely on subjective observation and experience judgment, lacking objectivity and real-time performance. Therefore, developing a method and system that can accurately detect the concentration of golf players in real time is of great significance for improving training effects and optimizing competition strategies.

[0004] In recent years, with the rapid development of electroencephalogram (EEG) acquisition and processing technology, EEG-based concentration detection methods have gradually become a research hotspot. By collecting and analyzing the electrical activity of the cerebral cortex, the individual's concentration level can be objectively reflected. At the same time, with the continuous advancement of wearable devices and wireless communication technology, it has become possible to integrate EEG acquisition equipment into daily wearable items to achieve real-time and portable concentration detection.

[0005] Integrating the traditional single-lead concentration detection device with the golf cap can detect the user's concentration in real time when the user is playing golf. If the user is not concentrating, the user can be given some vibration or music reminders. Summary of the invention

[0006] The present invention provides a golf cap integrated real-time EEG concentration detection method and system, which solves the problem of users' lack of concentration when playing golf.

[0007] In order to solve the above technical problems, the technical solution of the present invention is as follows: In a first aspect, a golf cap integrated real-time EEG concentration detection method is provided, the method comprising: Using the sensor on the golf cap to acquire the EEG signal in real time, and preprocessing it to obtain the preprocessed EEG signal; Extracting characteristic parameters of the EEG signal according to the preprocessed EEG signal; According to the characteristic parameters of the EEG signal, the concentration model analyzes the EEG concentration to obtain the analysis results; According to the analysis results and the preset frequency band, the concentration level is judged to obtain a judgment result; Based on the judgment result, the golf cap reminds the user by vibrating or playing music; All collected EEG signal data are acquired and recorded in real time, and the concentration model is optimized to obtain an optimized model.

[0008] Furthermore, the sensor on the golf cap is used to obtain the EEG signal in real time and preprocess it to obtain the preprocessed EEG signal, including: Use the sensor integrated into the golf cap to obtain the user's EEG signals in real time; Convert EEG signals into electrical signals and transmit them to the signal acquisition module; The signal acquisition module transmits the received EEG signal to the preprocessing module; The preprocessing module removes high-frequency noise and low-frequency interference from the EEG signal to obtain the EEG signal after filtering; According to the filtered EEG signal, artifact removal processing is performed to obtain a preprocessed EEG signal.

[0009] Furthermore, characteristic parameters of the EEG signal are extracted based on the preprocessed EEG signal, including: According to the preprocessed EEG signal, the preprocessed EEG signal is analyzed using wavelet transform to obtain the energy distribution of the signal at different frequencies and different time points; According to the preprocessed EEG signal, Fourier transform is performed on the EEG signal to convert the EEG signal from the time domain to the frequency domain to obtain the frequency component of the signal; Based on the preprocessed EEG signals, independent component analysis is used to separate the independent components in the EEG signals to obtain independent signals related to specific brain activities; The characteristic parameters of the EEG signal are obtained by integrating the signal's energy distribution at different frequencies and time points, the signal's frequency components, and independent signals related to specific brain activities.

[0010] Furthermore, according to the characteristic parameters of the EEG signal, the concentration model analyzes the EEG concentration to obtain analysis results, including: According to characteristic parameters of the EEG signal, a preset frequency band in the EEG signal is analyzed to obtain a threshold value of EEG concentration, wherein the preset frequency band includes theta wave, alpha wave, beta wave and gamma wave; Based on the historical data of EEG signals, a concentration model is constructed using a long short-term memory network, and the concentration model is trained using the EEG signal dataset to obtain a trained concentration model; The trained concentration model is used to analyze the frequency bands of the characteristic parameters of the EEG signal to obtain the analysis results.

[0011] Furthermore, the concentration level is judged according to the analysis results and the preset frequency band to obtain a judgment result, including: According to the preset frequency band, the concentration level is divided into different levels to obtain the preset concentration level; According to the preset concentration level, the analysis result is compared with the preset frequency band to obtain the judgment result; If the analysis result is within a certain frequency band, it is determined that the user is at a concentration level corresponding to the frequency band; If the analysis result is below all set thresholds, the user is judged to be at the lowest level of concentration.

