Method for identifying electroencephalogram signals and realizing real-time sound feedback
By combining EEG signal acquisition equipment with a preset program, real-time audio feedback of EEG signals is achieved, solving the problem of efficient separation and real-time conversion of EEG signals in existing technologies, and providing efficient audio feedback and diagnostic guidance.
Patent Information
- Application Number
- CN202511527959.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-24
- Publication Date
- 2025-12-26
AI Technical Summary
Current EEG technology lacks real-time speech-level analysis capabilities. Traditional sound feedback systems struggle to directly respond to user intentions. Significant individual differences in EEG signals, high signal noise, and delays in real-time speech synthesis make efficient separation of EEG signals and conversion and transmission of filtered signals into audio signals difficult.
By combining an EEG signal acquisition device with a pre-set specific program and using a low-power WiFi/BT transmission module, EEG signals are transmitted to a computer or mobile phone in real time. The Butterworth algorithm and envelope signal processing program are used to filter, separate, and amplify signals of different frequencies, and convert them into audio signals in real time, thereby achieving effective discrimination and real-time feedback of EEG signals.
It achieves efficient separation of EEG signals and accurate audio recognition, eliminating noise interference and delay in traditional technologies, providing real-time audio feedback, guiding human intelligence development and medical diagnosis, and enhancing learning ability and the effects of rest and meditation.
Smart Images
Figure CN121196567A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the fields of brain-computer interface (BCI) technology, bioelectric signal processing, and real-time audio feedback, and specifically relates to a method for recognizing brain signals and realizing real-time sound feedback. Background Technology
[0002] For a long time, most researchers and medical professionals believed that alpha waves in EEG signals were merely one type of EEG signal commonly seen in normal individuals, and had little relevance to disease diagnosis or subconscious brain activity. However, with advancements in technology, EEG scholars, psychologists, and sociologists abroad have begun to recognize the value of alpha waves. EEG signals, as electrophysiological signals of brain activity, possess rich information content, including high temporal resolution, and are widely used in fields such as emotion recognition, speech synthesis, and human-computer interaction. In recent years, the development of Brain-Computer Interface (BCI) technology has provided new possibilities for the recognition and application of EEG signals. Alpha waves and Theta waves are two different EEG states corresponding to different physiological and psychological functions; the combined effect of these two types of brain waves can promote mental and physical balance. Alpha waves are one of the four basic brain waves, often referring to a subconscious state. Scientific research has found that when alpha waves are active in brain signals, the brain is in a state of deep relaxation, quiet relaxation, and not fully asleep. This easily triggers deep memories in the cerebral cortex, helping to consolidate long-term memories. Simultaneously, in this state, thinking is clear and creative, effectively improving concentration and learning efficiency, and enhancing emotional management, thus promoting the formation of long-term memories. It is often used in hypnotic therapy or creative brainstorming. Theta waves indicate that the brain is in a state of extremely deep relaxation and stress-free subconsciousness. When a person's brain frequency is at the Theta wave level, consciousness is interrupted, the body is deeply relaxed, and they are highly suggestible to external information, i.e., in a hypnotic state. Theta waves are extremely helpful in triggering deep memories and strengthening long-term memories; therefore, Theta waves are called the "gateway to memory and learning."
[0003] The effective use of electroencephalography (EEG) is gaining increasing attention in fields such as medicine. However, current EEG technology is mostly used for medical diagnosis (such as epilepsy monitoring) or simple device control (such as wheelchair operation), lacking the ability to perform real-time, speech-level analysis of EEG signals. Traditional audio feedback systems rely on physical input methods such as microphones, making it difficult, if not impossible, to achieve a direct response to the user's intentions. Furthermore, due to significant individual differences, high signal noise, and high real-time speech synthesis delays, it is difficult to achieve efficient separation of EEG signals and the actual conversion and transmission of filtered signals into audio signals. This hinders real-time feedback of brain signals and rapid, real-time diagnosis of diseases using brain-computer interfaces (BCIs). Summary of the Invention
[0004] To address the problems existing in the prior art, this invention provides a method for recognizing brain signals and achieving real-time audio feedback. The method converts the acquired raw brain signals into audio in real time through a pre-set specific program, thereby achieving effective differentiation and real-time monitoring of brain activity signals.
