Non-invasive brain-computer interface system based on real-time electroencephalogram monitoring of earphone-auditory stimulation

Abstract

The embodiment of the present disclosure provides a non-invasive brain-computer interface system based on earphone real-time electroencephalogram monitoring-auditory stimulation, and relates to the technical field of neural engineering. The system comprises an in-ear earphone device and an external control device. The in-ear earphone device is used for collecting the brain wave signals of a user, transmitting the brain wave signals to the external control device, generating a sound stimulation signal based on a sound stimulation trigger instruction, and delivering the sound stimulation signal to the ear of the user for sound stimulation. The external control device is used for detecting whether there is a characteristic sharp-slow wave signal corresponding to absence epilepsy attack in the brain wave signals. When the characteristic sharp-slow wave signal corresponding to the absence epilepsy attack is detected, the target sharp-slow wave of the brain wave signals is extracted, a sound stimulation trigger instruction is generated based on the sharp wave component of the target sharp-slow wave, and the stimulation trigger instruction is transmitted to the in-ear earphone device. Real-time and accurate monitoring and non-invasive closed-loop intervention of absence epilepsy attack can be realized, the system has high safety, and is portable and comfortable.

Description

Technical Field

[0001] This disclosure relates to the field of neuroengineering technology, and in particular to a non-invasive brain-computer interface system based on real-time EEG monitoring and auditory stimulation via headphones. Background Technology

[0002] Absence seizures are one of the most common forms of epileptic seizures in clinical practice. They are characterized by recurrent episodes of altered consciousness and are marked by spike-and-wave discharges. They can be classified into typical absence seizures (3Hz) and atypical absence seizures (1-2.5Hz), and can occur in various epilepsy syndromes in children and adolescents. Frequent daily absence seizures severely impact patients' daily lives and studies, placing a heavy burden on individuals, families, and society.

[0003] Clinical treatment for absence epilepsy primarily involves antiepileptic drugs, with valproic acid being a commonly used medication, which needs to be taken in divided doses daily. Although most patients can control or reduce seizures through years of medication, it can lead to side effects such as cognitive impairment, liver damage, and polycystic ovary syndrome. Furthermore, some patients develop drug resistance, making it difficult to effectively control seizures with medication. Summary of the Invention

[0004] To address or at least partially address the aforementioned technical problems, this disclosure provides a non-invasive brain-computer interface system based on real-time EEG monitoring and auditory stimulation via headphones. By combining an in-ear headphone device and an external control device, it achieves real-time and accurate monitoring and non-invasive closed-loop intervention for absence epilepsy seizures. It has no drug side effects, high safety, and the in-ear headphone device is lightweight, compact, portable, and comfortable.

[0005] This disclosure provides a non-invasive brain-computer interface system based on real-time EEG monitoring and auditory stimulation using an in-ear headphone. The system includes an in-ear headphone device and an external control device. The in-ear headphone device collects the user's EEG signals, transmits these signals to the external control device, and generates and delivers a sound stimulation signal to the user's ear based on a sound stimulation trigger command. The external control device detects the presence of characteristic spike-and-wave (SSW) signals corresponding to absence seizures in the EEG signals. When a characteristic SSW signal is detected, the target SSW signal is extracted from the EEG signal, and a sound stimulation trigger command is generated based on the spike component of the target SSW signal. This command is then transmitted to the in-ear headphone device.

[0006] This disclosure also provides a method for EEG signal detection and auditory feedback, applied to the aforementioned non-invasive brain-computer interface system based on in-ear headphones. The method includes: acquiring a user's EEG signal; detecting whether a characteristic spike-and-wave signal corresponding to absence seizures exists in the EEG signal; when a characteristic spike-and-wave signal corresponding to absence seizures is detected, extracting the target spike-and-wave from the EEG signal, and generating a sound stimulation trigger command based on the spike component of the target spike-and-wave; the sound stimulation trigger command instructing the application of sound stimulation to the user, and the sound stimulation trigger command includes stimulation parameters; generating a sound stimulation signal based on the stimulation parameters in the sound stimulation trigger command and delivering it to the user's ear for sound stimulation.

[0007] This application proposes a non-invasive brain-computer interface system for real-time EEG monitoring and auditory stimulation via earphones. The system utilizes a lightweight and compact in-ear earphone, suitable for long-term wear in daily life, study, and work, significantly improving portability and comfort. Combining the in-ear earphone with external control devices, it collects and detects the user's brainwaves in real time, reacting instantly during absence seizures. Stimulation parameters can be dynamically adjusted based on the user's EEG activity to adapt to different users' conditions. Applying auditory stimulation effectively suppresses abnormal brainwave activity. The millisecond-level closed-loop "collection-detection-decision-stimulation" system constructed in this application allows for precise intervention in the early stages of absence seizures, effectively interrupting or suppressing them. The entire process utilizes in-ear EEG acquisition and auditory stimulation, achieving non-invasive detection and intervention without drug side effects or surgical risks. It is well-tolerated by users and suitable for special populations such as children and adolescents. Attached Figure Description

[0008] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0009] Figure 1 A schematic diagram of a non-invasive brain-computer interface system based on real-time EEG monitoring and auditory stimulation via headphones, provided for an embodiment of this disclosure; Figure 2 This is a schematic diagram illustrating an in-ear headphone device worn on a user's ear, as provided in an embodiment of this disclosure. Figure 3 A schematic diagram of an electroencephalogram (EEG) signal detection and auditory feedback method provided in an embodiment of this disclosure; Figure 4 A schematic diagram of another electroencephalogram (EEG) signal detection and auditory feedback method provided in this embodiment of the present disclosure; Figure 5A schematic diagram illustrating yet another method for detecting electroencephalogram (EEG) signals and providing auditory feedback, as provided in this embodiment of the disclosure; Figure 6 This is a schematic diagram of the structure of a computing device provided in an embodiment of the present disclosure. Detailed Implementation

