Biological signal acquisition method for brain-computer interface
By dynamically adjusting the sampling rate and signal channels of the brain-computer interface and optimizing the sampling parameters with machine learning, the problems of high power consumption and low efficiency in traditional brain-computer interfaces are solved, and efficient and low-cost biological signal acquisition is achieved, which is suitable for multi-field applications.
Patent Information
- Application Number
- CN202510472469.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-16
- Publication Date
- 2025-07-18
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
In traditional brain-computer interface technology, the high sampling rate of high-density electrode arrays leads to surge in power consumption, high data redundancy, and lacks dynamic perception capabilities. It is impossible to prioritize the allocation of sampling resources for task requirements, resulting in inefficient key signal capture.
The signal acquisition unit drives the algorithm to control the analog signal switch, dynamically adjust the equivalent sampling rate of the logic sampling channel, combine machine learning to predict the demand signal, optimize sampling parameters, realize multi-channel signal acquisition, and reduce costs.
It improves the acquisition accuracy and efficiency of key signals, reduces system power consumption, supports large-scale channel expansion, adapts to variable application scenarios, and meets real-time interaction needs.
Smart Images
Figure CN120335612A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the technical field of biological signal acquisition, and in particular to a biological signal acquisition method for a brain-computer interface. Background Art
[0002] In the field of brain-computer interface technology, efficient acquisition of biological signals (such as electroencephalogram, neural signals) is the key to achieving precise interaction and disease diagnosis. Traditional signal acquisition systems mostly adopt fixed sampling rates or sequential channel switching strategies, which have significant limitations: First, large-array electrodes (such as thousands to tens of thousands of electrode points) require high sampling rates to cover all channels, resulting in a sharp increase in system power consumption, high data redundancy, and key signals being easily submerged by noise; Second, existing technologies expand logical channels through switch gating, but lack the ability to dynamically perceive signal characteristics and cannot preferentially allocate sampling resources according to task requirements, resulting in low capture efficiency of key signals (such as epileptic precursor waves, neural spikes related to movement intentions); Third, the hardware architecture is rigid and it is difficult to adapt to variable application scenarios through parameter linkage (such as sampling rate, gain, filtering).
[0003] Therefore, there is an urgent need for a biological signal acquisition method that can dynamically optimize sampling resources, accurately capture required signals, and support large-scale channel expansion to improve the practicability and reliability of brain-computer interface systems. Summary of the Invention
[0004] To solve the above problems, this application provides a biological signal acquisition method for a brain-computer interface, which is controlled by a signal acquisition unit driving algorithm, and realizes the acquisition of multiple signals by controlling the on and off of analog signal switches, reducing the sampling cost. The technical solution is as follows:
[0005] This application provides a biological signal acquisition method for a brain-computer interface, including the following steps:
[0006] S1: Collect biological signals of n electrodes through m signal acquisition units. Each signal acquisition unit includes an ADC sampling channel, N analog signal switches, N sampling electrodes, and a shared operational amplifier. The analog signal switches are gated according to a preset timing sequence to time-division multiplex the ADC channel to obtain initial acquisition data;
[0007] S2: According to a preset signal acquisition task, input the initial acquisition data into an algorithm processing system to predict the logical sampling channel where the required data appears;
[0008] S3: Dynamically adjust the equivalent sampling rate w of the logical sampling channel, and allocate a higher w value to the predicted logical sampling channel for targeted acquisition;
[0009] S4: Reconfigure the sampling parameters based on the adjusted w value and output the biological signal data after optimizing the sampling efficiency of the channel.
[0010] For example, in the biological signal acquisition method for a brain-computer interface provided in an embodiment, the signal acquisition unit includes a programmable gain amplifier (PGA), and the PGA is connected in series with the common operational amplifier for adaptively adjusting the signal gain.
[0011] For example, in the biological signal acquisition method for a brain-computer interface provided in an embodiment, the analog signal switch is a MOS transistor array, and each MOS transistor is connected to a sampling electrode and independently gated through a control circuit.
