Driver abnormal emotion regulation and control system and method

Abstract

The invention provides a driver abnormal emotion regulation and control system and method, and relates to the technical field of vehicle safety, the system comprises a collection module, an emotion recognition module and a feedback training module, the collection module is used for collecting brain wave signals of a driver, and the emotion recognition module is used for recognizing the emotion state of the driver according to the brain wave signals; and the feedback training module is used for generating a stimulation signal synchronized with the target brain wave of the driver when the emotional state of the driver is abnormal so as to guide the brain wave of the driver to be synchronized with the stimulation signal, thereby adjusting the emotional state of the driver. The method can help the driver to adjust the emotion in time, relieve anger, anxiety, fatigue and other abnormal emotions in the driving process, and ensure the driving safety.

Description

Technical Field

[0001] The present invention relates to the field of vehicle safety technology, and in particular to a system and method for regulating abnormal emotions of a driver. Background Art

[0002] With the increase in the number of cars and the complexity of road traffic conditions, drivers are experiencing more and more abnormal emotions caused by various problems during driving. Especially in scenes such as driving on highways, driving on urban roads, and handling emergencies, drivers may experience abnormal emotional states such as tension, anxiety, or anger, resulting in reduced driving safety. Traditional methods of emotion control (such as drug therapy) can cause side effects to the body, and psychological assistance after driving may be difficult to use for real-time emotion regulation and may lag behind. Summary of the invention

[0003] The purpose of the present invention is to provide a system and method for regulating abnormal emotions of a driver, which is used to solve the problems that the existing emotion control methods will bring side effects to the body and it is difficult to regulate emotions in real time. It can help the driver adjust his emotions in time and alleviate abnormal emotions during driving to ensure driving safety.

[0004] In order to achieve the above-mentioned objectives, in a first aspect, the present invention provides a driver abnormal emotion regulation system, comprising an acquisition module, an emotion recognition module and a feedback training module, the acquisition module being used to acquire the driver's brain wave signals, the emotion recognition module being used to identify the driver's emotional state based on the brain wave signals, and the feedback training module being used to generate a stimulation signal synchronized with the driver's target brain wave when the driver's emotional state is abnormal, so as to guide the driver's brain wave to synchronize with the stimulation signal, thereby regulating the driver's emotional state.

[0005] A driver abnormal emotion regulation system provided by the present invention also includes a virtual simulation cockpit, which is used to simulate a real driving scene.

[0006] A driver abnormal emotion regulation system provided by the present invention also includes a preprocessing module, which is used to preprocess the brain wave signal to remove noise and artifacts.

[0007] According to a driver abnormal emotion regulation system provided by the present invention, the stimulation signal includes at least one of visual stimulation, auditory stimulation, tactile stimulation and olfactory stimulation.

[0008] According to a driver abnormal emotion regulation system provided by the present invention, the brain wave signals include delta wave, theta wave, alpha wave, beta wave and gamma wave brain waves.

[0009] According to a driver abnormal emotion regulation system provided by the present invention, the emotion recognition module is specifically used for:

[0010] Extracting EEG features from EEG signals based on cross-frequency coupling analysis;

[0011] Based on the characteristics of brain waves, the driver's emotional state is identified using a preset deep learning model.

[0012] According to a driver abnormal emotion regulation system provided by the present invention, abnormal emotional states include anger state, anxiety state, and fatigue state;

[0013] When the abnormal emotional state is an anger state, the feedback training module generates a stimulation signal synchronized with the gamma wave brain wave; when the abnormal emotional state is an anxiety state, the feedback training module generates a stimulation signal synchronized with the alpha wave brain wave; when the abnormal emotional state is a fatigue state, the feedback training module generates a stimulation signal synchronized with the beta wave brain wave.

[0014] A driver abnormal emotion regulation system provided according to the present invention also includes a storage and analysis module, which is used to store the driver's brain wave signals, emotional states, and stimulation signals, and analyze and evaluate the regulation effect of the feedback training module.

[0015] According to a driver abnormal emotion regulation system provided by the present invention, the feedback training module is also used to evaluate the synchronization between the stimulation signal and the driver's target brain wave in real time through a phase locking value or a coherence index, and then dynamically adjust the parameters of the stimulation signal according to the synchronization evaluation result.