[0010] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0011] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0012] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0013] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0014] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0015] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0016] Absence seizures are a common form of epileptic seizure in clinical practice, and can be divided into typical absence seizures and atypical absence seizures. Typical absence seizures are seen in childhood absence epilepsy, adolescent absence epilepsy, etc., and their significant characteristic is that the EEG during the seizure shows widespread spike-and-wave complexes of 3 Hz. Clinically, they manifest as loss of consciousness, cessation of movement, and sudden onset and cessation, lasting approximately 4–20 seconds. Atypical absence seizures can occur in Lennox-Gastaut syndrome, etc., and are characterized by milder levels of impaired consciousness, longer duration, and widespread spike-and-wave complexes of 1.5–2.5 Hz during the seizure.

[0017] In recent years, neuromodulation techniques such as vagus nerve stimulation and deep brain stimulation have provided new treatment options for drug-resistant epilepsy. The pathological mechanism of absence seizures is related to the synchronized abnormal discharge of the thalamic-cortical circuit. Sound stimulation can be transmitted to this circuit through the auditory pathway, inducing physiological rhythms such as spindle waves and slow-wave oscillations to "compete" with the spike-and-slow-wave discharges of absence seizures for the synchronized discharge resources of the thalamic-cortical circuit. At the same time, the physiological rhythms further induce a refractory period in the thalamic-cortical circuit, reducing the excitability of the thalamic-cortical feedback and blocking the persistence and propagation of spike-and-slow-wave discharges. Previous studies have shown that applying sound stimulation within the first 3 seconds of a typical absence seizure can effectively inhibit its epileptiform discharges. However, this study only verified the effectiveness of sound stimulation in inhibiting typical absence seizures; it did not precisely correlate the timing of sound stimulation with abnormal EEG discharges, nor did it establish a standardized stimulation paradigm, and it lacks wearable, real-time closed-loop control equipment.

[0018] To address the aforementioned issues, this disclosure provides a non-invasive brain-computer interface (BCI) system based on real-time EEG monitoring and auditory stimulation via headphones. This system includes an in-ear headphone and an external control device. The in-ear headphone collects the user's EEG signals, transmits these signals to the external control device, and generates and delivers a sound stimulation signal to the user's ear based on a sound stimulation trigger command. The external control device detects the presence of characteristic spike-and-wave (SSW) signals corresponding to absence seizures in the EEG signals. When these signals are detected, the target SSW is extracted from the EEG signal, and a sound stimulation trigger command is generated based on the spike component of the target SSW. This command is then transmitted to the in-ear headphone. The fully automated closed-loop management system of "acquisition-detection-decision-stimulation" constructed in this application provides immediate feedback when pathological events occur, aligning better with the precision treatment concept of neuromodulation. Physicians can also adjust various parameters of the sound stimulation mode according to the patient's response and treatment needs, maximizing patient comfort while maintaining therapeutic efficacy.

[0019] The system will now be described in detail with reference to specific embodiments.

[0020] Please see Figure 1 , Figure 1 This is a schematic diagram of a non-invasive brain-computer interface system based on real-time EEG monitoring and auditory stimulation via headphones, provided as an embodiment of this disclosure. Figure 1 As shown, the non-invasive brain-computer interface system based on in-ear headphones consists of two parts: hardware and software. The hardware is an in-ear headphone device, and the software is an external control device. The in-ear headphone device is worn on the user's ear, and the external control device can be integrated into a computing device. The in-ear headphone device and the external control device transmit data via wireless communication technology.

[0021] The external control device refers to the processing unit used to perform EEG signal analysis, feature recognition, and feedback control generation functions. This processing unit can be integrated into general computing devices such as smartphones and tablets in software form, or it can be embedded in a dedicated embedded processor, the charging case of the in-ear headphone device, or a separate wearable processing module in firmware form.

[0022] Specifically, wireless communication technologies can include long-range wireless communication technologies such as mobile communication and satellite communication, as well as short-range wireless communication technologies such as Wi-Fi and Bluetooth.

[0023] In one specific implementation, the wireless communication technology is Bluetooth Low Energy, which features low power consumption and stable transmission, enabling bidirectional communication.

[0024] For example, the external control device is a smart mobile terminal. See also... Figure 2 , Figure 2 This is a schematic diagram illustrating an in-ear headphone device worn on a user's ear, as provided in an embodiment of this disclosure. Figure 2 As shown, the user wears the in-ear headphone device on their ear and communicates with the smart mobile terminal via Bluetooth.

[0025] In a preferred embodiment, the earbud portion of the in-ear headphone device is configured to be worn in the user's ear canal at a preset insertion depth, ranging from 5 mm to 15 mm, preferably from 8 mm to 12 mm. This insertion depth range is ergonomically optimized to ensure stable and low-impedance contact between the first EEG recording electrode and the skin of the concha, and between the second EEG recording electrode and the skin of the earlobe, thereby improving the signal-to-noise ratio and acquisition reliability of the EEG signal. Simultaneously, this depth range avoids pressure discomfort or mucosal damage caused by excessive insertion into the ear canal, balancing comfort and safety for long-term wear. By adapting to the ear canal anatomy of different users, the insertion depth is adjustable or multi-size earbud kits are used to accommodate users of different ages, including adults, teenagers, and children, ensuring broad applicability and user compliance in daily scenarios.

