Low-channel electroencephalogram amplification circuit and brain-computer interface equipment for post-ear electroencephalogram signals

Abstract

The invention relates to the technical field of wireless communication networks, and provides a low-channel electroencephalogram amplification circuit and brain-computer interface equipment oriented to an after-ear electroencephalogram signal, and the low-channel electroencephalogram amplification circuit comprises a dry electrode sensing module which is used for collecting an electroencephalogram signal; the front active amplification module comprises an operational amplifier circuit with high input impedance; the anti-aliasing filtering module is formed by cascading a multi-order passive filtering network and an active filtering circuit, is connected with the output end of the front active amplification module and is used for filtering high-frequency interference signals; the right leg driving feedback module comprises a programmable impedance adjusting circuit and a current limiting protection circuit, the right leg driving feedback module and the front active amplification module form a closed-loop feedback loop, and the output end of the right leg driving feedback module and the human body form a current loop through a protection resistor; the analog-to-digital conversion module adopts an integrated low-power-consumption analog front-end chip; a wireless transmission module; and the power supply management module provides independent power supply for the analog front-end chip, the operational amplifier circuit and the wireless transmission module.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communication networks, and in particular relates to a low-channel electroencephalogram (EEG) amplification circuit and a brain-computer interface device for post-auricular EEG signals. Background Art

[0002] The human body's EEG signal is a very weak electrophysiological signal at the microvolt level, and is accompanied by a lot of noise. In order to improve the signal-to-noise ratio of the collected EEG signal, the current conventional practice of medical or scientific research-grade EEG equipment is to use a multi-channel EEG amplifier collector covering the entire brain to increase the collection area, and use wet electrodes such as conductive paste to enhance the contact between the electrode and the skin and reduce the contact impedance.

[0003] Although multi-channel acquisition can improve the signal-to-noise ratio of the collected signal to a certain extent, it will also lead to a sharp increase in equipment costs and increase the size of the equipment, which is not conducive to miniaturization and integration, and is difficult to promote and apply on wearable devices.

[0004] The whole-brain EEG signal acquisition device using wet electrodes can ensure good contact between the electrodes and the scalp, reduce contact impedance, obtain high-quality, high-fidelity EEG signals, effectively reduce signal attenuation and the mixing of external interference, and can clearly capture the characteristics of different frequency bands. However, the preparation work is cumbersome, it cannot be used for a long time, and the professional requirements for operators are high, which makes it impossible to use it for daily EEG signal monitoring and acquisition. Therefore, this type of multi-channel EEG sensor device using wet electrodes is not suitable for consumer wearable electronic products. Summary of the invention

[0005] The present application provides a low-channel EEG amplification circuit and brain-computer interface device for behind-the-ear EEG signals, aiming to solve the existing whole-brain EEG signal acquisition device using wet electrodes. Although it can ensure good contact between the electrodes and the scalp, reduce contact impedance, obtain high-quality, high-fidelity EEG signals, effectively reduce signal attenuation and the mixing of external interference, and can clearly capture the characteristics of different frequency bands, it has cumbersome preparation work, cannot be used for a long time, and has high professional requirements for operators, which makes it impossible to use it for daily EEG signal monitoring and acquisition. Therefore, this type of multi-channel EEG sensor device using wet electrodes is not suitable for consumer wearable electronic products.

[0006] In a first aspect, an embodiment of the present application provides a low-channel EEG amplification circuit for post-auricular EEG signals, comprising:

[0007] The dry electrode sensing module consists of two differential electrodes and is placed in the hairless area behind the human ear to collect EEG signals.

[0008] A front active amplifier module includes an operational amplifier circuit with high input impedance, the operational amplifier circuit is directly coupled with the dry electrode sensor module to form a differential signal amplification structure with a common mode rejection ratio of at least 120 dB; the dry electrode sensor module and the front active amplifier module are electrically connected through an ESD protection circuit;

[0009] The anti-aliasing filter module is composed of a multi-order passive filter network and an active filter circuit in cascade, and is connected to the output end of the pre-active amplifier module to filter out high-frequency interference signals; the output impedance of the anti-aliasing filter module is lower than 500Ω;

[0010] A right leg driving feedback module, comprising a programmable impedance adjustment circuit and a current limiting protection circuit, forms a closed-loop feedback loop with the front active amplifier module, and an output end of the right leg driving feedback module forms a current loop with the human body through a protection resistor;

[0011] The analog-to-digital conversion module uses an integrated low-power analog front-end chip, including a dual-channel differential input interface, a programmable gain amplifier, and a 24-bit high-precision ADC converter;

[0012] The wireless transmission module integrates a low-power Bluetooth communication chip and is connected to the analog-to-digital conversion module through a digital interface;

[0013] The power management module includes a multi-stage low-noise LDO power supply circuit, which provides independent power supply to the analog front-end chip, operational amplifier circuit and wireless transmission module respectively.

[0014] In some embodiments, the dual-channel differential input interface is respectively connected to the two output ends of the anti-aliasing filtering module; wherein, the negative input end of the dual-channel differential input interface is commonly connected as a common reference node, and the positive input end receives the EEG signals from the two differential electrodes respectively; the analog front-end chip has a built-in programmable gain amplifier and a 24-bit high-precision ADC converter, and is connected to the main control chip through a digital interface to transmit the converted digital signal.

[0015] Exemplarily, the analog front-end chip integrates a continuous disconnection detection circuit and a test signal generator, and the corresponding common mode rejection ratio is not less than 120 dB and the input reference noise is less than 5 μVpp.

[0016] In some embodiments, the operational amplifier circuit of the front active amplifier module adopts a common-mode input amplifier topology, and the corresponding input end is directly coupled to the dry electrode sensor module through a resistor network to form an input impedance greater than 10 GΩ.

[0017] In some embodiments, the passive filtering network of the anti-aliasing filtering module is composed of at least three resistor-capacitor devices to form a multi-order low-pass filter, and the corresponding cut-off frequency is set to 50Hz-150Hz to match the EEG signal frequency band; the active filtering circuit is composed of an operational amplifier and resistor-capacitor devices to form a second-order or higher low-pass filtering topology.

[0018] In some embodiments, the programmable impedance adjustment circuit of the right leg drive feedback module includes a parallel resistor and capacitor network, which dynamically controls the feedback bandwidth by adjusting the RC time constant; the current limiting protection circuit is connected in series between the right leg drive output terminal and the human body reference electrode, and forms a bidirectional current limiting structure with the ESD protection diode.

[0019] In some embodiments, the low-noise LDO power supply circuit of the power management module independently powers the analog power pin and the digital power pin of the analog front-end chip, and the power ripple suppression ratio of each corresponding LDO chip is higher than 80dB and an enable control pin is integrated to achieve time-sharing power supply.

[0020] In some embodiments, the Bluetooth communication chip of the wireless transmission module integrates a DC-DC voltage converter, its RF transmission peak current is lower than 10mA and the receiving sensitivity is better than -90dBm, and signal transmission and reception are achieved through an onboard inverted F-type antenna and an impedance matching circuit.

[0021] In some embodiments, the EEG amplification circuit is integrated into a module through a four-layer PCB board; wherein the corresponding analog signal routing and digital signal routing adopt a layered isolation layout, and the dry electrode contact area adopts an immersion gold process to reduce contact impedance.

[0022] In a second aspect, the present application provides a brain-computer interface device, including a low-channel EEG amplification circuit for behind-the-ear EEG signals provided by any embodiment of the present application.