[0012] Furthermore, according to the judgment result, the golf cap reminds the user by vibrating or playing music, including: Set the conditions for triggering reminders based on actual needs and application scenarios; Compare the judgment result with the reminder condition, and if the judgment result meets the reminder condition, remind the user; According to user needs, the vibration or music reminder module in the golf cap is activated to remind the user.

[0013] Furthermore, all collected EEG signal data are acquired and recorded in real time, and the concentration model is optimized to obtain an optimized model, including: Use the sensor on the golf cap to collect the user's EEG data in real time; The real-time collected EEG signal data is stored in a local device or a cloud server, and the data is labeled to obtain labeled data; Extract characteristic parameters related to concentration based on the labeled data; According to the characteristic parameters, the concentration model is optimized to obtain an optimized model; Repeat the above steps to get the final optimized model.

[0014] In the second aspect, a golf cap integrated real-time EEG concentration detection system includes: An acquisition module is used to acquire the EEG signal in real time using the sensor on the golf cap, and perform preprocessing to obtain the preprocessed EEG signal; The processing module is used to extract characteristic parameters of the EEG signal based on the preprocessed EEG signal; the concentration model analyzes the EEG concentration based on the characteristic parameters of the EEG signal to obtain an analysis result; the concentration level is judged based on the analysis result and a preset frequency band to obtain a judgment result; based on the judgment result, the golf cap reminds the user by vibrating or playing music; all collected EEG signal data are acquired and recorded in real time, and the concentration model is optimized to obtain an optimized model.

[0015] According to a third aspect, a computing device includes: one or more processors; The storage system is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the above method.

[0016] In a fourth aspect, a computer-readable storage medium stores a program, and the program implements the above method when executed by a processor.

[0017] The above solution of the present invention includes at least the following beneficial effects: In the above scheme of the present invention, the golf cap is used as a carrier, making the EEG signal acquisition equipment more portable, and the user does not need to wear a complicated helmet or head-mounted device. At the same time, the design of the golf cap also takes comfort into consideration to ensure that the user does not feel uncomfortable when wearing it for a long time. The system judges the result based on the user's concentration level and makes personalized reminders through vibration or playing music. This reminder method is timely and effective, and can help the user quickly adjust the state and avoid distraction or fatigue. The system acquires and records all collected EEG signal data in real time, and optimizes the concentration model. With the continuous accumulation of data, the accuracy and generalization ability of the model will continue to improve, providing users with more accurate analysis and feedback. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flowchart of a golf cap integrated real-time EEG concentration detection method provided by an embodiment of the present invention.

[0019] Figure 2 It is a schematic diagram of a golf cap integrated real-time EEG concentration detection system provided by an embodiment of the present invention.

[0020] FIG. 3 is a schematic diagram of the outer structure of a golf cap provided by an embodiment of the present invention.

[0021] Figure 4 The figure is a schematic diagram of the inner structure of a golf cap provided by an embodiment of the present invention.

[0022] Reference numerals: 31. Golf cap; 32. Detection device; 33. Connection contacts. DETAILED DESCRIPTION

[0023] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0024] like Figure 1 As shown, an embodiment of the present invention provides a golf cap integrated real-time EEG concentration detection method, the method comprising: 11. Use the sensor on the golf cap to obtain the EEG signal in real time and perform preprocessing to obtain the preprocessed EEG signal; 12. Extract characteristic parameters of the EEG signal based on the preprocessed EEG signal; 13. According to the characteristic parameters of the EEG signal, the concentration model analyzes the EEG concentration to obtain the analysis results; 14. According to the analysis results and the preset frequency band, the concentration level is judged to obtain a judgment result; 15. Based on the judgment result, the golf cap reminds the user by vibrating or playing music; 16. Obtain and record all collected EEG signal data in real time, and optimize the concentration model to obtain an optimized model.