[0026] The in-ear headphone device includes a signal acquisition module, a first signal processing module, a first communication module, an auditory feedback module, and a stimulus output port module.

[0027] The signal acquisition module is equipped with a high-sensitivity EEG motor to acquire the user's brainwave signals. When the user experiences absence seizures, the brainwave signals are characterized as corresponding characteristic spike-and-wave signals.

[0028] Understandably, the signal frequencies of normal EEG signals and characteristic spike-and-wave signals are different. Generally, the signal frequency of characteristic spike-and-wave signals is 1Hz-4Hz, and the signal frequency of the spike component of characteristic spike-and-wave signals is 14Hz-50Hz.

[0029] In one possible implementation, the in-ear headphone device acquires the user's brainwave signals at a preset sampling rate. For example, the preset sampling rate is 256Hz.

[0030] In one possible implementation, there are two in-ear headphone devices: a left EEG acquisition earpiece and a right EEG acquisition earpiece, worn in the user's left and right ears respectively. Each earpiece has a built-in signal acquisition module, including a first EEG recording electrode and a second EEG recording electrode. The first EEG recording electrode is located in the concha and is used to acquire the user's brainwave signals, while the second EEG recording electrode is located in the earlobe and serves as a reference electrode. The second EEG recording electrode, compared to the first EEG recording electrode, can reduce interference from environmental signals on the brainwave signals.

[0031] Generally, the brainwave signals acquired by the two signal acquisition modules are basically the same. You can choose either signal acquired by the two signal acquisition modules as the user's brainwave signal, or you can use both signals acquired by the two signal acquisition modules as the user's brainwave signal.

[0032] The first signal processing module is integrated inside the in-ear headphone device and is used to process brainwave signals.

[0033] In one possible implementation, the first signal processing module includes an analog front end (AFE) chip that integrates multiple microprocessors, including an analog front-end amplifier, filters, and an analog-to-digital converter, which sequentially process the EEG signals using the three microprocessors.

[0034] Specifically, the first signal processing module includes an analog front-end chip, which integrates: Instrumentation amplifier: Input impedance not less than 1GΩ, Common Mode Rejection Ratio (CMRR) not less than 100dB, and adjustable gain range of 1000. 5000 times, used for initial amplification of raw EEG signals at the microvolt level; Analog bandpass filter: Employs a second-order or higher-order active filter with a passband frequency of 0.5Hz. 100Hz The 3dB point is used to initially limit the signal bandwidth and suppress some noise in the analog domain. Analog-to-Digital Converter (ADC): Resolution at least 16 bits, sampling rate configurable to at least 256Hz to ensure accuracy for the target frequency band, such as 0.5 GHz. The 30Hz EEG signal is digitized without loss. This AFE chip preferably adopts a low-power design to be compatible with wearable devices.

[0035] Specifically, the brainwave signal is first amplified using an analog front-end amplifier to expand the weak brainwave signal to a processable range; then, the brainwave signal is filtered using a filter to remove high-frequency noise; finally, the brainwave signal is converted from an analog signal to a data signal using an analog-to-digital converter.

[0036] For example, high-frequency noise refers to radio wave signals with a frequency greater than 50Hz. A filter is used to initially filter out high-frequency noise above 50Hz in the EEG signal. The sampling rate of the signal conversion is 256Hz. An analog-to-digital converter is used to convert the EEG signal from an analog signal to a data signal at a sampling rate of 256Hz. This can balance signal integrity and device power consumption, and can prepare for subsequent wireless transmission.

[0037] The first communication module uses Bluetooth Low Energy (BLE) technology to send the processed EEG signals to an external control device in real time. The external control device detects the presence of characteristic spike-and-wave (SSW) signals corresponding to absence seizures in the EEG signals. When a characteristic SSW signal is detected, the target SSW signal is extracted from the EEG signal. Based on the spike component of the target SSW signal, a sound stimulus trigger command is generated and transmitted to the in-ear headphone device. The first communication module receives the sound stimulus trigger command without using BLE technology.

[0038] The auditory feedback module generates a stimulus adjustment scheme based on the stimulus parameters in the sound stimulus trigger command, and generates a corresponding sound stimulus signal according to the stimulus adjustment scheme.

[0039] The stimulation parameters include stimulation type, stimulation intensity, and stimulation duration.

[0040] In one possible implementation, the auditory feedback module has a built-in multi-channel programmable sound stimulation submodule that can receive and process sound stimulation trigger commands from external control devices.

[0041] Specifically, the multi-channel programmable sound stimulation submodule acquires the stimulation type, stimulation intensity, and stimulation duration from the stimulation parameters, dynamically adjusts the parameters to obtain the corresponding stimulation adjustment scheme, and generates a non-invasive sound stimulus as a sound stimulation signal according to the stimulation adjustment scheme.

[0042] The stimulation output port module sends the sound stimulation signal generated by the auditory feedback module to the user's ear for sound stimulation.

[0043] In one possible implementation, the stimulation output port module employs a miniature speaker to precisely deliver the sound stimulation signal generated by the auditory feedback module to the user's ear for sound stimulation, ensuring effective reception of the sound stimulation signal. Understandably, the miniature speaker is designed to fit in-ear headphones.

[0044] Furthermore, the in-ear headphone device also includes a power management module, which supplies power to the signal acquisition module, the first signal processing module, the first communication module, the auditory feedback module, and the stimulation output port module.

