Brain-computer interface system, electroencephalogram signal calibration method and device and electronic equipment
Through the integrated brain-computer interface system design, the system can autonomously generate, collect, store, and analyze EEG signals, solving the problems of inaccurate signal calibration and reliance on external equipment for debugging in existing technologies, and improving the reliability and efficiency of the system.
Patent Information
- Application Number
- CN202511611196.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-05
- Publication Date
- 2026-03-03
AI Technical Summary
Existing brain-computer interface systems lack autonomous verification capabilities and rely on external equipment for debugging, resulting in complex operation, low synchronization accuracy, poor portability, and inaccurate signal calibration.
It adopts an integrated design of main control and synchronization module, multi-channel EEG signal generation module, EEG acquisition module, data storage and interaction module and data analysis module, and realizes autonomous calibration and verification by generating, acquiring, storing and analyzing EEG signals.
This improves the reliability and development efficiency of brain-computer interface systems, reduces reliance on external devices, and enhances the accuracy of signal calibration and the applicability of the system.
Smart Images
Figure CN121597006A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of brain-computer interface technology, such as a brain-computer interface system, a brain signal calibration method and device, and an electronic device. Background Technology
[0002] Brain-Computer Interface (BCI) is a real-time communication and control system established between the biological brain and external devices, independent of peripheral nerves and muscles, enabling direct interaction between "thought and machine". The core of the system follows a closed-loop logic of "acquisition-decoding-output-feedback". It captures weak electrical signals generated by the activity of brain neurons through sensors. Non-invasive (such as EEG caps), semi-invasive (subcranial electrodes), or invasive (intracortical implants) devices are adapted to different precision requirements. Then, the EEG signals are converted into machine-recognizable commands through algorithms to filter noise and extract features, ultimately driving devices such as prostheses, wheelchairs, and computers to perform operations. At the same time, tactile or visual feedback forms an interactive closed loop.
[0003] Electroencephalogram (EEG) signals, as the core input of brain-computer interface (BCI) systems, are characterized by their weakness and susceptibility to interference. Current technologies rely on scalp electrodes or invasive sensors for EEG acquisition, which are susceptible to power frequency noise, electromyography (EMG) artifacts, and electrooculography (EOG) interference, leading to distortion of the raw signal. Without verification, this distortion directly reduces the accuracy of subsequent decoding algorithms.
[0004] To achieve calibration of brain-computer interfaces, related technologies disclose a calibrable invasive semiconductor brain-computer interface channel circuit and an invasive semiconductor brain-computer device, comprising: a voltage-to-current conversion unit that converts received EEG signals into corresponding current signals and transmits them to an oscillator conversion unit; the oscillator conversion unit converts the current signals into corresponding oscillation signals and phase signals; a counting unit counts the oscillation signals to obtain the corresponding number of oscillations; at the end of the integration period, a Gray code to binary conversion unit samples the number of oscillations to obtain a coarse quantization code value, and a phase sampling unit samples the phase signal so that a decoding unit decodes the sampling result of the phase sampling unit to obtain a fine quantization code value; and a calibration unit calibrates the coarse quantization code value and the fine quantization code value to obtain a quantization result.
[0005] In the process of implementing the embodiments of this disclosure, at least the following problems were found in the related art: The relevant technologies convert the collected signals into electrical signals and use an oscillator conversion unit for calibration. They cannot simulate and generate EEG signals on their own, and therefore cannot meet diverse testing needs.
[0006] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention
[0007] To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or describe the scope of protection of these embodiments, but rather as a prelude to the detailed description that follows.
[0008] This disclosure provides a brain-computer interface system, an EEG signal calibration method and apparatus, and an electronic device, which solves the problems of brain-computer interface systems lacking autonomous verification functions and relying on external devices for debugging.