[0025] In the embodiment of the present invention, the sensor on the golf cap can be used to obtain the user's EEG signal in real time and non-invasively. The signal is then transmitted to the preprocessing module, and after the steps of removing high-frequency noise and low-frequency interference, removing artifacts, etc., a high-quality preprocessed EEG signal is obtained, thereby ensuring the accuracy and reliability of subsequent analysis; the preprocessed EEG signal is extracted with characteristic parameters related to concentration through technical means such as wavelet transform, Fourier transform and independent component analysis. The characteristic parameters can reflect the activity pattern of the brain in different states, and provide an important data basis for subsequent concentration analysis; the concentration model uses the extracted characteristic parameters to reduce the feature dimension through recursive feature elimination, and uses deep learning algorithms such as long short-term memory network for training and analysis, which can accurately evaluate the user's concentration level and compare it with the preset frequency band to obtain the judgment result of the concentration level; according to the judgment result of the concentration level, the golf cap gives personalized reminders to the user by vibration or playing music, which is both timely and effective, and can help the user quickly The system can quickly adjust the state and improve concentration. The system can also set different reminder conditions and reminder methods according to the actual needs of users and application scenarios to provide more personalized services. The system acquires and records all collected EEG signal data in real time to optimize the concentration model. With the continuous accumulation and analysis of data, the accuracy and generalization ability of the model will continue to improve, ensuring that the system can continue to adapt to the needs of different users and environments and provide more accurate analysis and feedback. For golf players, the system can monitor and adjust their concentration level in real time to help them better cope with the pressure and challenges of the game. By understanding their concentration performance in different situations, athletes can develop more targeted training plans and improve their competition results. In addition to its application in golf, the system can also be used in other fields that require a high degree of concentration, such as study and work. By providing users with personalized concentration training and improvement plans, the system helps promote users' mental health and cognitive ability, and improve work efficiency and quality of life.

[0026] like Figure 1 As shown in 11, the sensor on the golf cap is used to obtain the EEG signal in real time, and preprocess it to obtain the preprocessed EEG signal, including: 111, using sensors integrated into a golf cap to obtain the user’s EEG signals in real time; 112, converting the EEG signal into an electrical signal and transmitting it to a signal acquisition module; 113, the signal acquisition module transmits the received EEG signal to the preprocessing module; 114, a preprocessing module removes high-frequency noise and low-frequency interference from the EEG signal to obtain a filtered EEG signal; 115 , performing artifact removal processing on the filtered EEG signal to obtain a pre-processed EEG signal.

[0027] In the embodiment of the present invention, the sensor integrated on the golf cap can obtain the user's EEG signal in real time and non-invasively, which directly reflects the electrophysiological activity of the brain and is the basis for subsequent analysis and evaluation of concentration. By obtaining the EEG signal in real time, the system can instantly capture the changes in the user's brain state and provide the latest data support for subsequent processing; the acquired EEG signal is first converted into an electrical signal and transmitted to the signal acquisition module through an internal transmission line or wirelessly, ensuring the stability and accuracy of the signal and providing a reliable data source for subsequent processing; the signal acquisition module is responsible for receiving the EEG signal from the sensor and passing it to the preprocessing module, ensuring the integrity and continuity of the signal and providing a basis for subsequent signal processing and analysis; the preprocessing module The module filters the received EEG signals to remove high-frequency noise and low-frequency interference. High-frequency noise may come from electromagnetic interference in the external environment, while low-frequency interference may come from the user's own physiological activities. Through filtering, the system can extract purer EEG signals and provide higher-quality data for subsequent analysis. On the basis of filtering, the preprocessing module also performs artifact removal. Artifacts refer to signal interference caused by user body movement, eye activity or other non-EEG activities. Interference signals may mask or distort real EEG signals, thereby affecting subsequent analysis results. Through artifact removal, the system can further improve the purity and reliability of EEG signals, providing stronger support for subsequent feature extraction and concentration analysis.