[0045] In one possible implementation, the power management module is powered by a rechargeable lithium battery, ensuring that the entire in-ear headphone device can work continuously for extended periods.

[0046] For example, the power management module is powered by a rechargeable lithium polymer battery with a typical capacity of 150mAh. The module integrates a low-power power management chip (PMIC) with dynamic voltage and frequency scaling (DVFS) and modular power gating. By optimizing the power consumption of each hardware module (such as using Bluetooth Low Energy mode, intermittent ADC sampling, and processor sleep mode), the overall average operating current of the system can be controlled to 15 kWh in typical operating mode. 20mA, thus supporting the device to work continuously for more than 8 hours.

[0047] The external control equipment includes a second communication module, a second signal processing module, a feature detection and recognition module, and a feedback control module.

[0048] The second communication module receives brainwave signals from in-ear headphones via wireless transmission technology.

[0049] The second signal processing module sequentially performs filtering, power frequency notch filtering, and artifact removal on the EEG signal to retain the target frequency band EEG signal containing spike-and-wave characteristics.

[0050] Specifically, the second signal processing module performs three levels of signal processing. First, it uses bandpass filtering to filter the EEG signal, removing low-frequency drift and high-frequency noise, while retaining the characteristic frequency range of spike-and-wave waves in absence epileptic seizures.

[0051] For example, a 0.5-30Hz bandpass filter is used.

[0052] Then, power frequency notch filtering is used to suppress mains interference in the EEG signal.

[0053] For example, the IIR notch filter algorithm is used to eliminate power frequency interference at 50Hz or 60Hz. The IIR notch filter algorithm is a filter algorithm based on the infinite impulse response (IIR) structure, mainly used to filter out interference components of specific frequencies in a signal while preserving other frequency components as much as possible.

[0054] Then artifact removal is used to ensure the purity of the EEG signal.

[0055] In one possible implementation, a thresholding method is used to remove physiological artifacts such as eye movement and electromyography from the EEG signal.

[0056] Among them, the threshold method directly identifies and processes artifact segments that exceed the threshold by setting thresholds for signal amplitude, slope, or energy. Specifically, feature quantities of artifacts in EEG signals are extracted to obtain amplitude thresholds, slope thresholds, and energy thresholds. Based on empirically or adaptively set thresholds, segments exceeding the thresholds are removed or replaced.

[0057] In one possible implementation, correlation analysis is used to remove physiological artifacts such as eye movement and electromyography from the EEG signal.

[0058] Correlation analysis utilizes the linear correlation between artifact source signals and EEG signals to identify and remove artifact components mixed into the EEG signals. Specifically, reference signals for physiological artifacts such as eye movements and electromyography are collected, the correlation coefficient between the EEG signals and the reference signals is calculated, and segments of the EEG signals with correlation coefficients greater than a correlation threshold are removed.

[0059] The feature detection and recognition module detects whether there are characteristic spike-and-wave discharges corresponding to absence epilepsy in the processed EEG signal. When characteristic spike-and-wave discharges corresponding to absence epilepsy are detected, the spike component of the corresponding target spike-and-wave is extracted.

[0060] In one possible implementation, the frequency and amplitude of the brainwave signal are identified, and the presence of characteristic spike-and-wave discharges corresponding to absence seizures is determined based on the frequency and amplitude.

[0061] Specifically, the feature detection and recognition module includes a feature extraction submodule and a pattern recognition submodule. The feature extraction submodule uses time-frequency analysis to calculate the target power value of the EEG signal within a preset spike-and-wave frequency band, and the pattern recognition submodule calculates the duration for which the target power value is greater than the preset power value. When the duration is greater than the preset duration, it is determined that there is a characteristic spike-and-wave discharge corresponding to absence epilepsy in the EEG signal, and it is identified as a spike-and-wave event.

[0062] For example, a preset power value is adaptively set based on the user's baseline resting-state EEG power. The preset frequency band for characteristic spike-and-wave complexes is 1Hz-4Hz, the preset frequency band for the spike component of characteristic spike-and-wave complexes is 14-50Hz, and the preset duration is 15ms. Continuous wavelet transform (CWT) is used to calculate the power value of the EEG signal in real time within the preset frequency band of 14Hz-50Hz to obtain the target power value. When the target power value exceeds the preset power value and the duration is greater than 15ms, it is determined that there is characteristic spike-and-wave discharge corresponding to absence seizures in the current EEG signal, and it is judged as a valid spike-and-wave event.

[0063] In one possible implementation, the brainwave signal is input into a convolutional neural network model to determine whether there are characteristic spike-and-wave discharges corresponding to absence epileptic seizures in the brainwave signal.

[0064] Specifically, a dataset is constructed using historical EEG signals and corresponding labels showing characteristic spike-and-wave discharges corresponding to absence epileptic seizures, and historical EEG signals and corresponding labels showing no such discharges. This dataset is then used to train a convolutional neural network model. The EEG signals are input into the trained convolutional neural network model to obtain the judgment results of characteristic spike-and-wave discharges corresponding to absence epileptic seizures.

[0065] In one possible implementation, the frequency and amplitude of the EEG signal are sequentially used, along with a trained convolutional neural network model, to determine whether characteristic spike-and-wave discharges corresponding to absence seizures exist in the EEG signal. The specific method is the same as described above and will not be repeated here.