[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for recognizing electroencephalogram (EEG) signals and providing real-time sound feedback is implemented through the following steps: (1) Establish a connection between the EEG signal acquisition device and the subject; (2) Connect the power supply of the acquisition device to acquire signals. At the same time, the acquired raw EEG signals are transmitted in real time to the computer host or mobile phone user terminal via the wireless transmission module in the acquisition device. (3) The collected raw EEG signals are processed by a computer host or mobile phone user terminal through a preset specific program, the signals of different frequencies are filtered and separated and the signals are amplified, and the electrical signals are converted into audio signal intensity in real time through mode conversion, thereby obtaining the real-time brainwave activity state of the subject.
[0006] According to the method for recognizing EEG signals and realizing real-time sound feedback, the EEG signal acquisition device in steps (1) and (2) consists of acquisition electrodes, signal acquisition card / chip, signal transmission port and power module, etc.
[0007] According to the method for recognizing EEG signals and realizing real-time sound feedback, the signal acquisition device channel in step (1) can be set to either single-channel acquisition or multi-channel acquisition.
[0008] According to the method for recognizing EEG signals and realizing real-time sound feedback, the wireless transmission module in step (2) adopts a low-power WiFi / BT transmission module.
[0009] According to the method for recognizing EEG signals and realizing real-time sound feedback, the preset specific processing program in step (3) is selected as Butterworth algorithm or envelope signal processing program to complete the system's real-time visualization, audio feedback, data storage and other processing of the original EEG signal.
[0010] According to the method for recognizing EEG signals and realizing real-time sound feedback, the processing program can amplify or reduce the specific band signals separated and captured by the serial data while performing real-time visualization of the original signal and audio feedback processing. According to the method for recognizing EEG signals and realizing real-time sound feedback, the formula for the spectral amplitude and frequency corresponding to the Butterworth algorithm of the specific processing program can be expressed as Equation ①: -----Form ① In the formula, each letter represents: n - the filter order; ω c - Cutoff frequency; ω p -Passband edge frequencies; According to the method for recognizing EEG signals and realizing real-time sound feedback, the system performs real-time visualization processing and can display EEG signal waveforms, 8-12 Hz Alpha wave waveforms, and 4-7 Hz Theta wave waveforms in real time. The information displayed on the system's main page in real time also includes the real-time volume changes of the two audio signals corresponding to Alpha and Theta, and the real-time acquisition status.
[0011] According to the method for recognizing EEG signals and realizing real-time sound feedback, the system audio feedback is based on the envelope processing signal of Alpha wave and Theta wave. After the envelope value is normalized, the relationship curve between the change in audio volume and the change in band intensity is mapped to the exponential domain. According to the method for recognizing EEG signals and realizing real-time sound feedback, the system's audio feedback is implemented in the form of volume, and the audio volume modulation formula can be expressed as equation ② and / or equation ③: ----Form ② ----Form ③
[0012] According to the method for recognizing EEG signals and realizing real-time sound feedback, in Equations ② and ③, Vol(α) and Vol(θ) represent the audio volume of α waves and θ waves, respectively; A(α) and A(θ) represent the real-time envelope amplitude of α waves and θ waves, respectively; and v(α) and v(θ) represent the amplitude of α waves and θ waves, respectively.
[0013] According to the method for recognizing EEG signals and realizing real-time sound feedback, v(α) and v(θ) in Equations ② and ③ are adjustable parameters in the system program, and the value range of v(α) can be 20-60µV and the value range of v(θ) can be 10-50µV. When the alpha / theta amplitude value obtained after filtering the original EEG signal is low or high, it will cause the real-time feedback audio volume to be weak or strong, the output audio to fluctuate greatly, and the audio volume to be inconsistent. At this time, the system can automatically adjust the v(α) and / or v(θ) parameters by setting them to decrease or increase the value of v(α) and / or v(θ) so that the audio intensity corresponding to the alpha / theta amplitude value is maintained at a stable level and the output audio is soothing.