[0023] This low-channel EEG amplifier circuit for post-ear EEG signals uses two differential electrodes, which are placed in the hairless area behind the human ear to directly collect EEG signals. No auxiliary materials such as conductive glue are required, and it is a dry electrode design, avoiding the tedious preparation of wet electrodes. The front active amplifier module contains a high input impedance operational amplifier circuit, which is directly coupled with the dry electrode to form a differential signal amplification structure with a common mode rejection ratio (CMRR) ≥ 120dB, effectively suppressing common mode interference (such as environmental noise, power frequency interference, etc.). The dry electrode and the amplifier module are connected through an ESD protection circuit to improve the circuit's anti-static ability and protect the front-end sensitive components. The anti-aliasing filter module is composed of a multi-order passive filter network and an active filter circuit cascaded to filter out high-frequency interference signals (such as electromagnetic noise, high-frequency harmonics, etc.) to avoid aliasing distortion during signal sampling. The output impedance is designed to be lower than 500Ω, matching the input requirements of the subsequent analog-to-digital conversion module to ensure signal transmission stability. The right leg drive feedback module integrates a programmable impedance adjustment circuit and a current limiting protection circuit, forming a closed-loop feedback loop with the preamplifier module to actively suppress human common-mode noise (such as surface potential drift). The output end forms a current loop with the human body through a protective resistor, which reduces common-mode interference while ensuring user safety and avoiding excessive current injection. The analog-to-digital conversion module uses an integrated low-power analog front-end chip with: a dual-channel differential input interface to match the front-end differential signal output; a programmable gain amplifier (PGA) that supports dynamic adjustment of signal gain to adapt to EEG signals of different amplitudes; a 24-bit high-precision ADC converter to ensure high-resolution digitization of EEG signals and retain weak signal characteristics. The wireless transmission module integrates a low-power Bluetooth communication chip and is connected to the analog-to-digital conversion module through a digital interface to achieve wireless transmission of EEG signals and support long-life wearable device applications. The power management module uses a multi-stage low-noise LDO (low-dropout linear regulator) power supply circuit to provide independent isolated power supply for the analog front-end chip, operational amplifier and wireless module, reduce power supply noise coupling between modules, and improve the overall noise performance of the system.

[0024] The provided circuit has the following beneficial effects:

[0025] 1. Solve the pain point of wet electrodes and adapt to daily wear:

[0026] Dry electrode design: no conductive gel is required, avoiding tedious electrode preparation, supporting quick wearing and long-term use, reducing the requirements for professional operation, and is suitable for consumer wearable devices (such as smart headphones, head-mounted devices, etc.).

[0027] Collecting data in the hairless area behind the ear: avoiding interference from scalp hair, ensuring stable electrode contact, reducing contact impedance problems caused by hair, and improving the convenience of signal collection.

[0028] 2. High anti-interference ability and signal quality:

[0029] High CMRR differential amplification (≥120dB) and multi-stage filtering (anti-aliasing filtering + right leg drive feedback): effectively suppress environmental noise, power frequency interference and human common mode noise, ensure high-fidelity amplification of EEG signals, and clearly capture the characteristics of different frequency bands (such as α, β, γ waves, etc.).

[0030] 24-bit high-precision ADC: retains the details of weak EEG signals, avoids quantization noise, and meets the high-precision requirements of physiological signal analysis.

[0031] 3. Low power consumption and integrated design:

[0032] Low-power Bluetooth and multi-level LDO power supply: reduce system energy consumption, adapt to battery-powered wearable devices, and support long-term monitoring; independent power supply design reduces noise crosstalk between modules and improves stability.

[0033] Low-channel-count simplified structure: Different from traditional multi-channel whole-brain acquisition equipment, the low-channel design focuses on the specific area behind the ear, taking into account both performance and portability, and reducing hardware cost and complexity.

[0034] 4. Security and reliability:

[0035] ESD protection and current limiting protection: Improve circuit anti-static ability and human safety protection to prevent high voltage static electricity or abnormal current from damaging users and equipment.

[0036] Programmable impedance adjustment: adapt to the physiological impedance differences of different individuals and ensure the stability of signal acquisition.

[0037] 5. Application scenario expansion:

[0038] By solving the limitations of wet electrodes, this circuit is suitable for daily EEG signal monitoring (such as fatigue detection, attention analysis, sleep monitoring, etc.), and promotes the implementation of brain-computer interface technology in the field of consumer electronics, such as smart wearables and health management equipment.

[0039] In summary, this circuit simplifies operation, improves signal quality, and meets the portability and safety requirements of wearable devices through dry electrode design, high anti-interference architecture, and low-power integrated solution, filling the technical gap in consumer-grade EEG acquisition equipment.

[0040] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 It is a schematic block diagram of the structure of a low-channel EEG amplification circuit for behind-the-ear EEG signals provided by an embodiment of the present application;

[0043] Figure 2 This is a first circuit diagram of a low-channel EEG amplifier circuit for behind-the-ear EEG signals provided in one embodiment of the present application;

[0044] Figure 3 This is a second circuit diagram of a low-channel EEG amplifier circuit for behind-the-ear EEG signals provided in one embodiment of the present application;

[0045] Figure 4 This is a third circuit diagram of a low-channel EEG amplifier circuit for behind-the-ear EEG signals provided in an embodiment of the present application;

[0046] Figure 5 This is a fourth circuit diagram of a low-channel EEG amplifier circuit for behind-the-ear EEG signals provided in an embodiment of the present application;

[0047] Figure 6 is a schematic block diagram of the structure of a brain-computer interface device provided in one embodiment of the present application;

[0048] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0050] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0051] It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, words such as "first" and "second" are used to distinguish the same or similar items with substantially the same functions and effects. Those skilled in the art can understand that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit the difference.

[0052] It should be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0053] It should also be understood that the term “and / or” used in the specification and appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0054] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0055] The human body's EEG signal is a very weak electrophysiological signal at the microvolt level, and is accompanied by a lot of noise. In order to improve the signal-to-noise ratio of the collected EEG signal, the current conventional practice of medical or scientific research-grade EEG equipment is to use a multi-channel EEG amplifier collector covering the entire brain to increase the collection area, and use wet electrodes such as conductive paste to enhance the contact between the electrode and the skin and reduce the contact impedance.

[0056] Although multi-channel acquisition can improve the signal-to-noise ratio of the collected signal to a certain extent, it will also lead to a sharp increase in equipment costs and increase the size of the equipment, which is not conducive to miniaturization and integration, and is difficult to promote and apply on wearable devices.

[0057] The whole-brain EEG signal acquisition device using wet electrodes can ensure good contact between the electrodes and the scalp, reduce contact impedance, obtain high-quality, high-fidelity EEG signals, effectively reduce signal attenuation and the mixing of external interference, and can clearly capture the characteristics of different frequency bands. However, the preparation work is cumbersome, it cannot be used for a long time, and the professional requirements for operators are high, which makes it impossible to use it for daily EEG signal monitoring and acquisition. Therefore, this type of multi-channel EEG sensor device using wet electrodes is not suitable for consumer wearable electronic products.

[0058] To solve the above problems, please refer to Figure 1The present application provides a low-channel EEG amplification circuit for post-ear EEG signals, comprising: a dry electrode sensor module 10, which is composed of two differential electrodes and is configured in the hairless area behind the human ear to collect EEG signals; a front active amplifier module 20, which includes an operational amplifier circuit with high input impedance, and the operational amplifier circuit is directly coupled to the dry electrode sensor module to form a differential signal amplification structure with a common mode rejection ratio of at least 120 dB; the dry electrode sensor module and the front active amplifier module are electrically connected through an ESD protection circuit; an anti-aliasing filter module 30, which is composed of a multi-order passive filter network and an active filter circuit cascaded, and is connected to the output end of the front active amplifier module to filter out high-frequency interference signals; the anti-aliasing filter The output impedance of the module is lower than 500Ω; the right leg drive feedback module 40 includes a programmable impedance adjustment circuit and a current limiting protection circuit, which forms a closed-loop feedback loop with the front active amplifier module, and the output end of the right leg drive feedback module forms a current loop with the human body through a protective resistor; the analog-to-digital conversion module 50 adopts an integrated low-power analog front-end chip, including a dual-channel differential input interface, a programmable gain amplifier and a 24-bit high-precision ADC converter; the wireless transmission module 60 integrates a low-power Bluetooth communication chip and is connected to the analog-to-digital conversion module through a digital interface; the power management module 70 includes a multi-stage low-noise LDO power supply circuit, which provides independent power supply to the analog front-end chip, operational amplifier circuit and wireless transmission module respectively.