[0066] Furthermore, when the EEG signal is determined to contain characteristic spike-and-wave discharges corresponding to absence seizures, the target spike-and-wave discharge corresponding to the characteristic spike-and-wave discharge is extracted; and the spike component of the corresponding target spike-and-wave discharge is extracted to identify the spike discharge time characteristics. These spike discharge time characteristics include the spike's onset time, termination time, and peak time.

[0067] In one possible implementation, the spike emission time feature is extracted using the peak detection method. The moment when the signal amplitude in the spike component frequency band is higher than the preset spike threshold is the start time, and the moment when it is lower than the preset spike threshold is the end time. The moment corresponding to the maximum amplitude is the peak time.

[0068] Furthermore, the spike-wave event and spike emission time characteristics are correlated and output.

[0069] The feedback control module is used to determine the stimulation parameters and delay duration based on the target spike-and-wavelength wave, and to generate a sound stimulation trigger command based on the stimulation parameters.

[0070] In one possible implementation, the feedback control module includes a factor extraction submodule and an instruction generation submodule. The factor extraction submodule is used to extract the target factors of the target spike-and-wavelength wave. The instruction generation submodule determines the stimulus parameters and delay duration based on the target factors and generates a sound stimulus trigger instruction based on the stimulus parameters.

[0071] Specifically, the target factors include target amplitude, target duration, and target attack frequency. The feedback control module initiates a preset algorithm program that integrates rich clinical data and experimental research results. It automatically analyzes the signal characteristics of the target spike-and-wave complex, obtains the user's specific condition, the corresponding target attack time, target amplitude, and target duration, intelligently adjusts the stimulus type, stimulus intensity, and stimulus duration, and sets a delay time. It then generates a sound stimulus trigger command based on the stimulus type, stimulus intensity, and stimulus duration to ensure that the intervention effectively suppresses abnormal discharges while avoiding unnecessary interference to the user.

[0072] For example, the stimulus type may include pink noise, white noise, pure tones of a specific frequency, etc. Understandably, the stimulus parameters can be adaptively adjusted according to individual user differences, such as hearing threshold and attack frequency, allowing for stimulus forms that better suit the patient's personal preferences and promoting treatment adherence.

[0073] Preferably, the stimulus type is pink noise, the stimulus duration is 50ms, and the stimulus intensity is 5-20dB above the user's hearing threshold. More preferably, the stimulus intensity is 12dB above the user's hearing threshold. Among these, pink noise, compared to white noise, has an energy distribution that conforms to the characteristics of human auditory perception, resulting in higher user acceptance.

[0074] The delay duration is the interval between the peak of the spike component of the characteristic spike-and-wave complex and the issuance of the stimulation command. It is used to instruct the second communication module to issue a sound stimulation trigger command after the delay duration. This delay duration avoids the ineffective intervention caused by the synchronization of stimulation and spike-and-wave complex emission, and ensures that stimulation is triggered in the early stage of absence seizure, so as to suppress the progression of the seizure to the greatest extent and ensure the effectiveness and specificity of the stimulation.

[0075] For example, the delay duration is a fixed time of 50-100ms.

[0076] The second communication module is also used to send the sound stimulus trigger command to the in-ear headphone device via wireless transmission technology after a delay period.

[0077] Furthermore, in-ear headphone devices generate precise acoustic signals based on sound stimulation trigger commands. This stimulation mode can not only effectively suppress abnormal brain electrical activity, but also has the advantages of being non-invasive and low-risk, providing an innovative and effective means for the treatment of absence epilepsy.

[0078] The in-ear headphone device described in this application is lightweight and compact, suitable for long-term wear in daily life, study, and work, greatly improving user compliance. Based on the in-ear headphone device, combined with external control equipment, stimulation parameters can be dynamically adjusted according to the user's brainwave patterns, and the detection model can be continuously optimized through machine learning algorithms to adapt to the differences in the conditions of different users. On this basis, a millisecond-level closed-loop link of "acquisition-detection-decision-stimulation" is constructed, which can accurately intervene in the early stage of absence seizures, effectively interrupting or inhibiting seizures. The entire process uses in-ear brainwave acquisition and sound stimulation to achieve non-invasive detection and intervention, without drug side effects, surgical risks, and good user tolerance, making it suitable for special populations such as children and adolescents.

[0079] Specifically, to ensure "collection" Detection decision making To achieve millisecond-level real-time performance of the "stimulus" closed loop, the system hardware is designed as follows: Low-latency wireless communication: The first and second communication modules adopt Bluetooth 5.0 and above in low-power, high-data-rate mode, and optimize the communication protocol stack to control the one-way transmission delay to within 10ms.

[0080] Local preprocessing and feature buffering: The first signal processing module of the in-ear headphone device has preliminary hardware filtering and feature detection capabilities (such as simple over-threshold detection). When a suspected spike event is detected, it can immediately send a high-priority interrupt or tag data packet to the external control device to reduce invalid data transmission and wake-up delay.

[0081] Dedicated processing acceleration: The processing unit of the external control device, if it is a dedicated embedded system, may include a digital signal processor (DSP) or a neural network processing unit (NPU) to accelerate the operation of convolutional neural network (CNN) models or time-frequency analysis algorithms, ensuring that feature recognition and stimulus instructions are completed within 50ms after receiving data.

[0082] Furthermore, within the millisecond-level closed-loop link, the feedback control module of this application, through reasonable configuration of delay duration, stimulation duration, and stimulation parameters, achieves an alternating "detection-delay-stimulation" phase for each intervention cycle. The duration of the detection phase is dynamically adjusted based on real-time changes in the EEG signal; the delay phase employs a randomized design to optimize intervention timing; and the stimulation phase delivers acoustic pulses in the form of short-duration bursts. By reasonably configuring the duration ratio and stimulation parameters of each phase, the inhibitory effect of neural modulation can be optimized, while significantly reducing energy consumption and improving device endurance.