[0028] like Figure 1 As shown in 12, according to the preprocessed EEG signal, characteristic parameters of the EEG signal are extracted, including: 121, analyzing the preprocessed EEG signal using wavelet transform to obtain energy distribution of the signal at different frequencies and different time points; 122, performing Fourier transform on the EEG signal according to the preprocessed EEG signal, converting the EEG signal from the time domain to the frequency domain, and obtaining the frequency component of the signal; 123, according to the preprocessed EEG signal, using independent component analysis to separate the independent components in the EEG signal to obtain independent signals related to specific brain activities; 124. The characteristic parameters of the EEG signal are obtained by integrating the energy distribution of the signal at different frequencies and different time points, the frequency components of the signal, and the independent signals related to specific brain activities.

[0029] In an embodiment of the present invention, based on the preprocessed EEG signal, the system uses wavelet transform to conduct in-depth analysis on the preprocessed EEG signal. Wavelet transform is a mathematical tool that can simultaneously provide localized information of the signal in the time domain and the frequency domain. By performing wavelet transform on the preprocessed EEG signal, the system can obtain the energy distribution of the signal at different frequencies and different time points. The energy distribution information can reveal the activity pattern of the brain in different states, and provide important clues for subsequent feature extraction; the system performs Fourier transform on the preprocessed EEG signal. Fourier transform is a mathematical method that converts the signal from the time domain to the frequency domain. Through Fourier transform, the system can obtain the frequency component of the EEG signal, that is, the signal amplitude at different frequencies. The frequency component reflects the activity intensity of the brain in different frequency ranges, which is very important for understanding the brain. The functional state of the brain is of great significance; on the basis of obtaining the frequency components, the system uses independent component analysis (ICA) to separate the independent components in the EEG signal. Independent component analysis can separate independent signal components from multi-channel signals. Through ICA, the system can extract independent signals related to specific brain activities. Independent signals have higher signal-to-noise ratio and better interpretability, providing more direct data support for subsequent feature extraction; the system integrates the energy distribution of the signal at different frequencies and different time points, the frequency components of the signal, and the independent signal related to specific brain activities to obtain the characteristic parameters of the EEG signal. The characteristic parameters can comprehensively reflect the activity characteristics of the brain in different states, providing a strong data basis for subsequent concentration analysis.

[0030] like Figure 1 As shown in 13, according to the characteristic parameters of the EEG signal, the concentration model analyzes the EEG concentration to obtain analysis results, including: 131. Analyze a preset frequency band in the EEG signal according to characteristic parameters of the EEG signal to obtain a threshold value of EEG concentration, wherein the preset frequency band includes θ wave, α wave, β wave and γ wave; 132. Based on the historical data of EEG signals, a concentration model is constructed using a long short-term memory network, and the concentration model is trained using the EEG signal dataset to obtain a trained concentration model; 133, the trained concentration model is used to analyze the frequency band of the characteristic parameters of the EEG signal to obtain the analysis results.