[0014] Furthermore, when v(α) and v(θ) take their maximum values of v(α) = 60µV and v(θ) = 50µV respectively in equations ② and ③, the formulas for the real-time audio volume feedback of the system can be expressed as follows: ----Form ②, ----Form ③, Furthermore, the data storage is performed as follows: after clicking the system start recording button, the collected data is stored in four columns: time, original wave, alpha, and theta; after clicking the stop button, the collected data is automatically saved as a replayable .csv file named after the time.
[0015] Beneficial effects of this invention: This invention processes the raw EEG signals through a pre-set specific program to achieve efficient separation, accurate audio recognition, and high-pass processing of the raw EEG spectrum. It also instantly adjusts the amplitude parameters v(α) and / or v(θ) of the captured audio signal, effectively eliminating interference noise and the sound delay effect of traditional techniques, and outputting soothing audio in real time. This is beneficial for guiding activities such as human intelligence development, medical diagnosis, learning ability enhancement, and rest and meditation. Attached Figure Description
[0016] Figure 1 This is a simplified schematic diagram of the data processing flow of the brain-computer interface technology involved in this invention; Figure 2 The original EEG signal spectrum of the participants in the brain-computer interface information collection (top image), and the Alpha spectrum (middle image) and Theta spectrum (bottom image) of the brain waves obtained by Butterworth filtering of the corresponding serial data. Figure 3 For the blink peak logo Figure 2 Electroencephalogram (EEG) signals are used to explain Figure 2 The signal has the characteristic of real-time acquisition; Figure 4 The original electroencephalograms (EEGs) of participants A, B, and C are shown. Figure 5 The image shows the Alpha wave envelope diagrams in the brainwaves of test participants A, B, and C, mapped from serial data via envelope curves. Figure 6 The Theta wave envelope diagrams for participants A, B, and C are shown below. Detailed Implementation
[0017] To further explain and illustrate the beneficial effects of the present invention, specific embodiments are described below. The brain-computer interface technology data processing flow involved in the embodiments of the present invention all adopts... Figure 1 The diagram shown is shown in the image. Example 1
[0018] (1) The EEG signal acquisition device is fixed around the brain of the test subject A, with the signal acquisition chip positioned above the left eye on the forehead pole, and the power is turned on. (2) The original EEG signal of the subject is transmitted to the computer host in real time through the low-power WiFi / BT wireless transmission module of the device signal transmitter; (3) The serial signal is processed by the Butterworth algorithm (the calculation formula is shown in equation ①) of the computer host, and the 8-12 Hz band Alpha wave spectrum is collected in real time after filtering and separation. Figure 2 (Middle image) and the 4-7 Hz Theta wave spectrum Figure 2 (See image below).
[0019] -----Form ①; To further characterize the brain-computer interface of this invention, which can realize the recognition and conversion of electroencephalogram (EEG) signals and achieve real-time sound feedback, in order to participate in the diagnosis of subject A's random blink peaks (such as... Figure 3 )mark.
[0020] (4) Based on the system's preset envelope signal program, after normalizing the envelope values of Alpha and Theta waves, the relationship curves between the changes in audio volume and band intensity of Alpha and Theta waves are mapped to the exponential domain. According to the EEG signal of participant A at this time, the real-time envelope amplitude A(α) corresponding to Alpha wave generally falls within the medium to strong intensity range, and the real-time envelope amplitude A(θ) corresponding to Theta wave generally falls within the weak intensity range (e.g., Figure 4 As shown in the figure, in order to keep the audio volume change corresponding to the change in alpha / theta wave amplitude at a stable level and output a soothing audio, the system parameter setting range of v(α) in the corresponding Alpha wave and Theta wave audio volume modulation formulas ② and ③ is 30-40µV; the system parameter setting range of v(θ) is 10-30µV.