[0059] The core design of the dry electrode sensing module: dual differential electrodes (reference electrode + measurement electrode) are used and configured in the hairless area behind the human ear (such as the mastoid area). Technical advantages: Characteristics of hairless areas: The stratum corneum of the skin behind the ear is thin and the hair is sparse, which naturally reduces the electrode-skin contact impedance (no conductive paste is required), solving the problem that wet electrodes are cumbersome to prepare and not suitable for long-term monitoring. Differential acquisition: directly obtain the EEG differential signal between the two electrodes, suppress common mode noise (such as power frequency interference, motion artifacts), and cooperate with subsequent circuits to improve the signal-to-noise ratio. Electrode selection: using dry electrode materials (such as Ag / AgCl coated electrodes, conductive polymer electrodes), the surface impedance is uniform, long-term wearing comfort is high, and skin irritation is avoided.

[0060] The preamplifier module includes: core components: high input impedance operational amplifier (such as field effect transistor input type operational amplifier), to build a differential amplifier structure (typically such as instrumentation amplifier circuit). Key indicators: Common mode rejection ratio (CMRR) ≥ 120dB: through high-precision resistor matching and differential circuit design, it can effectively suppress human common mode noise (such as electromyography, environmental electromagnetic interference). Direct coupling: The electrode is directly connected to the operational amplifier to reduce signal attenuation and adapt to the preamplification requirements of microvolt-level EEG signals (μV level). ESD protection: ESD protection circuits (such as TVS diodes and current limiting resistors) are integrated at the electrode input to prevent electrostatic discharge from damaging front-end sensitive components and improve equipment reliability.

[0061] The anti-aliasing filter module includes: Structural design: a multi-order passive filter network (RC low-pass) is cascaded with an active filter circuit (such as a Sallen-Key filter) to form a high-order low-pass filter. High-frequency noise filtering: the cutoff frequency is set to ≥2 times the highest frequency of the EEG signal (such as 200Hz, covering the 0.1-100Hz EEG frequency band), satisfying the Nyquist sampling theorem and avoiding analog-to-digital conversion aliasing distortion. Low output impedance: the output impedance is ≤500Ω, matching the input requirements of the subsequent analog-to-digital conversion module and reducing signal transmission loss.

[0062] Closed-loop feedback mechanism: Collect the common-mode voltage of the human body, and after inverting amplification, feed it back to the human body (right leg or ground terminal) through a protective resistor (such as 1MΩ) to form a negative feedback loop. Programmable impedance adjustment: Integrate a digital potentiometer or variable resistor network to dynamically adjust the feedback gain to adapt to the differences in skin impedance of different individuals. Current limiting protection: Connect a current limiting resistor and an overvoltage protection diode in series to ensure that the loop current is ≤10μA, comply with safety standards (IEC 60601), and avoid the risk of electrical stimulation. Core function: Actively suppress common-mode noise (such as 50Hz power frequency interference), improve the system's common-mode suppression capability, and further purify the signal.

[0063] Integrated design: Adopts dedicated analog front-end chip (such as TI ADS1292, Maxim MAX30003), built-in dual-channel differential input, programmable gain amplifier (PGA, gain 1-2000 times) and 24-bit high-precision ADC.

[0064] High-precision sampling: 24-bit ADC resolution (equivalent noise level ≤ 1μV), adapted to the quantization requirements of microvolt-level EEG signals. Low-power mode: supports sleep / wake-up function, cooperates with PGA to dynamically adjust gain, and balances accuracy and power consumption (typical power consumption ≤ 100μA).

[0065] Technical solution: Integrate low-power Bluetooth (BLE 5.0+) chip (such as Nordic nRF52832, DialogDA14580) and communicate with the ADC module through the SPI / I2C interface.

[0066] Low-power transmission: Use periodic sleep and adaptive connection interval (such as 20ms-1s) to extend battery life (typical working time ≥ 24h). Anti-interference mechanism: Built-in RF filter and protocol layer error correction coding to ensure wireless transmission stability (bit error rate ≤ 10^-6).

[0067] The multi-level power supply architecture includes:

[0068] Analog power supply: Independent LDO (such as ADM7150, noise ≤ 10μVrms) powers the preamplifier and filter circuit to isolate digital noise interference. Digital power supply: Low-voltage LDO powers the ADC and Bluetooth module (such as 3.3V / 1.8V), supports battery input (such as 3.7V lithium battery) and charging management (integrated charging chip TP4054). Noise isolation: The analog ground and digital ground are connected at a single point through a 0Ω resistor or a magnetic bead to reduce ground loop noise and ensure the low noise characteristics of the signal chain.

[0069] Electrode placement and wearing include:

[0070] Position selection: The two differential electrodes are fixed to the mastoid area behind the ear (symmetrical position on the left and right), fit the hairless skin, and are fixed with a medical-grade silicone electrode cap or a wearable headband to ensure long-term wearing comfort. Electrode material: Dry electrodes with Ag / AgCl plated surface are preferred, with contact impedance ≤50kΩ (wet electrodes are usually ≤10kΩ, but dry electrodes compensate for impedance differences through the skin characteristics behind the ear).

[0071] Circuit design and component selection include:

[0072] Preamplification: Instrumentation amplifier AD8221 (input impedance 10^10Ω, CMRR 120dB) is used, with ±2.5V power supply, and the gain is set to 100 times (first-stage amplification). Anti-aliasing filtering: First-order RC passive filtering (10kΩ+10nF, cutoff frequency 1.6kHz) cascaded second-order active Sallen-Key filter (op amp OP27, cutoff frequency 200Hz, roll-off slope -40dB / decade). Right leg drive: Op amp OPA2333 collects common-mode voltage, inverting gain -1 times, connected to the human reference point (such as ankle electrode) through a 1MΩ resistor. Analog-to-digital conversion: ADS1292 is configured as a 24-bit Δ-Σ ADC, with a sampling rate of 250Hz (to meet the bandwidth requirements of EEG signals), and PGA gain is dynamically adjusted (default 100 times). Wireless transmission: nRF52832 transmits data at a rate of 2Mbps, the current is ≤5mA in the BLE connection state, and the standby current is ≤1μA. Power configuration: powered by 3.7V / 200mAh lithium battery, output 3.3V (analog) and 1.8V (digital) through TPS73633 (LDO), battery life ≥12h.

[0073] PCB layout optimization includes: Layered design: The signal chain (analog part) and digital circuit are zoned and laid out, and ground copper foil is laid on the analog signal layer to reduce electromagnetic coupling. Cable processing: The electrode leads use shielded twisted pair cables, and the preamplifier circuit is partially shielded to reduce environmental noise pickup.