[0031] In the embodiment of the present invention, first, the signal strength of specific frequency bands such as theta waves, alpha waves, beta waves and gamma waves is extracted according to the characteristic parameters of the EEG signal. The characteristic parameters represent the activity of the brain in different states. In this example, the concentration can be calculated based on the intensity ratio of theta waves, alpha waves, beta waves and gamma waves, and a threshold is set to distinguish between the concentration and relaxation states; wherein theta waves are at 4-7 Hz, which are associated with deep relaxation and sleep, the alpha waves are at 8-13 Hz, which are associated with a quiet and relaxed alert state, the beta waves are at 13-30 Hz, which are associated with active thinking, concentration or anxiety, and the gamma waves are at 30-100 Hz. It is related to higher-level brain functions such as information processing, learning and memory formation; the higher the frequency band, the higher the concentration; by determining the concentration threshold, it can provide a benchmark for the subsequent use of the concentration model to analyze EEG signals; based on the historical data of EEG signals, a long short-term memory network (LSTM) is used to build a concentration model, and the long short-term memory network is used to analyze the time series data of EEG signals to obtain the constructed concentration model; then the LSTM model is trained to learn the association between features and concentration, and adjust its parameters to minimize the prediction error; the trained model is validated using methods such as cross-validation Verification is performed to ensure its accuracy and generalization ability; by constructing and training the LSTM concentration model, automatic analysis and prediction of EEG signals can be achieved, the complex relationship between features and concentration can be learned, and accurate predictions can be made on new EEG signal data; the trained concentration model is used to analyze the frequency bands of the characteristic parameters of the EEG signals to obtain the analysis results, and the EEG signal data to be analyzed is input into the trained LSTM concentration model. After the same preprocessing and feature extraction steps to ensure consistency with the training data, the LSTM model will make predictions based on the characteristic parameters of the input data and output a concentration score or state, which can represent the subject's concentration level at the current time point; according to the output of the model, the subject's concentration level is interpreted by comparing the concentration score with a preset threshold. If the score exceeds the threshold, the subject is considered to be in a state of concentration; otherwise, it is considered to be in a state of relaxation; the analysis results are applied to actual scenarios, such as evaluating the subject's attention level, optimizing the human-computer interaction interface, etc. By using the trained concentration model to analyze the EEG signals, the subject's concentration level at different time points can be obtained, which is used to evaluate the subject's attention state.

[0032] like Figure 1 As shown in 14, the concentration level is judged according to the analysis result and the preset frequency band to obtain a judgment result, including: 141, the concentration level is divided into different levels according to the preset frequency band to obtain the preset concentration level; 142, according to the preset concentration level, the analysis result is compared with the preset frequency band to obtain a judgment result; 143, if the analysis result is within a certain frequency band area, it is determined that the user is at a concentration level corresponding to the frequency band; 144, if the analysis result is lower than all the set thresholds, it is determined that the user is at the lowest concentration level.

[0033] In an embodiment of the present invention, a frequency band range is defined, and each range corresponds to a concentration level. For example, the following concentration levels are set: deep relaxation / sleep: θ wave, relaxation / not focusing: α wave, β wave, deep concentration: γ wave; the concentration level is quantified through the brain wave frequency band to provide a basis for subsequent analysis; a brain wave acquisition device is used to obtain the user's real-time brain wave data, and the collected brain wave data is subjected to frequency analysis to determine its main frequency components, and the user's brain wave data is analyzed in real time to provide data support for determining the user's current concentration state. If the analysis result falls within a certain frequency band, such as the α wave range, it is determined that the user is currently in a deep relaxation / sleep state; based on the analysis result of the brain wave data, the user's concentration level is accurately determined and immediate feedback is provided; if the analysis result is lower than all preset thresholds, the user is at the lowest concentration level and may be relaxed, distracted or in a sleeping state; it is ensured that the system can give reasonable judgments on all possible brain wave data to avoid the system from failing to work properly due to data anomalies.

[0034] like Figure 1 As shown in 15, according to the judgment result, the golf cap reminds the user by vibrating or playing music, including: 151. Set the conditions for triggering reminders according to actual needs and application scenarios; 152, comparing the judgment result with the reminder condition, and if the judgment result meets the reminder condition, reminding the user; 153. According to the user's needs, a vibration or music reminder module in the golf cap is activated to remind the user.