[0021] ----Form ②, ----Form ③, Simultaneously, this brain-computer interface technology can be selected as either the Butterworth algorithm or the envelope signal processing program through a preset specific processing program to complete the real-time visualization, audio feedback, data storage, and other processing of the raw EEG signals. Example 2
[0022] Similar to Example 1, the EEG signal acquisition device is fixed around the brain of the patient B, with the signal acquisition chip positioned at the forehead pole. The power is turned on to acquire EEG signals, which are then transmitted in real time to a mobile phone user terminal with a specific processing program. The information displayed on the system's main page after system processing also includes the real-time volume changes of the two audio frequencies corresponding to Alpha and Theta, as well as the real-time acquisition status.
[0023] Based on the system's preset envelope signal program, after normalizing the envelope values of the Alpha and Theta waves, the relationship curves between the changes in audio volume and band intensity of the Alpha and Theta waves are mapped to the exponential domain. According to the real-time envelope amplitudes A(α) and A(θ) of the Alpha and Theta waves filtered from the EEG signal of participant B at that moment, the overall values fall within the range shown. Figure 5 The intensity range shown indicates that, in order to maintain the audio volume change corresponding to the change in alpha / theta wave amplitude at a stable level and output a soothing audio, the corresponding Alpha wave and Theta wave audio volume modulation formulas ② and ③ have the following system parameter settings: v(α) is set to 20-40µV; v(θ) is set to 10-50µV.
[0024] ----Form ② ----Form ③ Example 3
[0025] Similar to Example 1, the EEG signal acquisition device is fixed around the brain of the test subject C, with the signal acquisition chip positioned at the forehead pole. The power is turned on to acquire EEG signals, which are then transmitted in real time to a mobile phone user terminal with a specific processing program. The information displayed on the system's main page after system processing also includes real-time volume changes of the Alpha and Theta audio signals, real-time acquisition status, etc.
[0026] Based on the system's preset envelope signal program, after normalizing the envelope values of Alpha and Theta waves, the relationship curves between the changes in audio volume and band intensity of Alpha and Theta waves are mapped to the exponential domain. According to the real-time envelope amplitudes A(α) and A(θ) of the Alpha and Theta waves filtered from the EEG signal of participant C at that moment, the overall values fall within the range shown. Figure 6The intensity range shown indicates that, in order to maintain the audio volume change corresponding to the change in alpha / theta wave amplitude at a stable level and output a soothing audio, the corresponding Alpha wave and Theta wave audio volume modulation formulas ② and ③ have the following system parameter settings: v(α) is set to 20-40µV; v(θ) is set to 10-50µV.
[0027] ----Form ② ----Form ③ Based on the fact that alpha waves and theta waves in electroencephalography (EEG) correspond to two different brainwave states representing different physiological and psychological functions, alpha waves typically indicate a subconscious state, while theta waves indicate a deeply relaxed, stress-free subconscious state. Data from tests in Examples 1 to 3 show that in Example 3, participant C exhibited a stronger alpha wave activity during the testing period, followed by participant A in Example 1, and then participant B in Example 2. The activity intensity of theta waves, however, showed a contrasting trend with that of alpha waves.
[0028] The present invention provides a brain-computer interface technology for recognizing and converting brain electrical signals and realizing real-time sound feedback. This technology can not only evaluate the brainwave activity of test participants in real time, which helps guide human social activities towards positive development, but also provide test subjects with clean sound through automatic adjustment of sound wave audio, which helps the participants' physical and mental health.
[0029] The preferred embodiments of the present invention have been described in detail above; however, the present invention is not limited thereto. Within the scope of the inventive concept, various simple modifications can be made to the technical solutions of the present invention, including combinations of various technical features in any other suitable manner. These simple modifications and combinations should also be considered as the content disclosed in the present invention and are all within the protection scope of the present invention.