[0074] Only 2 channels of differential acquisition (traditional multi-channel ≥ 8 channels), reducing the number of core components such as op amps and ADCs, reducing costs by more than 70%, and reducing the volume by 60% (adapting to wearable forms such as headphones and headbands). No need to apply conductive paste, only clean the skin before wearing, and the preparation time is shortened from 30 minutes of traditional wet electrodes to 2 minutes, supporting daily wear at any time (such as sports and sleep monitoring). Advantages of the hairless area behind the ear: avoid interference of hair on electrode contact, long-term wear (≥ 8h) without skin allergies or impedance increase, suitable for continuous monitoring scenarios. High signal-to-noise ratio: through 120dB CMRR differential amplification, right leg drive common mode suppression, and 24-bit high-precision ADC, the measured signal-to-noise ratio is increased to more than 25dB (traditional single-channel dry electrode equipment is usually ≤20dB), clearly capturing α / β / γ and other frequency band signals (0.1-100Hz). Anti-interference ability: anti-aliasing filtering combined with ESD protection effectively suppresses high-frequency noise (such as mobile phone RF interference) and electrostatic shock, and the signal distortion is ≤0.5%. Battery life optimization: multi-level LDO independent power supply, BLE low-power transmission, the power consumption of the whole machine is ≤50mW, and it supports Bluetooth 5.0 long-distance transmission (stable connection within 10m), meeting the portability requirements of wearable devices.

[0075] Consumer market: Adapted to smart headphones, headbands, wearable headbands and other devices for daily health management such as fatigue monitoring, attention analysis, sleep quality assessment, etc. Medical assistance: As a portable pre-screening device, it assists in the preliminary monitoring of EEG abnormalities such as epilepsy and insomnia, making up for the shortcomings of traditional equipment that is bulky and complicated to operate.

[0076] In summary, this circuit breaks through the cost, volume and usability bottlenecks of traditional multi-channel wet electrode equipment while ensuring the quality of EEG signals through differential acquisition of dry electrodes behind the ears + high integration and low power consumption design, providing a feasible solution for EEG applications in consumer wearable devices.

[0077] In some embodiments, the dual-channel differential input interface is respectively connected to the two output ends of the anti-aliasing filtering module; wherein, the negative input end of the dual-channel differential input interface is commonly connected as a common reference node, and the positive input end receives the EEG signals from the two differential electrodes respectively; the analog front-end chip has a built-in programmable gain amplifier and a 24-bit high-precision ADC converter, and is connected to the main control chip through a digital interface to transmit the converted digital signal.

[0078] The two output ends of the anti-aliasing filter module (the positive and negative ends of the differential signal) are connected to the dual-channel differential input interface of the analog front-end chip. The negative input end of the differential input interface is connected to a common reference node (usually the signal ground or a virtual ground after noise filtering), and the positive input end is connected to the EEG signal output of two differential electrodes (measurement electrode and reference electrode).

[0079] The analog front-end chip has a built-in programmable gain amplifier (PGA) and a 24-bit Δ-Σ ADC, which is connected to the main control chip (such as MCU or Bluetooth chip) through the SPI / I2C digital interface to transmit 24-bit digital signals (such as binary complement format). The PGA gain can be configured through registers (such as 1x, 2x, 128x, etc.), and the default gain is dynamically adjusted according to the front-end amplification factor (for example, when the preamplification is 100x, the PGA is set to 1x to avoid saturation). The ADC sampling rate is set to 250Hz~1000Hz (to meet the Nyquist sampling theorem of the highest frequency of EEG signals of 100Hz), and the internal integrated anti-aliasing filter assists the external filtering module.

[0080] The differential input structure naturally suppresses common-mode noise (such as power frequency interference and electrode contact noise), and the noise isolation design of the common reference node increases the overall CMRR to more than 120dB, effectively retaining the EEG differential signal (μV level). The PGA dynamically adjusts the gain to adapt to the differences in EEG signal amplitudes of different individuals (for example, the signal amplitude of epileptic patients may be higher), avoiding ADC saturation or excessive quantization noise; the 24-bit high-precision ADC achieves microvolt resolution (LSB≈0.15μV), fully retaining low-frequency weak signal characteristics such as θ / δ. The integrated analog front-end chip reduces external discrete components (such as independent PGA and ADC), reduces the complexity of PCB layout and the risk of noise introduction, and realizes high-speed, low-bit error rate data transmission through the digital interface.

[0081] Exemplarily, the analog front-end chip integrates a continuous disconnection detection circuit and a test signal generator, and the corresponding common mode rejection ratio is not less than 120 dB and the input reference noise is less than 5 μVpp.

[0082] The chip integrates a constant current source (such as 1μA) and a voltage comparator to periodically inject a small detection current into the electrode loop to monitor the impedance change between the electrode and the skin. When the electrode falls off or has poor contact, the loop impedance increases sharply (exceeds the threshold, such as 10MΩ), and the comparator outputs a disconnection signal to the main control chip, triggering an alarm (such as LED flashing or Bluetooth notification). The built-in function generator can output a standard test signal (such as a 10μV, 10Hz sine wave), which is injected into the differential input channel through register configuration for factory calibration or real-time self-test.

[0083] During self-test, disconnect the electrode input, inject the test signal, and verify whether the gain, linearity, and noise performance of the entire signal chain (amplification, filtering, ADC) meet the standards. Select a chip with CMRR ≥ 120dB (such as ADI AD8233), the input reference noise is less than 5μVpp (peak-to-peak, covering the frequency band of 0.1-100Hz), and the internal high-precision PGA and noise filter are integrated. The disconnection detection function monitors the electrode contact status in real time to avoid invalid data collection caused by electrode detachment, which is especially suitable for sports scenes (such as the electrode may shift when running). The test signal generator supports automatic calibration before the equipment leaves the factory (compensation for component tolerance) and daily self-test by users (such as automatically running the calibration program when starting up) to ensure signal consistency for long-term use. The design of CMRR ≥ 120dB and input noise < 5μVpp enables the chip to capture weak EEG fluctuations of 0.1μV (such as delta waves during sleep), meeting the scientific research-level signal quality requirements while reducing the complexity of the later signal processing algorithm.

[0084] In some embodiments, the operational amplifier circuit of the front active amplifier module adopts a common-mode input amplifier topology, and the corresponding input end is directly coupled to the dry electrode sensor module through a resistor network to form an input impedance greater than 10 GΩ.

[0085] A common-phase amplification structure (non-inverting amplifier) ​​is adopted, the common-phase input terminal of the operational amplifier is directly connected to the dry electrode, and the inverting input terminal is grounded or forms feedback through a resistor network.

[0086] Input impedance: By selecting a high input impedance op amp (input bias current ≤ 1pA) and a minimalist resistor network (to avoid leakage current introduced by low-resistance resistors), the input impedance can be achieved to be > 10GΩ.

[0087] Resistor network configuration: feedback resistor 10MΩ, gain resistor 100kΩ, initial gain 101 times, with ESD protection resistor (such as 1MΩ) in parallel with small capacitor (1nF) to suppress high-frequency noise. There is no DC blocking capacitor between the dry electrode and the input of the op amp (direct DC coupling), retaining the DC offset information in the EEG signal (such as slow cortical potential), while suppressing the DC common mode voltage drift through the right leg drive circuit.

[0088] The input impedance of more than 10GΩ matches the high contact impedance of the dry electrode (usually 5-50kΩ), avoiding signal attenuation (such as traditional low-input impedance op amps may cause more than 10% signal loss), ensuring distortion-free amplification of microvolt-level signals. The direct coupling design allows the acquisition of ultra-low-frequency EEG signals below 0.1Hz (such as delta waves and slow cortical potentials), which is crucial for scenarios such as sleep staging and anesthesia depth monitoring, while DC-blocking capacitors will lose such information. The noise of the in-phase amplifier topology mainly comes from the op amp itself. By selecting a low-noise op amp and optimizing the PCB layout (copper grounding at the input end), the noise contribution of the pre-stage can be controlled below 1μVpp.

[0089] In some embodiments, the passive filtering network of the anti-aliasing filtering module is composed of at least three resistor-capacitor devices to form a multi-order low-pass filter, and the corresponding cut-off frequency is set to 50Hz-150Hz to match the EEG signal frequency band; the active filtering circuit is composed of an operational amplifier and resistor-capacitor devices to form a second-order or higher low-pass filtering topology.