[0012] For example, in the biological signal acquisition method for a brain-computer interface provided in an embodiment, in step S3, the adjustment of the equivalent sampling rate w is based on historical acquisition data and task priorities, and a machine learning algorithm is used for prediction.
[0013] For example, in the biological signal acquisition method for a brain-computer interface provided in an embodiment, the signal acquisition unit adopts a stacked integrated circuit design, supporting the cascading of m units to expand to m×n logical sampling channels.
[0014] For example, in the biological signal acquisition method for a brain-computer interface provided in an embodiment, the non-inverting input terminal of the common operational amplifier is connected to a sampling capacitor, and the capacitance value of the sampling capacitor is dynamically configured according to the signal frequency band.
[0015] For example, in the biological signal acquisition method for a brain-computer interface provided in an embodiment, in step S4, the sampling parameters include the ADC resolution, gain range, and filter cut-off frequency, which are synchronously adjusted according to the w value.
[0016] For example, in the biological signal acquisition method for a brain-computer interface provided in an embodiment, the method is applied to an epilepsy warning system, and a high w value is preferentially assigned to the logical channels corresponding to abnormal electroencephalogram activity regions.
[0017] For example, in the biological signal acquisition method for a brain-computer interface provided in an embodiment, the algorithm processing system is embedded in an FPGA chip to realize real-time synchronous control of w value adjustment and signal acquisition.
[0018] For example, in the biological signal acquisition method for a brain-computer interface provided in an embodiment, the method aggregates and analyzes the acquisition data of multiple users through a cloud platform to optimize the global w value allocation strategy.
[0019] The beneficial effects brought by a biological signal acquisition method for a brain-computer interface provided in some embodiments of this application are:
[0020] (1) Dynamic resource allocation and channel prediction: Based on historical data and task requirements, predict high-value signal channels, dynamically adjust the equivalent sampling rate (w value), increase the sampling rate of critical channels, reduce the acquisition of invalid data, and improve the overall system efficiency.
[0021] (2) Parameter linkage optimization: Bind and adjust the sampling rate w with the ADC resolution, gain setting, and filtering parameters to achieve coordinated optimization of the entire system, improve signal fidelity, and reduce power consumption.
[0022] (3) Hardware scalability: Adopt a stacked acquisition unit design, support flexible expansion of m×n logical channels, reduce the cost per channel, and adapt to the requirements from laboratory research to clinical-scale brain-computer interface arrays.
[0023] (4) Breakthrough in real-time performance and accuracy: The embedded FPGA algorithm processing system achieves latency control at the 10ms level, improves the signal capture accuracy in scenarios such as epilepsy warning, and meets the requirements of high-real-time interaction.
[0024] (5) Cross-scenario adaptation ability: Optimize the global sampling strategy through cloud collaborative learning, support personalized electroencephalogram feature analysis, improve the model prediction accuracy, and promote the implementation of brain-computer interfaces in multiple fields such as medical and consumer electronics.
[0025] This application breaks through the bottleneck of "blind sampling and resource waste" in traditional technologies, provides an efficient, low-cost, and high-precision solution for high-density bio-signal acquisition, and has significant academic value and industrialization potential. BRIEF DESCRIPTION OF THE DRAWINGS
[0026] To more clearly illustrate the technical solutions in the embodiments of this specification or the prior art, the following will briefly introduce the drawings required for the embodiments. Obviously, the drawings in the following description are only some embodiments of this application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.
[0027] Figure 1 It is the flowchart of the bio-signal acquisition method for the brain-computer interface of this application;
[0028] Figure 2 It is the circuit schematic diagram of the bio-signal acquisition method for the brain-computer interface of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present application in combination with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.