[0035] In an embodiment of the present invention, according to actual needs and application scenarios, the system sets conditions for triggering reminders. Reminders are triggered when the user is at a low concentration level, or when the user's concentration level continues to decline. The setting of reminder conditions is intended to ensure the timeliness and effectiveness of reminders, and help users maintain the best state in golf games or other scenarios that require high concentration; the system compares the judgment result of the concentration level with the preset reminder conditions. If the judgment result meets the reminder conditions, for example, the user's concentration level is lower than the preset frequency band, the system decides to remind the user. The comparison process is based on logical judgment to ensure the accuracy and reliability of the reminder; according to user needs, the system activates the vibration or music reminder module in the golf cap. The vibration reminder directly acts on the user through physical stimulation, which can quickly arouse the user's attention without disturbing the surrounding environment. The music reminder transmits information through sound signals, which may be more suitable for use in scenarios that require certain ambient sounds. The system can select the appropriate reminder method according to the user's preferences or application scenarios. Once the reminder module is started, the system begins to execute the reminder operation. The vibration reminder module will generate a slight vibration and transmit it to the user through the contact surface of the hat. The music reminder module will play preset music or sounds to remind the user in the form of sound signals. Parameters such as the intensity, frequency and duration of the reminder can be customized according to user needs and preferences to ensure the comfort and effectiveness of the reminder. During the reminder execution process, the system can also collect user feedback, such as whether the user responds to the reminder and how effective the reminder is. Based on these feedbacks, the system can adjust and optimize the reminder strategy to better meet the needs and preferences of the user.

[0036] like Figure 1 As shown in 16, all collected EEG signal data are acquired and recorded in real time, and the concentration model is optimized to obtain an optimized model, including: 161, using sensors on golf caps to collect user’s EEG data in real time; 162, storing the real-time collected EEG signal data in a local device or a cloud server, and annotating the data to obtain annotated data; 163, extracting characteristic parameters related to concentration based on the labeled data; 164, optimizing the concentration model according to the characteristic parameters to obtain an optimized model; 165, repeat the above steps to obtain the final optimized model.

[0037] In an embodiment of the present invention, the sensor integrated on the golf cap can collect the user's EEG signal data in real time. The data reflects the electrical activity of the user's brain in different states and is the basis for subsequent analysis and optimization. Real-time collection ensures the timeliness and accuracy of the data, so that the model can timely reflect the user's concentration state; the EEG signal data collected in real time is stored in a local device or cloud server for subsequent analysis and processing, and the data is labeled to obtain labeled data. The labeling process may include marking the collection time of the data, the user's concentration state and other relevant information. The labeled data provides more accurate and valuable information for model training and optimization; based on the labeled data, the system extracts characteristic parameters related to concentration, and the characteristic parameters include the frequency components, energy, and frequency components of the EEG signal. The quantity distribution, waveform characteristics, etc. can reflect the key information of the user's concentration state. The extraction of feature parameters is a key step in model training and optimization, which directly affects the performance of the model. According to the extracted feature parameters, the system optimizes the concentration model. The optimization process includes adjusting the model parameters, improving the model structure or adopting new algorithms. Through continuous optimization, the model can more accurately identify the user's concentration state and improve the accuracy and reliability of the analysis. The above steps are a continuous iterative process. With the continuous collection and annotation of new data, the system can continuously extract new feature parameters and further optimize the model. The iterative optimization process enables the model to continuously adapt to new environments and user needs, and improve the generalization ability and robustness of the model. After multiple iterative optimizations, the system obtains the final optimized model.

[0038] like Figure 2 As shown, an embodiment of the present invention further provides a golf cap integrated real-time EEG concentration detection system 20, comprising: An acquisition module 21 is used to acquire the EEG signal in real time using the sensor on the golf cap, and perform preprocessing to obtain the preprocessed EEG signal; The processing module 22 is used to extract characteristic parameters of the EEG signal based on the preprocessed EEG signal; the concentration model analyzes the EEG concentration based on the characteristic parameters of the EEG signal to obtain an analysis result; the concentration level is judged based on the analysis result and a preset frequency band to obtain a judgment result; based on the judgment result, the golf cap reminds the user by vibrating or playing music; all collected EEG signal data are acquired and recorded in real time, and the concentration model is optimized to obtain an optimized model.