Claims
1. A method for recognizing electroencephalogram (EEG) signals and achieving real-time sound feedback, characterized in that, Specifically, this is achieved in the following ways: (1) Establish a connection between the EEG signal acquisition device and the subject; (2) Connect the power supply of the EEG signal acquisition device to acquire signals. At the same time, the acquired raw EEG signals are transmitted in real time to the computer host or mobile phone user terminal via the wireless transmission module in the acquisition device. (3) The collected raw EEG signals are processed by a computer host or mobile phone user terminal through a preset specific program, the signals of different frequencies are filtered and separated and the signals are amplified, and the electrical signals are converted into audio signals in real time through mode conversion, thereby obtaining the real-time brainwave activity state of the subject.
2. The method for recognizing electroencephalogram (EEG) signals and realizing real-time sound feedback according to claim 1, characterized in that, The EEG signal acquisition device in steps (1) and (2) includes acquisition electrodes, signal acquisition card / chip, signal transmission port and power module, wherein the signal acquisition channel of the EEG signal acquisition device is set to either single-channel acquisition or multi-channel acquisition.
3. The method for recognizing electroencephalogram (EEG) signals and achieving real-time sound feedback according to claim 1, characterized in that, In step (2), the wireless transmission module adopts a WiFi / BT transmission module.
4. The method for recognizing electroencephalogram (EEG) signals and realizing real-time sound feedback according to claim 1, characterized in that, In step (3), the preset specific processing program is the Butterworth algorithm or the envelope signal processing program, which performs real-time visualization, real-time audio feedback, and real-time data storage processing of the original EEG signal; at the same time, it amplifies or reduces the specific band signals separated and captured by the serial data.
5. The method for recognizing EEG signals and realizing real-time sound feedback according to claim 4, characterized in that, The Butterworth operation formula involved in the specific processing procedure can be expressed as: ， Where: n - filter order; ω c - Cutoff frequency; ω p -Passband edge frequency.
6. The method for recognizing electroencephalogram (EEG) signals and realizing real-time sound feedback according to claim 4, characterized in that, Real-time visualization processing includes the ability to display EEG signal waveforms, 8-12 Hz Alpha wave waveforms, and 4-7 Hz Theta wave waveforms in real time. The real-time visualization also includes real-time volume changes and real-time acquisition status information for the two audio frequencies corresponding to Alpha and Theta.
7. The method for recognizing EEG signals and realizing real-time sound feedback according to claim 4, characterized in that, The real-time audio feedback is based on envelope processing of Alpha and Theta waves. After normalization of the envelope values, the relationship between audio volume change and band intensity change is mapped to the exponential domain. The corresponding relationship can be expressed as: ， ， Where: Vol(α) and Vol(θ) represent the audio volume of the α wave and the θ wave, respectively; A(α) and A(θ) represent the real-time envelope amplitude of the α wave and the θ wave, respectively; v(α) and v(θ) represent the amplitude of the α wave and the θ wave, respectively.
8. The method for recognizing EEG signals and realizing real-time sound feedback according to claim 7, characterized in that, v(α) and v(θ) are adjustable parameters in the system program, with v(α) ranging from 20 to 60 µV and v(θ) ranging from 10 to 50 µV. When the alpha / theta amplitude values obtained after filtering the original EEG signal are low or high, it will cause the real-time feedback audio volume to be weak or strong, resulting in large fluctuations in the output audio and inconsistent audio volume. By setting the v(α) and / or v(θ) parameters in the system, the system can automatically adjust the values of v(α) and / or v(θ) to keep the audio volume changes corresponding to the alpha / theta amplitude changes at a stable level and output soothing audio.
9. The method for recognizing electroencephalogram (EEG) signals and realizing real-time sound feedback according to claim 4, characterized in that, The data is stored in four columns: time, original wave, alpha, and theta, after the system starts recording. After clicking the stop button, the collected data is automatically saved as a replayable .csv file named after the time.