[0009] In some embodiments, the brain-computer interface system includes: a main control and synchronization module, used to allocate EEG signal processing tasks to different modules of the brain-computer interface system according to verification and calibration requirements; a multi-channel EEG signal generation module, connected to the main control and synchronization module, used to generate different types of EEG signals and adjust the parameters of the EEG signals; an EEG acquisition module, connected to the multi-channel EEG signal generation module and the main control and synchronization module, used to acquire real EEG signals and EEG signals generated by the multi-channel EEG signal generation module; a data storage and interaction module, connected to the EEG acquisition module, used to store the EEG signals acquired by the EEG acquisition module; and a data analysis module, connected to the data storage and interaction module, used to analyze and process the acquired EEG signals.
[0010] Optionally, the brain-computer interface system further includes a database storing standard calibration data and historical data required by the multi-channel EEG signal generation module.
[0011] In some embodiments, the EEG signal calibration method includes: generating different types of EEG signals through a multi-channel EEG signal generation module and adjusting the parameters of the EEG signals; acquiring and storing the generated EEG signals; and analyzing and processing the stored EEG signals.
[0012] Optionally, different types of EEG signals are generated through a multi-channel EEG signal generation module, including: generating different types of EEG signals using standard calibration data and historical data through the multi-channel EEG signal generation module.
[0013] Optionally, the parameters of the EEG signal can be adjusted, including adjusting parameters such as the frequency, amplitude, and phase of the EEG signal.
[0014] Optionally, the generated EEG signals are acquired and stored, including filtering and amplifying the acquired signals.
[0015] Optionally, the stored EEG signals are analyzed and processed, including: calculating the time-domain features, frequency-domain features, and time-frequency features of the EEG signals; and decoding the EEG signals using a steady-state visual evoked potential recognition algorithm and / or a motor imagery EEG recognition algorithm.
[0016] Optionally, the analysis and processing of the stored EEG signals may further include: when a standard calibration signal is generated using standard calibration data, detecting the acquisition accuracy of the standard calibration signal; verifying the brain signal characteristics and noise data of the standard calibration signal; and feeding back the acquisition accuracy, brain signal characteristics, and noise data as calibration results.
[0017] In some embodiments, the EEG signal calibration device includes a processor and a memory storing program instructions, the processor being configured to execute the EEG signal calibration method as described above when the program instructions are executed.
[0018] In some embodiments, the electronic device includes: an electronic device body; and an EEG signal calibration device, as described above, mounted on the electronic device body.
[0019] The brain-computer interface system, EEG signal calibration method and apparatus, and electronic device provided in this disclosure can achieve the following technical effects: In this embodiment, firstly, the main control and synchronization module, acting as the system control center, scientifically allocates EEG signal processing tasks to other modules based on actual verification and calibration needs, ensuring orderly collaboration among the modules. The multi-channel EEG signal generation module connects to the main control and synchronization module, receiving task instructions and generating different types of EEG signals, such as standard calibration signals and simulated pathological signals. It can also flexibly adjust parameters such as frequency, amplitude, and phase of the generated signals according to testing requirements. The EEG acquisition module connects to both the multi-channel EEG signal generation module and the main control and synchronization module. On one hand, it can acquire real human EEG signals to meet practical application data needs; on the other hand, it can receive simulated EEG signals output by the generation module, providing test input for system autonomous verification. The data storage and interaction module connects to the EEG acquisition module, classifying and storing the acquired real and simulated EEG signals to avoid data loss and provide data support for subsequent analysis. The data analysis module connects to the data storage and interaction module, performing feature extraction, noise analysis, and other processing on the stored two types of EEG signals, providing data interpretation for verification and calibration. This disclosure embodiment realizes the basic process of EEG signal generation, acquisition, storage and analysis through clear connection and functional division between modules, solves the problem of existing EEG devices lacking integrated autonomous verification function and relying on external devices for debugging, and improves device reliability and development efficiency.