[0030] Unless otherwise defined, the technical terms or scientific terms used in this disclosure shall have the ordinary meanings understood by those of ordinary skill in the art to which this disclosure belongs. The terms "first", "second" and similar words used in this disclosure do not denote any order, quantity or importance, but are only used to distinguish different components. Words such as "including" or "comprising" mean that the elements or items appearing before this word cover the elements or items listed after this word and their equivalents, without excluding other elements or items. Words such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Upper", "lower", "left", "right", etc. are only used to represent relative position relationships, and when the absolute position of the object being described changes, the relative position relationship may also change accordingly.
[0031] The present application provides a method for collecting biological signals for a brain-computer interface, as Figure 1-2 shown, including the following steps:
[0032] S1: Collect biological signals of n electrodes through m acquisition signal units. Each acquisition signal unit includes an ADC sampling channel, N analog signal switches, N sampling electrodes and a shared operational amplifier. The analog signal switches are gated according to a preset time sequence to time-division multiplex the ADC channels to obtain initial acquisition data;
[0033] Among them, the acquisition signal unit includes a programmable gain amplifier (PGA). The PGA is connected in series with the shared operational amplifier and is used to adaptively adjust the signal gain, enhance the amplification accuracy of weak bio-signals, improve the signal-to-noise ratio, and adapt to the acquisition requirements of electroencephalogram (EEG) signals with different amplitudes. The analog signal switch is a MOS transistor array. Each MOS transistor is connected to a sampling electrode and is independently gated through a control circuit to achieve high-speed and low-noise signal switching. The channel switching delay is less than 1 μs, which is suitable for the acquisition of high-frequency bio-signals. In addition, the analog signal switch includes, but is not limited to, electromagnetic relays, multiplex switches, and other signal selection components. The acquisition signal unit adopts a stacked integrated circuit design, supports the cascading of m units to expand to m×n logical sampling channels, has a compact hardware structure, reduces the cost of channel expansion, and is suitable for large-scale brain-computer interface arrays. The non-inverting input terminal of the shared operational amplifier is connected to a sampling capacitor. The capacitance value of the sampling capacitor is dynamically configured according to the signal frequency band to suppress high-frequency interference and improve signal fidelity, especially suitable for the acquisition of high-frequency neural spike signals.
[0034] S2: According to the preset signal acquisition task, input the initial acquisition data into the algorithm processing system to predict the logical sampling channels where the required data appears.
[0035] S3: Dynamically adjust the equivalent sampling rate w of the logical sampling channels, and assign a higher w value to the predicted logical sampling channels for targeted acquisition. Among them, the equivalent sampling rate is the number of samplings per unit time.
[0036] In step S3, the adjustment of the equivalent sampling rate w is based on historical acquisition data and task priorities, and relevant algorithms are used for prediction. Preferably, it is a machine learning algorithm, and its weight is adjusted by this algorithm to achieve a high w value at the target signal, dynamically optimize resource allocation, improve the sampling rate of key channels, and improve data effectiveness.
[0037] One example of the specific prediction method is to use the above algorithm to predict the next EEG signal, and compare the signal and its position with the requirements of the target signal. The w value with a higher degree of fit with the target signal requirements is higher. The requirement of this algorithm is that given the target signal, the higher the probability of the target signal appearing in the actual brain region, the higher its w value.
[0038] S4: Reconfigure the sampling parameters based on the adjusted w value, and output the bio-signal data after optimizing the sampling efficiency of the channels.
[0039] Among them, in step S4, the sampling parameters include ADC resolution, gain setting, and filter cut-off frequency, which are synchronously adjusted according to the w value to achieve parameter linkage optimization, improve the system response speed, and avoid resource waste caused by oversampling.
[0040] The bio-signal acquisition method for a brain-computer interface of the present application significantly improves the acquisition accuracy of key signals, enhances the logical channel expansion ability, and reduces the system power consumption by predicting the required data channels and dynamically adjusting the sampling rate.
[0041] For example, in the bio-signal acquisition method for a brain-computer interface provided in an embodiment, the method is applied to an epilepsy warning system, and high w values are preferentially assigned to the logical channels corresponding to the abnormal electroencephalogram activity regions, so as to capture the precursor signals of epileptic seizures in real time and improve the warning accuracy rate.