[0083] In another possible implementation, the external control device also includes a data storage and management module.

[0084] The data storage and management module stores the labeling information, corresponding EEG signals, stimulation parameters, and delay duration of spike-and-wave events. The labeling information includes the detection time and duration.

[0085] Specifically, upon detecting a spike-and-wave event, the data storage and management unit stores the EEG signal of the spike-and-wave event, the detection time and duration of the event, the corresponding stimulation parameters, delay duration, and system logs in the local storage of the external control device. Furthermore, through remote transmission to the medical cloud platform and push to medical staff terminals, and cloud backup on the medical cloud platform, not only is a dual storage mode of local and cloud storage achieved, ensuring data security and complete recording of attack and intervention data, but it also provides objective evidence for medical staff to evaluate treatment efficacy and adjust treatment plans, thus realizing "precision medicine."

[0086] The data storage and management module also supports data visualization. Specifically, it displays the waveform of the EEG signal, marked spike-and-wave events, and corresponding stimulation parameter adjustment trajectories in real time on an external control device.

[0087] The data storage and management module also supports historical data review. Specifically, when medical staff need to view a user's condition, they can query attack records and intervention records by time dimension.

[0088] In another possible implementation, medical staff can manually adjust the weighting of several parameters by viewing the marked spike-and-wave events in the early stages of treatment. This allows them to prioritize detection sensitivity to ensure efficacy in the early stages of treatment, while focusing on adjusting the stimulation intensity to optimize comfort during the stable period, thus forming a dual guarantee system of "automatic response - manual calibration".

[0089] Figure 3 This diagram illustrates a method for detecting electroencephalogram (EEG) signals and providing auditory feedback, as provided in an embodiment of this disclosure. This method can be executed by a non-invasive brain-computer interface system based on real-time EEG monitoring via headphones and auditory stimulation. Figure 3 As shown, the method includes: S301. Collect the user's brainwave signals.

[0090] S302. Detect whether there are characteristic spike-and-wave signals corresponding to absence epileptic seizures in the electroencephalogram (EEG) signals.

[0091] S303. When a characteristic spike-and-wave signal corresponding to absence epilepsy is detected, the target spike-and-wave signal of the EEG signal is extracted, and a sound stimulus triggering command is generated based on the spike component of the target spike-and-wave signal.

[0092] Sound stimulus triggering instructions are used to instruct the application of sound stimuli to the user, and the sound stimulus triggering instructions contain stimulus parameters.

[0093] S304. Generate a sound stimulation signal based on the sound stimulation trigger command and send it to the user's ear for sound stimulation.

[0094] This application proposes a method based on EEG signal detection and auditory feedback, combined with in-ear headphones and external control devices, to collect and detect the user's brainwaves in real time. It reacts instantly during absence seizures and can dynamically adjust stimulation parameters based on the user's EEG activity to adapt to different users' conditions. By applying sound stimulation to the user, abnormal brainwave activity is effectively suppressed. The millisecond-level closed-loop chain of "collection-detection-decision-stimulation" constructed in this application allows for precise intervention in the early stages of absence seizures, effectively interrupting or suppressing them. The entire process uses in-ear EEG acquisition and sound stimulation, achieving non-invasive detection and intervention, without drug side effects or surgical risks, and is well-tolerated by users, making it suitable for special populations such as children and adolescents.

[0095] Figure 4 This diagram illustrates another method for EEG signal detection and auditory feedback provided in an embodiment of this disclosure. This method can be executed by a non-invasive brain-computer interface system based on real-time EEG monitoring via headphones and auditory stimulation. Figure 4 As shown, the method includes: S401. Collect the user's brainwave signals.

[0096] S402. Process the brainwave signals.

[0097] Specifically, the brainwave signal is amplified, the filter is used to filter the brainwave signal to remove high-frequency noise, and the analog-to-digital converter is used to convert the brainwave signal from an analog signal to a data signal.

[0098] S403: The processed brainwave signal is sent to an external control device via wireless transmission technology.

[0099] S404 receives sound stimulus trigger commands via wireless transmission technology.

[0100] S405. Generate a stimulus adjustment scheme based on the stimulus parameters in the sound stimulus triggering command, and generate a corresponding sound stimulus signal according to the stimulus adjustment scheme.

[0101] S406. The sound stimulation signal is sent to the user's ear to provide sound stimulation.

[0102] The proposed method, based on EEG signal detection and auditory feedback, utilizes a lightweight and compact in-ear headphone device suitable for long-term wear in daily life, study, and work, greatly improving portability and comfort. The in-ear headphone device collects the user's EEG data in real time and applies sound stimulation, employing non-invasive in-ear acquisition and non-invasive sound stimulation throughout. This effectively suppresses abnormal EEG activity while achieving non-invasive detection and intervention, with no drug side effects, no surgical risks, and good user tolerance, making it suitable for special populations such as children and adolescents.

[0103] Figure 5 This is a schematic diagram of another method for detecting electroencephalogram (EEG) signals and providing auditory feedback, provided in an embodiment of this disclosure. This method can be executed by an external control device. Figure 5 As shown, the method includes: S501 receives brainwave signals via wireless transmission technology.

[0104] S502. The EEG signal is sequentially filtered, filtered at power frequency, and processed by artifact removal to retain the target frequency band EEG signal containing spike-and-wave characteristics.