[0039] In a specific embodiment of the present invention, by integrating a detection device 32 on a golf cap 31, the collected EEG data is transmitted to the APP using wireless communication technology, and then an algorithm analysis of concentration and fatigue is performed in the cloud, providing the user with real-time concentration and fatigue feedback; First, a detection device 32 is installed at the front end of the golf cap 31. The detection device 32 includes a battery controller, a communication module (such as a Bluetooth or WiFi module) and an EEG sensor. The EEG sensor is used to collect the user's EEG signals. Three connection contacts 33 are provided at the lower end of the detection device 32. The contacts are connected to the three channels inside the golf cap for transmitting electrical signals. The other parts of the golf cap are matched with the detection device through electrical connection to ensure that the three channels can stably transmit electrical signals. After the user wears the golf cap, the EEG sensor starts to collect the user's EEG signals, and the collected EEG signals are transmitted to the detection device through the three channels inside the golf cap. The battery controller in the detection device detects the EEG signals. The EEG data is processed and transmitted to the APP on the user's smartphone through the communication module (Bluetooth or WiFi); after receiving the EEG data, the APP uploads it to the cloud server; the cloud server processes and analyzes the received EEG data; the algorithm model calculates the user's current concentration level and fatigue level based on the characteristics of the EEG signal; the cloud sends the analysis results back to the APP, and the APP displays the user's current concentration level and fatigue level on the user interface; the user can adjust his or her status and improve his or her performance in golf based on the feedback on the APP; at the same time, the APP can also record the user's concentration and fatigue change history and provide users with personalized analysis and suggestions.

[0040] The golf cap integrated real-time EEG concentration detection method of the present invention integrates a detection device on the golf cap, utilizes wireless communication technology to transmit the collected EEG data to the APP, and performs algorithm analysis in the cloud, thereby providing users with real-time concentration and fatigue feedback, which not only improves the user's performance in golf, but also provides users with personalized health monitoring services.

[0041] It should be noted that the system is a system corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0042] The embodiment of the present invention further provides a computing device, comprising: a processor, a memory storing a computer program, wherein when the computer program is executed by the processor, the method described above is executed. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0043] The embodiment of the present invention also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the method described above. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0044] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0045] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0046] In the embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or units, which can be electrical, mechanical or other forms.

[0047] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0048] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0049] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical disks.

[0050] In addition, it should be noted that in the system and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. Moreover, the steps of performing the above series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order, and some steps can be performed in parallel or independently of each other. For those of ordinary skill in the art, it is understandable that all or any steps or components of the method and system of the present invention can be implemented in any computing system (including processors, storage media, etc.) or a network of computing systems in hardware, firmware, software or a combination thereof, which can be achieved by those of ordinary skill in the art using their basic programming skills after reading the description of the present invention.

[0051] Therefore, the purpose of the present invention can also be achieved by running a program or a group of programs on any computing system. The computing system can be a well-known general system. Therefore, the purpose of the present invention can also be achieved by simply providing a program product containing a program code that implements the method or system. That is to say, such a program product also constitutes the present invention, and the storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any well-known storage medium or any storage medium developed in the future. It should also be pointed out that in the system and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. In addition, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order. Some steps can be performed in parallel or independently of each other.

[0052] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A golf cap integrated real-time EEG concentration detection method, characterized in that: The method comprises: Using the sensor on the golf cap to acquire the EEG signal in real time, and preprocessing it to obtain the preprocessed EEG signal; Extracting characteristic parameters of the EEG signal according to the preprocessed EEG signal; According to the characteristic parameters of the EEG signal, the EEG concentration is analyzed using the concentration model to obtain the analysis results; According to the analysis results and the preset frequency band, the concentration level is judged to obtain a judgment result; Based on the judgment result, the golf cap reminds the user by vibrating or playing music; All collected EEG signal data are acquired and recorded in real time, and the concentration model is optimized to obtain an optimized model.

2. The golf cap integrated real-time EEG concentration detection method according to claim 1, characterized in that: The sensor on the golf cap is used to obtain the EEG signal in real time and preprocess it to obtain the preprocessed EEG signal, including: Use the sensor integrated into the golf cap to obtain the user's EEG signals in real time; Convert EEG signals into electrical signals and transmit them to the signal acquisition module; The signal acquisition module transmits the received EEG signal to the preprocessing module; The preprocessing module removes high-frequency noise and low-frequency interference from the EEG signal to obtain the EEG signal after filtering; According to the filtered EEG signal, artifact removal processing is performed to obtain a preprocessed EEG signal.