[0042] For example, in the bio-signal acquisition method for a brain-computer interface provided in an embodiment, the algorithm processing system is embedded in an FPGA chip to achieve real-time synchronous control of w value adjustment and signal acquisition, accelerate the execution of the hardware algorithm, reduce the data processing delay, and meet the requirements of real-time brain-computer interaction.
[0043] For example, in the bio-signal acquisition method for a brain-computer interface provided in an embodiment, the method aggregates and analyzes the collected data of multiple users through a cloud platform, optimizes the global w value allocation strategy, supports the learning of group electroencephalogram characteristics, improves the model prediction accuracy, and promotes the application of personalized brain-computer interfaces.
[0044] Although the embodiments of the present application have been disclosed as above, they are not limited to the applications listed in the specification and embodiments. It can be fully applied to various fields suitable for the present application. For those familiar with the field, additional modifications can be easily made. Therefore, without departing from the general concept defined by the claims and the equivalent scope, the present application is not limited to the specific details and the illustrated and described examples here.
Claims
1. A biological signal acquisition method for a brain-computer interface, characterized in that, Including the following steps: S1: Collect the bio-signals of n electrodes through m acquisition signal units. Each acquisition signal unit includes an ADC sampling channel, N analog signal switches, N sampling electrodes, and a shared operational amplifier. The analog signal switches are gated according to a preset timing sequence to multiplex the ADC channels in a time-division manner, obtaining the initial acquisition data; S2: According to the preset signal acquisition task, input the initial acquisition data into the algorithm processing system to predict the logical sampling channels where the required data appears; S3: Dynamically adjust the equivalent sampling rate w of the logical sampling channels, and assign a higher w value to the predicted logical sampling channels for targeted acquisition; S4: Reconfigure the sampling parameters based on the adjusted w value, and output the bio-signal data after optimizing the sampling efficiency of the channels.
2. The biological signal acquisition method for a brain-computer interface according to claim 1, wherein The acquisition signal unit includes a programmable gain amplifier PGA, and the PGA is connected in series with the shared operational amplifier for adaptively adjusting the signal gain.
3. The method for biometric signal acquisition for a brain-computer interface according to claim 1, wherein The analog signal switch is a MOS transistor array, each MOS transistor is connected to a sampling electrode, and is independently gated through a control circuit.
4. The biological signal acquisition method for a brain-computer interface according to claim 1, wherein, In step S3, the adjustment of the equivalent sampling rate w is based on historical acquisition data and task priorities, and is predicted using a machine learning algorithm.
5. The biological signal acquisition method for a brain-computer interface according to claim 1, characterized in that, The acquisition signal unit adopts a stacked integrated circuit design, supporting the cascading of m units to expand to m×n logical sampling channels.
6. The biological signal acquisition method for a brain-computer interface according to claim 1, characterized in that, The non-inverting input terminal of the shared operational amplifier is connected to a sampling capacitor, and the capacitance value of the sampling capacitor is dynamically configured according to the signal frequency band.
7. The biological signal acquisition method for a brain-computer interface according to claim 1, characterized in that, In step S4, the sampling parameters include ADC resolution, gain setting, and filter cut-off frequency, and are synchronously adjusted according to the w value.
8. The biological signal acquisition method for a brain-computer interface according to claim 1, characterized in that, The method is applied to an epilepsy warning system, and a high w value is preferentially assigned to the logical channels corresponding to the abnormal electroencephalogram activity areas.
9. The biological signal acquisition method for a brain-computer interface according to claim 1, wherein The algorithm processing system is embedded in an FPGA chip to achieve real-time synchronous control of the w value adjustment and signal acquisition.
10. The biological signal acquisition method for a brain-computer interface according to claim 1, wherein The method aggregates and analyzes the acquisition data of multiple users through a cloud platform to optimize the global w value allocation strategy.