[0105] Specifically, the frequency and amplitude of the EEG signal are identified, and based on the frequency and amplitude, it is determined whether there are characteristic spike-and-wave discharges corresponding to absence epilepsy in the EEG signal; and / or the EEG signal is input into a convolutional neural network model to determine whether there are characteristic spike-and-wave discharges corresponding to absence epilepsy in the EEG signal.

[0106] More specifically, the time-frequency analysis method is used to calculate the target power value of the EEG signal within a preset frequency band. The preset spike-and-wave frequency band is 1Hz-4Hz, and the preset spike component frequency band is 14Hz-50Hz. The duration for which the target power value is greater than the preset power value is calculated. When the duration is greater than the preset duration, it is determined that there is a characteristic spike-and-wave discharge corresponding to absence epilepsy in the EEG signal, and it is identified as a spike-and-wave event. At the same time as identifying it as a spike-and-wave event, the timing characteristics of the spike discharge of the event are output in association.

[0107] S503. Detect whether there are characteristic spike-and-wave discharges corresponding to absence epilepsy in the processed EEG signal. When characteristic spike-and-wave discharges corresponding to absence epilepsy are detected, extract the corresponding target spike-and-wave.

[0108] S504. Determine the stimulation parameters and delay duration based on the spike component of the target spike-slow wave, and generate a sound stimulus trigger command based on the stimulation parameters.

[0109] Specifically, target factors of the target spike-and-wave complex are extracted, including target amplitude, target duration, and target attack frequency. Stimulation parameters and delay duration are determined based on the target factors, and sound stimulus triggering instructions are generated based on the stimulation parameters, including stimulus type, stimulus intensity, and stimulus duration.

[0110] S505: After a delay, the sound stimulus trigger command is sent to the earphone device via wireless transmission technology.

[0111] The non-invasive brain-computer interface system based on real-time EEG monitoring and auditory stimulation proposed in this application can detect the user's brainwaves in real time through external control devices and react instantly when pathological events occur. It can dynamically adjust stimulation parameters according to the user's brainwaves to adapt to the differences in the condition of different users. It can accurately intervene in the early stage of absence seizures, effectively interrupt or inhibit seizures, which is more in line with the concept of precision treatment of neuromodulation.

[0112] To implement the above embodiments, this disclosure also proposes a computer program product, including a computer program / instructions, which, when executed by a processor, implements any of the EEG signal detection and auditory feedback methods in the above embodiments.

[0113] Figure 6 This is a schematic diagram of the structure of a computing device provided in an embodiment of the present disclosure.

[0114] The following is a detailed reference. Figure 6 The diagram illustrates a structural schematic suitable for implementing the computing device 600 in the embodiments of this disclosure. The computing device 600 in the embodiments of this disclosure may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 6 The computing device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0115] like Figure 6As shown, computing device 600 may include a processor (e.g., central processing unit, graphics processor, etc.) 601, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 602 or a program loaded from memory 608 into random access memory (RAM) 603. RAM 603 also stores various programs and data required for the operation of computing device 600. Processor 601, ROM 602, and RAM 603 are interconnected via bus 604. Input / output (I / O) interface 605 is also connected to bus 604.

[0116] Typically, the following devices can be connected to I / O interface 605: input devices 606 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 607 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 608 including, for example, magnetic tapes, hard disks, etc.; and communication devices 609. Communication device 609 allows computing device 600 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 6 A computing device 600 with various devices is shown, but it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have alternatively.

[0117] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 609, or installed from a memory 608, or installed from a ROM 602. When the computer program is executed by the processor 601, it performs the functions defined in any of the EEG signal detection and auditory feedback methods of embodiments of this disclosure.

[0118] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0119] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0120] The aforementioned computer-readable medium may be included in the aforementioned computing device; or it may exist independently and not assembled into the computing device.

[0121] The aforementioned computer-readable medium carries one or more programs, which, when executed by the computing device, cause the computing device to perform any of the aforementioned electroencephalogram (EEG) signal detection and auditory feedback methods.

[0122] The computing device can be programmed with computer program code in one or more programming languages ​​or a combination thereof to perform the operations of this disclosure. These programming languages ​​include, but are not limited to, object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0123] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0124] The units described in the embodiments of this disclosure can be implemented in software or hardware. The names of the units are not, in some cases, intended to limit the specific unit.

[0125] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0126] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0127] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

[0128] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain environments, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0129] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A non-invasive brain-computer interface system based on real-time EEG monitoring and auditory stimulation via headphones, characterized in that, The system includes an in-ear headphone device and an external control device, wherein... The in-ear headphone device is used to collect the user's brainwave signals, transmit the brainwave signals to the external control device, and generate sound stimulation signals based on sound stimulation trigger commands and send them to the user's ears for sound stimulation. The external control device is used to detect whether there is a characteristic spike-and-wave signal corresponding to absence epilepsy in the electroencephalogram (EEG) signal. When a characteristic spike-and-wave signal corresponding to absence epilepsy is detected, the target spike-and-wave signal of the EEG signal is extracted, and the sound stimulation trigger command is generated based on the spike component of the target spike-and-wave signal. The sound stimulation trigger command is then transmitted to the in-ear headphone device.