[0020] The above general description and the description below are exemplary and illustrative only and are not intended to limit this application. Attached Figure Description
[0021] One or more embodiments are illustrated by way of example with reference to the accompanying drawings. These illustrations and drawings do not constitute a limitation on the embodiments. Elements having the same reference numerals in the drawings are shown as similar elements. The drawings are not to be scaled. And wherein: Figure 1 This is a schematic diagram of a brain-computer interface system provided in an embodiment of this disclosure; Figure 2 This is a schematic diagram of an electroencephalogram (EEG) signal calibration method provided in an embodiment of this disclosure; Figure 3 This is a schematic diagram of an electroencephalogram (EEG) signal calibration device provided in an embodiment of this disclosure. Detailed Implementation
[0022] To provide a more detailed understanding of the features and technical content of the embodiments of this disclosure, the implementation of the embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. The accompanying drawings are for illustrative purposes only and are not intended to limit the embodiments of this disclosure. In the following technical description, for ease of explanation, several details are used to provide a full understanding of the disclosed embodiments. However, one or more embodiments may still be implemented without these details. In other cases, well-known structures and devices may be simplified in their depiction to simplify the drawings.
[0023] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this disclosure described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion.
[0024] Unless otherwise stated, the term "multiple" means two or more.
[0025] In this embodiment of the disclosure, the character " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B means: A or B.
[0026] The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.
[0027] The term "correspondence" can refer to an association or binding relationship. The correspondence between A and B means that there is an association or binding relationship between A and B.
[0028] Brain-computer interface is a novel communication and control channel established between the brain and the external environment, independent of peripheral nerves and muscles, enabling direct interaction between the brain and external devices.
[0029] Electroencephalography (EEG) signals are neural electrical signals generated by the firing of neurons in the human brain and transmitted to the surface of the scalp through the volumetric conductor effect. EEG signal acquisition is a core component of brain-computer interface systems, and its signal quality directly affects system performance. Current EEG acquisition equipment typically relies on external signal generators for verification and calibration, which suffers from problems such as complex operation, low synchronization accuracy, and poor portability.
[0030] Existing technologies for assessing EEG signal quality primarily focus on signal feature analysis, such as evaluating signal quality through power spectral density and artifact detection, but do not address integrated signal generation and verification functions. Furthermore, developers need to rely on data collected from real subjects when debugging post-feedback applications, resulting in low efficiency and limited application scenarios. Therefore, there is an urgent need for a brain-computer interface system that integrates brain signal generation capabilities to achieve autonomous verification, calibration, and efficient debugging.
[0031] Combination Figure 1 , Figure 1 This is a schematic diagram of a brain-computer interface system provided in an embodiment of this disclosure. Figure 1 As shown, the brain-computer interface system includes: a main control and synchronization module 101, a multi-channel EEG signal generation module 102, an EEG acquisition module 103, a data storage and interaction module 104, and a data analysis module 105.
[0032] In this embodiment, firstly, the main control and synchronization module 101, acting as the system control center, scientifically allocates EEG signal processing tasks to other modules based on actual verification and calibration needs, ensuring orderly collaboration among the modules. The multi-channel EEG signal generation module 102 connects to the main control and synchronization module 101, receiving task instructions and generating different types of EEG signals, such as standard calibration signals and simulated pathological signals. It can also flexibly adjust parameters such as frequency, amplitude, and phase of the generated signals according to testing requirements. The EEG acquisition module 103 connects to both the multi-channel EEG signal generation module 102 and the main control and synchronization module 101. On one hand, it can acquire real human EEG signals to meet practical application data requirements; on the other hand, it can receive generated simulated EEG signals, providing test input for system autonomous verification. The data storage and interaction module 104 connects to the EEG acquisition module 103, classifying and storing the acquired real and simulated EEG signals to avoid data loss and provide data support for subsequent analysis. The data analysis module 105 is connected to the data storage and interaction module 104, and performs feature extraction, noise analysis, and other processing on the two types of stored EEG signals to provide data interpretation for verification and calibration. This embodiment of the present disclosure, through clear connections and functional divisions between modules, realizes the basic process of EEG signal generation, acquisition, storage, and analysis, solving the problems of existing EEG devices lacking integrated autonomous verification functions and relying on external devices for debugging, thereby improving device reliability and development efficiency.