3. The golf cap integrated real-time EEG concentration detection method according to claim 2, characterized in that: According to the preprocessed EEG signal, the characteristic parameters of the EEG signal are extracted, including: According to the preprocessed EEG signal, the preprocessed EEG signal is analyzed using wavelet transform to obtain the energy distribution of the signal at different frequencies and different time points; According to the preprocessed EEG signal, Fourier transform is performed on the EEG signal to convert the EEG signal from the time domain to the frequency domain to obtain the frequency component of the signal; Based on the preprocessed EEG signals, independent component analysis is used to separate the independent components in the EEG signals to obtain independent signals related to specific brain activities; The characteristic parameters of the EEG signal are obtained by integrating the signal's energy distribution at different frequencies and time points, the signal's frequency components, and independent signals related to specific brain activities.

4. The golf cap integrated real-time EEG concentration detection method according to claim 3, characterized in that: According to the characteristic parameters of the EEG signal, the concentration model analyzes the EEG concentration to obtain analysis results, including: According to characteristic parameters of the EEG signal, a preset frequency band in the EEG signal is analyzed to obtain a threshold value of EEG concentration, wherein the preset frequency band includes theta wave, alpha wave, beta wave and gamma wave; Based on the historical data of EEG signals, a concentration model is constructed using a long short-term memory network, and the concentration model is trained using the EEG signal dataset to obtain a trained concentration model; The trained concentration model is used to analyze the frequency bands of the characteristic parameters of the EEG signal to obtain the analysis results.

5. The golf cap integrated real-time EEG concentration detection method according to claim 4, characterized in that: According to the analysis results and the preset frequency band, the concentration level is judged to obtain the judgment results, including: According to the preset frequency band, the concentration level is divided into different levels to obtain the preset concentration level; According to the preset concentration level, the analysis result is compared with the preset frequency band to obtain the judgment result; If the analysis result is within a certain frequency band, it is determined that the user is at a concentration level corresponding to the frequency band; If the analysis result is below all set thresholds, the user is judged to be at the lowest level of concentration.

6. The golf cap integrated real-time EEG concentration detection method according to claim 5, characterized in that: Based on the judgment result, the golf cap reminds the user by vibrating or playing music, including: Set the conditions for triggering reminders based on actual needs and application scenarios; Compare the judgment result with the reminder condition, and if the judgment result meets the reminder condition, remind the user; According to user needs, the vibration or music reminder module in the golf cap is activated to remind the user.

7. The golf cap integrated real-time EEG concentration detection method according to claim 6, characterized in that: Acquire and record all collected EEG signal data in real time, optimize the concentration model to obtain an optimized model, including: Use the sensor on the golf cap to collect the user's EEG data in real time; The real-time collected EEG signal data is stored in a local device or a cloud server, and the data is labeled to obtain labeled data; Extract characteristic parameters related to concentration based on the labeled data; According to the characteristic parameters, the concentration model is optimized to obtain an optimized model; Repeat the above steps to obtain the final optimized model.

8. A golf cap integrated real-time EEG concentration detection system, characterized in that: include: An acquisition module is used to acquire the EEG signal in real time using the sensor on the golf cap, and perform preprocessing to obtain the preprocessed EEG signal; A processing module, used for extracting characteristic parameters of the EEG signal based on the preprocessed EEG signal; According to the characteristic parameters of the EEG signal, the concentration model analyzes the EEG concentration to obtain the analysis results; According to the analysis results and the preset frequency band, the concentration level is judged to obtain a judgment result; Based on the judgment result, the golf cap reminds the user by vibrating or playing music; All collected EEG signal data are acquired and recorded in real time, and the concentration model is optimized to obtain an optimized model.

9. A computing device, characterized in that include: one or more processors; A storage system for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, which, when executed by a processor, implements the method according to any one of claims 1 to 7.

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