2. The non-invasive brain-computer interface system based on real-time EEG monitoring and auditory stimulation via headphones according to claim 1, characterized in that, The in-ear headphone device includes a signal acquisition module, a first signal processing module, a first communication module, an auditory feedback module, and a stimulus output port module, wherein... The signal acquisition module is used to acquire the user's electroencephalogram (EEG) signals; The first signal processing module is used to process the electroencephalogram (EEG) signal; The first communication module is used to send the processed EEG signal to the external control device via wireless transmission technology and to receive the sound stimulus trigger command via wireless transmission technology. The auditory feedback module is used to generate a stimulus adjustment scheme based on the stimulus parameters in the sound stimulus triggering command, and to generate a corresponding sound stimulus signal according to the stimulus adjustment scheme. The stimulation output port module is used to send the sound stimulation signal to the user's ear for sound stimulation.

3. The non-invasive brain-computer interface system based on real-time EEG monitoring and auditory stimulation via headphones according to claim 1, characterized in that, The external control device includes a second communication module, a second signal processing module, a feature detection and recognition module, and a feedback control module, wherein... The second communication module is used to receive the brainwave signals via wireless transmission technology; The second signal processing module is used to sequentially perform filtering, power frequency notch filtering, and artifact removal processing on the EEG signal to retain the target frequency band EEG signal containing spike-and-wave characteristics in the EEG signal; The feature detection and recognition module is used to detect whether there are characteristic spike-and-wave discharges corresponding to absence epileptic seizures in the processed EEG signal. When the characteristic spike-and-wave discharges corresponding to absence epileptic seizures are detected, the spike component of the corresponding target spike-and-wave is extracted. The feedback control module is used to determine the stimulation parameters and delay duration based on the spike component of the target spike-wave, and to generate the sound stimulation trigger command based on the stimulation parameters. The second communication module is also used to send the sound stimulus trigger command to the earphone device via wireless transmission technology after the delay period.

4. The non-invasive brain-computer interface system based on real-time EEG monitoring and auditory stimulation via headphones according to claim 3, characterized in that, The feature detection and recognition module is specifically used to identify the frequency and amplitude of the EEG signal, and based on the frequency and amplitude, determine whether there are characteristic spike-and-wave discharges corresponding to absence seizures in the EEG signal; and / or The EEG signal is input into a convolutional neural network model to determine whether there are characteristic spike-and-wave discharges corresponding to absence epilepsy seizures in the EEG signal.

5. The non-invasive brain-computer interface system based on real-time EEG monitoring and auditory stimulation via headphones according to claim 4, characterized in that, The feature detection and recognition module includes a feature extraction submodule and a pattern recognition submodule, wherein, The feature extraction submodule is used to calculate the target power value of the EEG signal in a preset spike-and-slow-wave frequency band using time-frequency analysis. The pattern recognition submodule is used to calculate the duration for which the target power value in the preset spike-and-wave frequency band is greater than the preset power value; when the duration is greater than the preset duration, it is determined that there is a characteristic spike-and-wave discharge corresponding to absence epilepsy in the EEG signal, and it is identified as a spike-and-wave event; after the spike-and-wave event is identified, the spike discharge time feature of the corresponding target spike-and-wave is extracted.

6. The non-invasive brain-computer interface system based on real-time EEG monitoring and auditory stimulation via headphones according to claim 3, characterized in that, The feedback control module includes a factor extraction submodule and an instruction generation submodule, wherein, The factor extraction submodule is used to extract the target factors of the target spike-and-wave, including the target amplitude, target duration, and target attack frequency. The instruction generation submodule is used to determine the stimulation parameters and delay duration based on the target factors, and to generate the sound stimulation trigger instruction based on the stimulation parameters, wherein the stimulation parameters include stimulation type, stimulation intensity and stimulation duration.

7. The non-invasive brain-computer interface system based on real-time EEG monitoring and auditory stimulation via headphones according to claim 2, characterized in that, The in-ear headphone device includes a left EEG acquisition earpiece and a right EEG acquisition earpiece, each with a built-in signal acquisition module. The signal acquisition module includes a first EEG recording electrode and a second EEG recording electrode. The first EEG recording electrode is located in the concha cavity and is used to collect the user's brain wave signals in the temporal lobe direction; The second EEG recording electrode is located on the earlobe and serves as a reference electrode.

8. The non-invasive brain-computer interface system based on real-time EEG monitoring and auditory stimulation via headphones according to claim 7, characterized in that, The first signal processing module includes an analog front-end amplifier, a filter, and an analog-to-digital converter, wherein, The analog front-end amplifier is used to amplify the electroencephalogram (EEG) signal; The filter is used to filter the brainwave signal and remove high-frequency noise; The analog-to-digital converter is used to convert the electroencephalogram (EEG) signal from an analog signal to a digital signal.

9. The non-invasive brain-computer interface system based on real-time EEG monitoring and auditory stimulation via headphones according to claim 3, characterized in that, The external control device also includes a data storage and management module, wherein, The data storage and management module is used to store the labeling information of the spike-and-wave event, the corresponding EEG signal, the stimulation parameters, and the delay duration; wherein, the labeling information includes the detection time and duration.

10. A method for detecting electroencephalogram (EEG) signals and providing auditory feedback, applied to the non-invasive brain-computer interface system based on real-time EEG monitoring and auditory stimulation via headphones as described in any one of claims 1-9, characterized in that, The method includes: Collect the user's brainwave signals; The presence of characteristic spike-and-wave signals corresponding to absence epileptic seizures was detected in the electroencephalogram (EEG) signals. When a characteristic spike-and-wave signal corresponding to absence epilepsy is detected, the target spike-and-wave of the EEG signal is extracted, and a sound stimulus trigger command is generated based on the spike component of the target spike-and-wave. Based on the sound stimulus trigger command, a sound stimulus signal is generated and sent to the user's ear for sound stimulation.

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