[0033] Combination Figure 1 Optionally, the brain-computer interface system further includes a database 106 that stores standard calibration data and historical data required by the multi-channel EEG signal generation module 102.
[0034] In this embodiment, database 106 specifically stores key data required by the multi-channel EEG signal generation module 102, including standard calibration data and historical data. The standard calibration data covers sine wave and square wave signal data of different frequencies (e.g., 5Hz, 10Hz) and amplitudes (e.g., 5μV, 10μV), as well as pre-collected multi-channel EEG data with distinct characteristics such as steady-state visual evoked potentials, providing a precise basis for benchmark signal generation. Historical data includes real EEG data collected during the system's past operation (e.g., motor imagery EEG data from different subjects, clinical pathological EEG data, etc.), providing data support for reproducing signals from real-world scenarios. Database 106 solves the problem of lack of data for the multi-channel EEG signal generation module 102. On the one hand, standard calibration data ensures stable output of signals that meet industry benchmarks, avoiding inaccurate verification results due to signal benchmark deviations and improving system calibration accuracy. On the other hand, historical data allows the system to reproduce real-world signals without relying on real-time acquisition, reducing dependence on real subjects. This is especially suitable for scenarios where rare pathological data is difficult to obtain in real time. At the same time, it provides reusable historical data resources for subsequent algorithm debugging, further expanding the applicability of the system in different testing scenarios and enhancing the reliability and flexibility of the system's autonomous verification and calibration.
[0035] Combination Figure 2 As shown, this disclosure provides a method for calibrating electroencephalogram (EEG) signals, including: S201 generates different types of EEG signals through a multi-channel EEG signal generation module and adjusts the parameters of the EEG signals.
[0036] S202, collect and store the generated EEG signals.
[0037] S203 analyzes and processes the stored EEG signals.
[0038] Using the method provided in this embodiment, a multi-channel EEG signal generation module generates different types of EEG signals, such as standard calibration signals and simulated interference signals. Simultaneously, based on testing requirements, parameters such as frequency (e.g., adjusted to 50Hz for simulating power frequency interference), amplitude (e.g., adjusted to microvolts for simulating weak EEG signals), and phase are specifically adjusted to ensure the generated signals match the specific testing scenario. The EEG acquisition module acquires the simulated EEG signals. During acquisition, preliminary processing is performed based on signal characteristics (e.g., weak signal amplification, noise filtering). The acquired signals are then transmitted to the data storage and interaction module, which classifies and stores the signal data according to a preset format, ensuring data integrity and traceability. The data analysis module retrieves the stored acquired signal data from the data storage and interaction module, performs time-domain feature calculations (e.g., peak value, mean value), frequency-domain feature calculations (e.g., power spectral density), noise analysis, and signal integrity detection, etc., to determine whether the acquired signals meet expectations. This method enables autonomous testing of EEG signals without relying on external signal generators or other equipment, solving the problems of traditional system verification relying on external tools and complex operations, while also improving the efficiency of system debugging and maintenance.
[0039] Optionally, different types of EEG signals are generated through a multi-channel EEG signal generation module, including: generating different types of EEG signals using standard calibration data and historical data through the multi-channel EEG signal generation module.
[0040] The multi-channel EEG signal generation module retrieves the necessary standard calibration data or historical data from the database as the data source for signal generation. When a standard reference signal needs to be generated (e.g., for calibrating the accuracy of the acquisition module), the module reads the standard calibration data (e.g., sine wave data of specific frequency and amplitude) from the database and converts the digital standard data into an analog EEG signal using techniques such as digital-to-analog conversion and signal modulation, ensuring that the generated signal is highly consistent with the standard reference. When it is necessary to reproduce real-world scene signals (e.g., for debugging motor imagery decoding algorithms), the module retrieves historical data from the database (e.g., previously acquired motor imagery EEG data of subjects) and generates an analog signal according to the characteristic parameters of the original signal (e.g., time-domain waveform, frequency-domain distribution) to recreate the real acquisition scene.