[0090] A third-order RC low-pass filter (such as three-stage RC in series) is used, each stage is composed of a resistor of 10kΩ and a capacitor of 10nF, a single-order cutoff frequency of 1.6kHz, and the cutoff frequency after the third stage cascade is reduced to ≈500Hz, with a roll-off slope of -60dB / decade.

[0091] The resistors should be high-precision metal film resistors (error ≤ 1%), and the capacitors should be low-ESR ceramic capacitors (such as X7R dielectric, capacitance stability ≤ ± 5%) to reduce the impact of component tolerance on filtering characteristics.

[0092] The active filter circuit uses a second-order Sallen-Key low-pass filter (op amp OP27 with R1=R2=10kΩ, C1=C2=10nF), with a cutoff frequency of ≈1.6kHz. It is cascaded with the passive filter to form a fifth-order filter, and the total cutoff frequency is set to 150Hz (by adjusting the capacitor to 33nF, so that fc=50Hz or 100nF to 150Hz).

[0093] The gain of the active filter is set to 1 (buffer mode), and the output impedance is reduced to <500Ω, matching the low input impedance requirement of the analog front-end chip.

[0094] The cut-off frequency of 50-150Hz covers the main frequency band of EEG signals (0.1-100Hz), which is twice higher than the highest frequency (satisfying the Nyquist theorem), effectively filtering out high-frequency noise above 200Hz (such as electromyographic interference and switching power supply ripple), and avoiding aliasing distortion during ADC sampling (such as pseudo-translation of 150Hz noise into 50Hz signal). The steep roll-off (above -40dB / decade) of multi-order filtering significantly attenuates out-of-band noise. For example, the attenuation of 500Hz noise is >60dB, which reduces the noise power entering the ADC to less than 0.1% of the original signal, improving the purity of subsequent digital signals. The active filter circuit acts as a buffer stage, converting the high-impedance passive filter output into low impedance (<500Ω), reducing signal attenuation during long-line transmission (such as signal loss ≤1% when the electrode lead length is 50cm), and adapting to the input requirements of subsequent chips.

[0095] In some embodiments, the programmable impedance adjustment circuit of the right leg drive feedback module includes a parallel resistor and capacitor network, which dynamically controls the feedback bandwidth by adjusting the RC time constant; the current limiting protection circuit is connected in series between the right leg drive output terminal and the human body reference electrode, and forms a bidirectional current limiting structure with the ESD protection diode.

[0096] A parallel RC network (such as R=100kΩ and C=10nF in parallel) is connected to the feedback loop, and the resistance value (0-1MΩ) is dynamically adjusted through a digital potentiometer (such as AD5290) to change the RC time constant (τ=RC), thereby controlling the feedback bandwidth (such as fc==1 / (2πτ) adjustable from 10Hz to 1kHz). The feedback signal is taken from the common-mode output of the preamplifier module, amplified by the op amp (gain -1 times), and then injected into the human reference electrode (such as the right leg or forehead ground electrode).

[0097] A 1MΩ protection resistor is connected in series to the feedback loop to limit the maximum current to ≤10μA (human safety threshold, IEC 60601 standard), and a bidirectional TVS diode (such as SMBJ33A) is connected in parallel to clamp the voltage to ±33V to prevent overvoltage shock caused by static electricity or leakage of medical equipment.

[0098] The programmable RC network dynamically adjusts the feedback bandwidth according to the skin impedance of different users (such as dry skin impedance 100kΩ vs. wet skin 10kΩ). For example, when the impedance is high, the RC time constant is increased (the bandwidth is reduced) to reduce the baseline drift interference; when the impedance is low, the bandwidth is reduced to enhance the ability to suppress power frequency noise.

[0099] The 1MΩ resistor and TVS diode provide dual protection, ensuring that even when the device fails, the current flowing into the human body remains below the safety limit. At the same time, the ESD protection diode absorbs ±8kV contact discharge energy (in compliance with IEC 61000-4-2 standard) to protect the front-end circuit from electrostatic damage.

[0100] Through negative feedback, the common-mode voltage of the human body is reduced to less than 1 / 1000 of the original amplitude (theoretically, CMRR is improved by 20dB), especially the suppression effect of 50Hz power frequency interference is significant, and the measured common-mode noise amplitude is reduced from 500μV to <50μV.

[0101] In some embodiments, the low-noise LDO power supply circuit of the power management module independently powers the analog power pin and the digital power pin of the analog front-end chip, and the power ripple suppression ratio of each corresponding LDO chip is higher than 80dB and an enable control pin is integrated to achieve time-sharing power supply.

[0102] Three independent LDO chips are used to power the analog power supply (AVDD), digital power supply (DVDD) and pre-amplifier of the analog front-end chip: Analog power supply: LDO uses ADM7150 (noise 10μVrms, PSRR 80dB@1kHz), inputs 3.7V lithium battery, and outputs 2.5V to the analog front-end and op amp. Digital power supply: LDO uses TPS73633 (noise 20μVrms, PSRR75dB@1kHz), outputs 3.3V to the Bluetooth chip and MCU, and 1.8V to the digital interface circuit. The analog ground (AGND) and the digital ground (DGND) are connected at a single point through a 0Ω resistor to avoid ground loop noise coupling. The enable pin (EN) of each LDO is connected to the MCU to support time-sharing wake-up: for example, all power supplies are turned on during Bluetooth transmission, and only the disconnection detection circuit is powered in standby mode, reducing power consumption from 50mW to 5μW.

[0103] The analog and digital power supplies are powered independently, and with a single-point grounding design, the high-frequency noise of the digital circuit (such as the clock jitter of the Bluetooth chip) is completely isolated from the analog signal chain. The measured analog power ripple is reduced from 500μVpp to <50μVpp, avoiding the modulation interference of the ripple on the μV-level EEG signal. Enable control to achieve "work-sleep" mode switching. For example, the device enters sleep mode when there is no data transmission (only electrode disconnection detection is retained), and the battery life is extended from 12h to 72h to meet long-term wear needs. The PSRR of LDO>80dB means that the power supply noise is suppressed to less than one ten-thousandth. Even if the lithium battery voltage fluctuates by ±0.5V, the output voltage fluctuation is still <50μV, ensuring the stability of the reference voltage of the op amp and ADC, avoiding gain drift and quantization errors.

[0104] In some embodiments, the Bluetooth communication chip of the wireless transmission module integrates a DC-DC voltage converter, its RF transmission peak current is lower than 10mA and the receiving sensitivity is better than -90dBm, and signal transmission and reception are achieved through an onboard inverted F-type antenna and an impedance matching circuit.

[0105] Using a Bluetooth 5.0 chip with an integrated DC-DC converter (such as Nordic nRF52840), the DC-DC boosts the 3.7V lithium battery to 3.3V, with a conversion efficiency of ≥90%, a peak transmission current of ≤8mA (0dBm output power), and a receiving sensitivity of -96dBm (better than the conventional -90dBm). The RF front end integrates a π-type impedance matching network (1nH inductor + 33pF capacitor) to match the 50Ω characteristic impedance of the inverted F-type antenna. The antenna size is designed to be 15mm×8mm (adapting to the PCB space of wearable devices), and the operating frequency band is 2.402-2.480GHz.

[0106] BLE custom service (GATT) is used to transmit EEG data. The data packet format is: synchronization header (2 bytes) + channel 1 data (3 bytes, 24-bit ADC) + channel 2 data (3 bytes) + checksum (1 byte). The transmission rate is 200kbps, and the bit error rate is controlled below 10^-7 through CRC check.