[0041] This embodiment generates signals based on standard calibration data, avoiding parameter deviations when the generation module autonomously generates signals, ensuring the accuracy of the reference signal, and improving the reliability of subsequent calibration results. By generating signals based on historical data, there is no need to repeatedly recruit real people to collect data, which is especially suitable for scarce data scenarios such as rare pathological data, reducing data collection costs and time costs. At the same time, it can stably reproduce signals in the same scenario, providing a repeatable test environment for algorithm debugging and improving algorithm optimization efficiency.
[0042] Optionally, the parameters of the EEG signal can be adjusted, including adjusting parameters such as the frequency, amplitude, and phase of the EEG signal.
[0043] Regarding frequency adjustment, the signal frequency can be set to the common frequency band of EEG signals according to the test requirements, or special frequency signals such as power frequency interference (50Hz / 60Hz) can be simulated to test the anti-interference capability of the acquisition module. Regarding amplitude adjustment, the signal amplitude can be controlled within the typical range of real EEG signals. By adjusting the amplitude (such as generating a weak signal of 5μV and a normal signal of 50μV), the acquisition module's ability to capture signals of different intensities can be tested. Regarding phase adjustment, the phase difference between multi-channel signals (such as 0°, 90°, 180°) can be changed to verify the acquisition accuracy of the acquisition module in synchronizing multi-channel signals.
[0044] By clearly defining the specific dimensions and ranges of parameter adjustments, the signal generation process becomes more targeted. Corresponding parameters can be adjusted according to different verification objectives to generate signals that conform to the test scenario, thus improving the accuracy of verification. The system can simulate various complex signal scenarios (such as weak EEG signals with power frequency interference and multi-channel phase shift signals), covering edge scenarios that are difficult to test with traditional systems. This further enhances the system's adaptability to different application needs and ensures the comprehensiveness of subsequent calibration results.
[0045] Optionally, the generated EEG signals are acquired and stored, including filtering and amplifying the acquired signals.
[0046] In this embodiment, a filtering operation is first performed, using techniques such as bandpass filtering to remove interference components (such as power frequency noise and EMG artifacts) from the signal, preserving the effective EEG signal frequency band (e.g., 0.5-30Hz) to avoid interference signals affecting subsequent analysis results. For the weak EEG signal after filtering (typically at the microvolt level), amplification processing is performed, using amplifiers or other devices to amplify the signal amplitude to a range suitable for subsequent storage and analysis (e.g., at the millivolt level), ensuring that signal features are not obscured. This embodiment, through filtering and amplification preprocessing steps, solves the problems of noise pollution and weak signals that may occur during analog signal transmission and acquisition, ensuring high-quality stored signal data and providing reliable data input for subsequent data analysis modules to accurately extract features and determine calibration results.
[0047] Optionally, the stored EEG signals are analyzed and processed, including: calculating the time-domain features, frequency-domain features, and time-frequency features of the EEG signals; and decoding the EEG signals using a steady-state visual evoked potential recognition algorithm and / or a motor imagery EEG recognition algorithm.
[0048] In this embodiment, basic feature calculations are performed on the stored EEG signals. Time-domain feature calculations include the extraction of parameters such as signal peak value, mean, variance, and waveform duration to determine the time-domain stability of the signal. Frequency-domain feature calculations obtain information such as the power spectral density and characteristic frequency distribution of the signal through methods such as Fourier transform to analyze the frequency composition of the signal. Time-frequency feature calculations use techniques such as wavelet transform to capture the variation patterns of the signal in different time and frequency dimensions, which is suitable for the analysis of non-stationary EEG signals.