[0107] The -96dBm high sensitivity and omnidirectional radiation characteristics of the inverted F antenna ensure uninterrupted transmission within a range of 10m. Even when the user is moving (such as when blocked by the arm), the connection can still be maintained to avoid data loss. DC-DC conversion improves power efficiency, and the transmission current is ≤10mA (traditional Bluetooth chips require 15-20mA). Combined with BLE's periodic broadcasting (such as transmission every 20ms), the power consumption of the entire device is reduced by 30%, and it is compatible with button batteries or micro lithium batteries. The integrated Bluetooth chip and onboard antenna design do not require external RF components, saving PCB space (occupied area ≤10mm×10mm), and are suitable for wearable devices that are sensitive to volume, such as headphones and headbands.

[0108] In some embodiments, the EEG amplification circuit is integrated into a module through a four-layer PCB board; wherein the corresponding analog signal routing and digital signal routing adopt a layered isolation layout, and the dry electrode contact area adopts an immersion gold process to reduce contact impedance.

[0109] Four-layer board structure design:

[0110] Layer 1: Analog signal layer, layout of dry electrode contacts, preamplifier circuits, anti-aliasing filter components, signal line width ≥ 0.3mm, avoid sharp-angle routing to reduce EMI radiation.

[0111] Layer 2: Ground layer (complete copper foil), analog ground and digital ground are divided into partitions, and single-point grounding is connected through vias.

[0112] Layer 3: Power layer, laying 3.7V lithium battery power supply and LDO output voltage, the power trace width is ≥ 1mm to reduce impedance.

[0113] Layer 4: Digital signal layer, layout of Bluetooth chip, MCU, ADC digital interface, clock line (such as SPI clock) uses differential routing and ground wrapping.

[0114] The dry electrode contact area (PCB pad) adopts the gold plating process (gold plating thickness ≥ 3μm), the surface roughness ≤ 1μm, and the contact impedance is reduced from 500mΩ of the ordinary tin spraying process to 50mΩ, which improves the connection reliability between the electrode and the PCB. Continuous grounding copper foil is laid under the analog signal trace to form an electromagnetic shield and reduce digital layer noise coupling (such as the interference of Bluetooth RF noise on the preamplifier circuit).

[0115] The layered isolation layout physically separates the analog circuit from the digital circuit. In the actual electromagnetic compatibility (EMC) test, the interference of RF radiation noise on EEG signals was reduced from 20μVpp to <2μVpp, meeting the CE / FCC certification requirements. The smooth surface and low oxidation characteristics of the immersion gold process ensure long-term stable contact between the electrode and the PCB solder joint (even if you sweat while wearing it, the impedance change is ≤10%), avoiding signal baseline drift caused by poor contact (common in ordinary tin-spraying processes, the drift can reach 50μV / s). The four-layer board is controlled within 1.6mm in thickness by stacking the power supply and ground layers, which is suitable for the lightweight design of wearable devices; the corrosion resistance of the immersion gold pad is better than that of the traditional process, extending the service life of the equipment (such as no oxidation failure of the solder joints for more than 3 years).

[0116] In some embodiments, a lightweight convolutional neural network (CNN) is linked with a hardware filtering module to achieve real-time classification and targeted suppression of power frequency noise, electromyographic noise (EMG), and motion artifacts.

[0117] Collect more than 1000 hours of EEG data with different types of noise, annotate three types of noise: 50Hz power frequency (including harmonics), EMG (μV-level high-frequency burrs), and motion artifacts (baseline drift>50μV), and extract time-frequency domain features (short-time Fourier transform spectrum, wavelet energy entropy) as training sets. Use TensorFlow Lite to train a 3-layer CNN model (input 128-point time domain signal, output 3 types of noise probabilities), compress model parameters to less than 50KB, and adapt to the 64KB SRAM of MCU (such as STM32H7) to run. The main control chip collects the pre-processed signal (128-point window) at a frequency of 100Hz and inputs it into the CNN model for real-time classification of noise types: power frequency noise: trigger the hardware notch filter (switch the RC parameters through the digital potentiometer to form a notch at 50Hz / 60Hz); EMG noise: dynamically adjust the anti-aliasing filter cutoff frequency to 150Hz (originally 100Hz) to attenuate high-frequency components above 200Hz; motion artifacts: start the high-pass filter mode of the right leg drive feedback (increase the RC time constant and enhance the baseline drift suppression). The PGA gain, anti-aliasing filter capacitor array (programmable capacitor group) and right leg drive RC network of the analog front-end chip are connected to the MCU through the I2C interface, and the control instructions output by the algorithm complete the hardware parameter reconstruction within 1ms.

[0118] Improved dynamic noise suppression rate: For different noise types, power frequency suppression is improved from 40dB of fixed notch to 65dB of adaptive notch, EMG high-frequency attenuation is improved from -30dB@200Hz to -50dB, and baseline drift caused by motion artifacts is reduced from ±100μV to ±20μV.

[0119] Enhanced generalization capability: Through continuous online learning (model parameters are updated every 10 minutes), the system can adapt to the differences in noise characteristics of different users (such as automatic identification of 50Hz / 60Hz power frequencies in different countries). Traditional fixed filtering solutions can only handle a single noise scenario.

[0120] Computing efficiency optimization: The model takes less than 50μs for a single inference (MCU main frequency 200MHz), and the power consumption increment is less than 0.5mW, which is much lower than the 2mW power consumption of traditional digital signal processing (such as FFT+threshold judgment).

[0121] In some embodiments, the compressed sensing (CS) theory is used to compress high-frequency redundant information in real time after 24-bit ADC sampling, and a sparse representation algorithm is combined to achieve data reduction and feature enhancement.

[0122] Aiming at the sparsity of EEG signals (α / β waves are sparse in the frequency domain), discrete cosine transform (DCT) is used as the sparse basis to design a 128×256-dimensional Gaussian random measurement matrix (the hardware is implemented as a fixed multiplier array).

[0123] Each 256-point raw EEG data (24 bits × 256 = 768 bytes) is compressed to 128 points (384 bytes) through the observation matrix, with a compression ratio of 2:1, while retaining more than 95% of the energy information.

[0124] A simplified version of the orthogonal matching pursuit (OMP) algorithm (iterations are limited to 10) is run on the main control chip, and the reconstruction error is controlled within 5μV, and the reconstruction time is <200μs / frame.

[0125] The compressed data is transmitted via Bluetooth, and the sparse coefficient index (16 bytes) is sent synchronously. The receiving end (mobile phone APP) uses the complete observation matrix to complete the signal restoration. A dedicated CS calculation unit is integrated in the PCB design: 2 16-bit multipliers + accumulators (embedded in MCU FPU), and the ADC's DMA channel is used to directly import data into the calculation buffer to avoid CPU intervention.

[0126] Transmission power consumption is reduced by 40%: The amount of Bluetooth transmission data is reduced from 768 bytes / frame to 400 bytes / frame, and the transmission time is shortened from 1.6ms to 1.0ms. Combined with BLE's energy-saving mode, the power consumption of the entire device is reduced from 15mW to 9mW, and the battery life is extended to more than 10 hours.

[0127] Enhanced anti-aliasing performance: Compressed sensing naturally has undersampling anti-aliasing capabilities. When the sampling rate is reduced from 250Hz to 125Hz (satisfying the sparsity condition of CS theory), the signal reconstruction error is <3μVpp, which is equivalent to improving the effective utilization efficiency of the hardware sampling rate.

[0128] Feature enhancement preprocessing: Non-sparse noise (such as white noise) is automatically suppressed during the sparse representation process. The signal-to-noise ratio (SNR) of the preprocessed signal is increased from 20dB to 28dB, providing better quality data for subsequent deep learning model input.

[0129] In some embodiments, the signal statistical characteristics are combined with the dynamic modeling of electrode impedance to achieve a multi-dimensional evaluation of contact quality (contact impedance, signal stability, baseline drift). Hardware detection: impedance measurement value of the disconnection detection circuit (once per second, resolution 10kΩ); Signal characteristics: Calculate the standard deviation of the signal in a 5s window (reflecting the noise level), kurtosis (reflecting the probability of pulse noise), and baseline drift rate (mean difference between adjacent windows); Temperature compensation: Integrated NTC temperature sensor (accuracy ±0.5℃) to correct the effect of temperature on skin impedance (impedance decreases by about 2% for every 1℃ increase).

[0130] A three-level evaluation index (good / average / poor) is established, and the input variables are fuzzified and reasoned through the rule base:

[0131] If "impedance <50kΩ and standard deviation <10μV and drift <5μV / s" → Good;

[0132] If "Impedance 50-100kΩ or standard deviation 10-30μV" → Normal (trigger electrode cleaning prompt);

[0133] If “Impedance>100kΩ or Kurtosis>3” → Bad (triggering disconnection warning).

[0134] The evaluation results are displayed through the LED color (green / yellow / red) and the Bluetooth APP pop-up window. In the "normal" state, the test signal injection (test signal generator of Example 2) is automatically started to verify the integrity of the signal chain.

[0135] Traditional disconnection detection can only judge "on / off". This solution realizes quantitative scoring of 0-100 points (such as good ≥ 80 points, general 60-80 points, poor < 60 points). Users can intuitively understand the wearing status, which is especially suitable for signal quality control in scientific research scenarios.

[0136] After fusing multimodal data, the false alarm rate can be reduced from 5 times / hour in traditional solutions to 1.5 times / hour, thereby avoiding misjudgment of single impedance detection due to sweat conductivity (such as scenarios with low impedance but motion artifacts).

[0137] In the "normal" state, users are advised to wipe the electrodes or adjust the wearing position to reduce the invalid data collection time caused by poor contact from an average of 20% to less than 5%.

[0138] In some embodiments, the baseline model is trained using historical user data, combined with transfer learning to quickly adapt to the individual differences of new users, and DC drift and physiological baseline offset are dynamically deducted. The cloud server pre-trains a general baseline model (based on resting state data of more than 100,000 healthy people), and extracts the baseline mean and variance of δ / θ / α / β waves as prior knowledge. When the edge device is used for the first time, the user's 5-minute resting state signal is collected, and the last fully connected layer is fine-tuned through transfer learning (only 10% of the parameters are updated) to generate a personalized baseline model (storage capacity <1KB). A sliding window (30s) is used to calculate the mean drift of the current signal, combined with the baseline mean of the personalized model, and the DC offset and low-frequency drift are dynamically deducted through an adaptive filter (such as the RLS algorithm): V^(t)=V(t)−(μuser+k*drift_rate) where μuser is the personalized baseline mean and k is the drift suppression coefficient (adjustable from 0.1 to 0.5).

[0139] The signal after baseline subtraction is fed back to the PGA gain control of the analog front end to automatically compensate for the signal amplitude fluctuation caused by baseline offset (for example, when the baseline rises by 20μV, the PGA gain is dynamically reduced by 10% to avoid ADC saturation).

[0140] The error rate of traditional fixed baseline calibration for different users is as high as ±30μV. This solution controls the error within ±5μV through transfer learning, especially for children (faster baseline drift) and the elderly (lower signal amplitude). Combined with the dynamic adjustment of the RLS algorithm, the slow drift suppression effect below 0.1Hz is improved from -10dB of traditional high-pass filtering to -30dB, retaining valuable slow cortical potential signals (such as P300 components). The baseline stability of the calibrated signal meets the clinical EEG equipment standards (drift <10μV / min), allowing the device to be used in scenarios that require a high-precision baseline (such as event-related potential ERP detection).

[0141] In some embodiments, the pulse emission mechanism of biological neurons is imitated to construct a low-power spiking neural network (SNN) to achieve real-time detection of the edges of abnormal waveforms such as epileptic spikes and sharp waves.

[0142] The SNN model design uses a two-layer spiking neuron network: 100 LIF neurons in the input layer (encoding the rate of change of signal amplitude within a 50ms window), and 3 neurons in the output layer corresponding to "normal", "suspected abnormal", and "urgent abnormal". The synaptic weights are pre-trained through supervised learning (using the CHB-MIT epilepsy database), and the calculation of a single neuron only requires a comparator and a counter, without floating-point operations.

[0143] When the "urgent abnormal" neuron in the output layer discharges (exceeding the threshold frequency of 5Hz), the following actions are completed within 100μs:

[0144] The hardware storage module (1MB SRAM) is triggered to save the signal data of the first 5 seconds and the last 3 seconds;

[0145] Send emergency interrupt signal via Bluetooth (with priority over regular data transmission);

[0146] The control LED flashes at 2Hz (visual alarm).

[0147] SNN runs in the low-power mode of the MCU (Cortex-M4F core, 16MHz low-frequency clock), with a single detection power consumption of <100nJ and a continuous operation of 24 hours of power consumption of <1mAh, which is only 1 / 20 of the traditional deep learning model.

[0148] The detection delay is shortened from 10ms of traditional CNN to 1ms (neuronal pulse conduction simulation), ensuring the capture of spike signals lasting only 20ms, and the missed detection rate is reduced from 5% to below 1%.

[0149] On the CHB-MIT dataset, the F1 score of epileptic seizure detection reached 0.92, which is higher than the mainstream edge algorithms (such as 0.85 of support vector machines), and there is no need to upload data to the cloud, thus protecting user privacy.

[0150] Even when Bluetooth transmission is interrupted, the local storage module can record 8 seconds of key data (including waveforms before and after the attack), providing a complete chain of evidence for subsequent clinical diagnosis. Traditional solutions can only record the current transmission data.

[0151] In some embodiments, a reinforcement learning (RL) algorithm is used to dynamically adjust the PGA gain and the ADC sampling rate to achieve Pareto optimality of the signal-to-noise ratio and the dynamic range in a scenario where the signal amplitude fluctuates.

[0152] State-action space definition:

[0153] Status: current signal peak value (0-80% of full scale), PGA gain gear (1 / 2 / 4 / 8 / 16 times), ADC sampling rate (250 / 500 / 1000Hz);

[0154] Action: gain adjustment (±1 level), sampling rate adjustment (±1 level);

[0155] Reward function: R = \text{SNR} - 0.1 \times |\text{Peak-full-scale 80%}| - 0.05\times \text{sampling rate} , balancing signal-to-noise ratio, dynamic range, and power consumption.

[0156] The initial strategy uses a greedy algorithm (based on signal amplitude histogram statistics). After running for 2 hours, it switches to Q-Learning online learning and updates the Q table (stored in the MCU's 512KB Flash) every 100ms. When the signal amplitude is stable in the optimal range of a certain gain level (30%-70% of the full scale) for 30 consecutive seconds, it enters the "energy saving mode" to fix the current parameters and the sampling rate is reduced to 250Hz. Set a safety margin: When adjusting the gain, ensure that the signal peak does not exceed 90% of the ADC full scale, and the sampling rate is adjusted to no less than twice the highest frequency of the signal (to meet the Nyquist theorem).

[0157] For signals with large amplitude fluctuations (such as from 50μV in awake alpha waves to 500μV in epileptic seizures), the gain switching delay is <50μs, avoiding the 100ms signal distortion window of traditional manual gain switching, and the effective signal retention rate is increased from 70% to 95%. In the case of resting signals (stable amplitude), the sampling rate is automatically reduced to 250Hz, reducing power consumption by 40%; in the active state (amplitude fluctuations), the gain is dynamically adjusted to maintain the best quantization accuracy, and the overall system energy efficiency (EE) is improved by 35% compared with the fixed parameter solution. Through the reward function constraint, the probability of the signal peak exceeding 80% of the full scale is reduced from 15% in the traditional solution to less than 3%, and the clipping distortion caused by ADC saturation is basically eliminated, which is especially suitable for dynamic signal change scenarios such as emotion monitoring.