[0049] This disclosure embodiment decodes EEG signals, and the system supports embedding external decoding algorithms, including a steady-state visual evoked potential recognition algorithm and a motor imagery EEG recognition algorithm. The steady-state visual evoked potential recognition algorithm can identify signals containing this feature and determine whether there is a visual evoked response of a specific frequency in the signal; the motor imagery EEG recognition algorithm can classify and identify simulated motor imagery signals (such as imagined raising an arm or foot) and output the recognition results.
[0050] Optionally, the analysis and processing of the stored EEG signals may further include: when a standard calibration signal is generated using standard calibration data, detecting the acquisition accuracy of the standard calibration signal; verifying the brain signal characteristics and noise data of the standard calibration signal; and feeding back the acquisition accuracy, brain signal characteristics, and noise data as calibration results.
[0051] In this embodiment, firstly, the acquisition accuracy of the standard calibration signal is detected by comparing the acquired signal with the corresponding standard calibration data in the database and calculating the deviation values of the two in parameters such as frequency, amplitude, and phase (e.g., whether the amplitude deviation is less than 1μV and the frequency deviation is less than 0.1Hz) to determine whether the acquisition module accurately captures the standard signal. Secondly, the brain signal characteristics and noise data of the standard calibration signal are examined. The brain signal characteristic examination needs to confirm whether the core characteristics in the standard data (such as the specific frequency peak of steady-state visual evoked potentials) are completely preserved in the acquired signal to ensure that the characteristics are not lost during the acquisition process. The noise data is analyzed by calculating parameters such as signal-to-noise ratio and noise power to evaluate the noise level introduced during the acquisition process and determine the anti-interference capability of the acquisition module. Finally, the above acquisition accuracy deviation value, brain signal characteristic integrity result, and noise data evaluation result are integrated into a calibration result and fed back to the main control and synchronization module to provide the main control module with a decision-making basis for determining whether recalibration and parameter adjustment are needed. This ensures that the system can promptly detect accuracy deviations or insufficient anti-interference issues in the acquisition module, thereby improving equipment performance through subsequent adjustments and guaranteeing the reliability of the brain-computer interface system in practical applications. It also solves the problems of traditional systems having no quantitative basis for calibration results and untimely feedback.
[0052] Combination Figure 3As shown, this embodiment of the disclosure provides an electroencephalogram (EEG) signal calibration device 30, including a processor 300 and a memory 301. Optionally, the device 30 may further include a communication interface 302 and a bus 303. The processor 300, communication interface 302, and memory 301 can communicate with each other via the bus 303. The communication interface 302 can be used for information transmission. The processor 300 can call logical instructions in the memory 301 to execute the EEG signal calibration method of the above embodiment.
[0053] Furthermore, the logic instructions in the aforementioned memory 301 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.
[0054] The memory 301, as a computer-readable storage medium, can be used to store software programs and computer-executable programs, such as program instructions / modules corresponding to the methods in the embodiments of this disclosure. The processor 300 executes functional applications and data processing by running the program instructions / modules stored in the memory 301, thereby implementing the EEG signal calibration method in the above embodiments.
[0055] The memory 301 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the terminal device. Furthermore, the memory 301 may include high-speed random access memory and may also include non-volatile memory.
[0056] This disclosure provides an electronic device, including a product body and the aforementioned electroencephalogram (EEG) signal calibration device. The EEG signal calibration device is installed on the electronic device body. The installation relationship described herein is not limited to placement within the electronic device body, but also includes installation connections with other components of the electronic device, including but not limited to physical connections, electrical connections, or signal transmission connections. Those skilled in the art will understand that the EEG signal calibration device can be adapted to suitable electronic device bodies to achieve other feasible embodiments.
[0057] The technical solutions of this disclosure can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes one or more instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in this disclosure. The aforementioned storage medium can be a non-transitory storage medium, such as a USB flash drive, external hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, etc., and other media capable of storing program code.