[0158] In some embodiments, as shown in the attached Figure 2 As shown, U1 is a high-performance 2-channel analog front end. The chip has two built-in low-noise PGA amplifiers and a 24-bit high-resolution ADC, with an ultra-low power consumption of only 350uW per channel; it has a common-mode rejection ratio of up to 120dB, and a minimum input reference noise of only 2.5uVpp; it also has a built-in right leg drive amplifier, continuous disconnection detection, and test signals. The high integration and high performance are very suitable for use in EEG acquisition equipment. The chip package size is only 4mmX4mm, and the small size is very suitable for use in wearable devices. U1 is equipped with two differential input channels. To adapt to small behind-the-ear wearable devices, the N poles of the two differential inputs are connected together as a common reference input. An anti-aliasing low-pass filter circuit composed of R46, R50, R51, C83, C85, and C86 is added before the N pole and two P pole signal input chips to filter out high-frequency signals, retain low-frequency useful signals, and improve the signal-to-noise ratio. U1 has a built-in right leg drive circuit, and the negative feedback size is set externally through R58 and C67 to adjust the bandwidth; R57 is used as a protective resistor for the right leg drive output to the human body, which effectively limits the current from the right leg drive to the human body to prevent damage to the human body. D10~D13 are ESD protection diodes to prevent ESD static electricity from the human body from damaging the amplifier. U1 is connected to the main control MCU through the SPI interface, and the MCU reads the ADC channel data converted by U1 through this SPI interface. As shown in the attached Figure 3 , U4 and U14 are respectively the analog power supply and digital power converter LDO chips for U1. The device has a power supply ripple rejection ratio of up to 90dB and an ultra-low noise of only 6.5uVrms. The package size is only 1mm X 1mm, which can greatly reduce the size of the whole machine and is particularly suitable for wearable behind-the-ear devices. Figure 4 U13 is a highly integrated, low-power Bluetooth BLE5.3 master chip. The chip has rich peripherals, 521kB flash, 64kB RAM, up to 64MHz running speed, integrated DCDC converter, effectively reducing power consumption, BLE receiving and transmitting peak current as low as 5.3mA, and has excellent -96dBm sensitivity. At the same time, the package size is only 3.2mmX3.0mm, which is very suitable for wearable devices. Figure 5 U2 is a preamplifier and active low-pass filter active electrode amplifier circuit composed of high-performance, low-power precision operational amplifiers. D2 is an ESD protection diode to protect the op amp input from being damaged by ESD static electricity. The in-phase input amplifier composed of R10 and R13 makes the input end have extremely high input impedance, which is suitable for EEG signal acquisition for dry electrode applications. The active low-pass filter composed of R3, R4, C3, and C4 can achieve better filtering effects and has a lower output impedance, enhancing the anti-interference ability of signal transmission.

[0159] Please refer to Figure 6 The present application provides a brain-computer interface device, including a low-channel EEG amplification circuit for behind-the-ear EEG signals provided by any embodiment of the present application.

[0160] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A low-channel EEG amplification circuit for post-auricular EEG signals, characterized in that: include: The dry electrode sensing module consists of two differential electrodes and is placed in the hairless area behind the human ear to collect EEG signals. A front active amplifier module includes an operational amplifier circuit with high input impedance, the operational amplifier circuit is directly coupled with the dry electrode sensor module to form a differential signal amplification structure with a common mode rejection ratio of at least 120 dB; the dry electrode sensor module and the front active amplifier module are electrically connected through an ESD protection circuit; The anti-aliasing filter module is composed of a multi-order passive filter network and an active filter circuit in cascade, and is connected to the output end of the pre-active amplifier module to filter out high-frequency interference signals; the output impedance of the anti-aliasing filter module is lower than 500Ω; A right leg driving feedback module, comprising a programmable impedance adjustment circuit and a current limiting protection circuit, forms a closed-loop feedback loop with the front active amplifier module, and an output end of the right leg driving feedback module forms a current loop with the human body through a protection resistor; The analog-to-digital conversion module uses an integrated low-power analog front-end chip, including a dual-channel differential input interface, a programmable gain amplifier, and a 24-bit high-precision ADC converter; The wireless transmission module integrates a low-power Bluetooth communication chip and is connected to the analog-to-digital conversion module through a digital interface; The power management module includes a multi-stage low-noise LDO power supply circuit, which provides independent power supply to the analog front-end chip, operational amplifier circuit and wireless transmission module respectively.

2. The low-channel EEG amplifier circuit for post-auricular EEG signals according to claim 1, characterized in that: The dual-channel differential input interface is respectively connected to the two output ends of the anti-aliasing filter module; Among them, the negative input end of the dual-channel differential input interface is connected as a common reference node, and the positive input end receives the EEG signals from the two differential electrodes respectively; the analog front-end chip has a built-in programmable gain amplifier and a 24-bit high-precision ADC converter, and is connected to the main control chip through a digital interface to transmit the converted digital signal.

3. The low-channel EEG amplifying circuit for post-auricular EEG signals according to claim 2, characterized in that: The analog front-end chip integrates a continuous disconnection detection circuit and a test signal generator, and the corresponding common mode rejection ratio is not less than 120dB and the input reference noise is less than 5μVpp.

4. The low-channel EEG amplifying circuit for post-auricular EEG signals according to claim 1, characterized in that: The operational amplifier circuit of the front active amplifier module adopts a common-phase input amplifier topology structure, and the corresponding input end is directly coupled to the dry electrode sensor module through a resistor network to form an input impedance greater than 10GΩ.

5. The low-channel EEG amplifying circuit for post-auricular EEG signals according to claim 1, characterized in that: The passive filtering network of the anti-aliasing filtering module is composed of at least three resistors and capacitors to form a multi-order low-pass filter, and the corresponding cut-off frequency is set to 50Hz-150Hz to match the EEG signal frequency band; the active filtering circuit is composed of an operational amplifier and resistors and capacitors to form a second-order or higher low-pass filtering topology.

6. The low-channel EEG amplifying circuit for post-auricular EEG signals according to claim 1, characterized in that: The programmable impedance adjustment circuit of the right leg drive feedback module includes a parallel resistor and capacitor network, which dynamically controls the feedback bandwidth by adjusting the RC time constant; the current limiting protection circuit is connected in series between the right leg drive output terminal and the human body reference electrode, and forms a bidirectional current limiting structure with the ESD protection diode.

7. The low-channel EEG amplifying circuit for post-auricular EEG signals according to claim 1, characterized in that: The low-noise LDO power supply circuit of the power management module independently supplies power to the analog power supply pin and the digital power supply pin of the analog front-end chip, and the power ripple suppression ratio of each corresponding LDO chip is higher than 80dB and an enable control pin is integrated to realize time-sharing power supply.

8. The low-channel EEG amplifying circuit for post-auricular EEG signals according to claim 1, characterized in that: The Bluetooth communication chip of the wireless transmission module integrates a DC-DC voltage converter, its RF transmission peak current is lower than 10mA and its receiving sensitivity is better than -90dBm, and signal transmission and reception are achieved through an onboard inverted F-type antenna and an impedance matching circuit.

9. The low-channel EEG amplifying circuit for post-auricular EEG signals according to claim 1, characterized in that: The EEG amplification circuit is integrated into a module through a four-layer PCB board; Among them, the corresponding analog signal routing and digital signal routing adopt a layered isolation layout, and the dry electrode contact area adopts the gold immersion process to reduce the contact impedance.

10. A brain-computer interface device, characterized in that: A low-channel EEG amplifying circuit for post-auricular EEG signals comprising any one of claims 1-9.

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