[0058] The foregoing description and accompanying drawings fully illustrate embodiments of this disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the order of operation may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the terminology used in this application is for describing embodiments only and is not intended to limit the claims. As used in the description of embodiments and claims, the singular forms “a,” “an,” and “the” are intended to equally include the plural forms unless the context clearly indicates otherwise. Similarly, the term “and / or” as used in this application means including one or more of the associated listed items and all possible combinations thereof. Additionally, when used in this application, the term "comprise" and its variations "comprises" and / or "comprising" refer to the presence of stated features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof. Without further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, or apparatus that includes said element. In this document, each embodiment may focus on the differences from other embodiments, and similar or identical parts between embodiments can be referred to mutually. For methods, products, etc., disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, the relevant parts can be referred to the description of the method section.
[0059] Those skilled in the art will recognize that the units and algorithm steps of the various examples 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 implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this disclosure. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.
[0060] The methods and products disclosed in the embodiments herein (including but not limited to devices and equipment) can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units may be merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to implement this embodiment according to actual needs. In addition, the functional units in the embodiments of this disclosure 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.
[0061] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.
Claims
1. A brain-computer interface system, characterized in that, include: The main control and synchronization module is used to distribute EEG signal processing tasks to different modules of the brain-computer interface system according to verification and calibration requirements. A multi-channel EEG signal generation module, connected to the main control and synchronization module, is used to generate different types of EEG signals and adjust the parameters of the EEG signals; The EEG acquisition module, connected to the multi-channel EEG signal generation module and the main control and synchronization module, is used to acquire real EEG signals as well as EEG signals generated by the multi-channel EEG signal generation module. The data storage and interaction module is connected to the EEG acquisition module and is used to store the EEG signals acquired by the EEG acquisition module. The data analysis module, connected to the data storage and interaction module, is used to analyze and process the collected EEG signals.
2. The brain-computer interface system according to claim 1, characterized in that, Also includes: The database stores standard calibration data and historical data required by the multi-channel EEG signal generation module.
3. A method for calibrating electroencephalogram (EEG) signals, characterized in that, include: Different types of EEG signals are generated through a multi-channel EEG signal generation module, and the parameters of the EEG signals are adjusted. Collect and store the generated electroencephalogram (EEG) signals; The stored electroencephalogram (EEG) signals are analyzed and processed.
4. The method according to claim 3, characterized in that, Different types of EEG signals are generated through a multi-channel EEG signal generation module, including: The multi-channel EEG signal generation module generates different types of EEG signals using standard calibration data and historical data.
5. The method according to claim 3, characterized in that, Adjusting parameters of EEG signals, including: The frequency, amplitude, phase, and other parameters of the EEG signal are adjusted.
6. The method according to claim 3, characterized in that, The generated electroencephalogram (EEG) signals are collected and stored, including: The acquired signals are filtered and amplified.
7. The method according to any one of claims 3 to 6, characterized in that, The stored electroencephalogram (EEG) signals are analyzed and processed, including: The time-domain, frequency-domain, and time-frequency characteristics of EEG signals are calculated and processed. EEG signals are decoded using steady-state visual evoked potential recognition algorithms and / or motor imagery EEG recognition algorithms.
8. The method according to any one of claims 4 to 6, characterized in that, The analysis and processing of stored electroencephalogram (EEG) signals also includes: When a standard calibration signal is generated using standard calibration data, the acquisition accuracy of the standard calibration signal is tested. The brain signal characteristics and noise data of the calibration signal were examined. The acquisition accuracy, brain signal characteristics, and noise data are used as feedback for calibration results.
9. A brainwave signal calibration device, comprising a processor and a memory storing program instructions, characterized in that, The processor is configured to perform the EEG signal calibration method as described in any one of claims 3 to 8 when running the program instructions.
10. An electronic device, characterized in that, include: The electronic device itself; The EEG signal calibration device as described in claim 9 is installed on the electronic device body.