[0016] In a second aspect, the present invention provides a method for regulating abnormal driver emotions, which is applied to the system for regulating abnormal driver emotions in the first aspect, and the method comprises:

[0017] Collect the driver's brain wave signals;

[0018] Identify the driver’s emotional state based on brain wave signals;

[0019] When the driver's emotional state is abnormal, a stimulation signal synchronized with the driver's target brain waves is generated to guide the driver's brain waves to synchronize with the stimulation signal, thereby adjusting the driver's emotional state.

[0020] The technical solution of the present invention has at least the following technical effects:

[0021] The present invention provides a driver abnormal emotion regulation system and method, which collects the driver's brain wave signals, and then identifies the driver's emotional state through an emotion recognition module based on cross-frequency coupling analysis and a deep learning model. When the driver is identified as being in an abnormal emotional state, synchronous training is triggered through multiple sensory stimulations such as sight, hearing, touch, and smell, helping the driver to regulate emotions in a timely manner and relieve abnormal emotions such as anger, anxiety, and fatigue during driving, so as to ensure driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0023] In the attached picture:

[0024] Figure 1 The figure is a structural block diagram of the driver abnormal emotion regulation system of the present invention. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] Some embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0027] Electroencephalogram (EEG) signals show good operability in monitoring and controlling emotions due to their real-time nature and high sensitivity. According to current clinical practice of biofeedback, brain waves have very good effects in preventing and treating specific diseases.

[0028] Cross Frequency Coupling (CFC) is an emerging neuroscience analysis technology. Its main starting point is to achieve synchronization of brain waves in different frequency bands. It can identify brain activity patterns under different emotional states by analyzing the phase and amplitude coupling of brain waves in different frequency bands.

[0029] Synchronicity training (ET) is a new method of emotion regulation. The idea is to enhance the synchronization of theta, alpha and gamma waves, and ultimately be able to guide attention and relaxation states by regulating the synchronization of brain waves.

[0030] The present invention aims to combine cross-frequency coupling analysis with synchronous training, and provide a virtual driving environment through a virtual simulation cockpit, so as to realize the detection, feedback and regulation of the abnormal emotional state of the driver.

[0031] See also Figure 1An embodiment of the present invention provides a driver abnormal emotion regulation system, including a virtual reality driving cabin (VDC), a collection module, a preprocessing module, an emotion recognition module (ERM), a feedback training module (FTM) and a storage and analysis module.

[0032] The virtual simulation cockpit is used to simulate real driving scenarios through high-fidelity display screens, seats, steering wheels, accelerator and brake pedals, etc., to provide virtual reality driving scenarios including highways, urban roads, emergencies, etc., and simulate various traffic conditions that may cause abnormal emotions in drivers, such as: vehicles cutting in, pedestrians jaywalking, traffic congestion and bad weather, etc., to improve the authenticity of the experimental environment.

[0033] The acquisition module includes an EEG acquisition module (EAM, EEG Acquisition Module), which is used to collect the driver's brain wave signals in real time. In order to obtain more comprehensive and accurate EEG information, multi-channel EEG acquisition equipment (for example, 64-channel, 128-channel or even higher-density electrode caps) can be used to cover a wider range of brain areas. The collected signals include but are not limited to brain waves of various frequency bands such as delta waves, theta waves, alpha waves, beta waves and gamma waves. In addition, in order to more accurately identify the driver's emotional state, the acquisition module may also include other physiological signal acquisition equipment for collecting other physiological signals, such as heart rate, respiratory rate, skin conductance, eye movement, etc., and fusion analysis of these physiological signals with brain wave signals. Of course, the acquisition module needs to have the characteristics of high sampling rate, low noise, and anti-interference to ensure the quality of the collected signals.

[0034] Specifically, according to the common abnormal emotions of drivers, electrodes can be placed in different areas of the brain scalp and the main frequency range can be focused on. The recommended key areas and main wave frequency bands are shown in Table 1.

[0035] Table 1. Recommended key areas and main frequency bands

[0036]

[0037]

[0038] Since there may be a lot of interference in the acquisition environment (such as cockpit environmental noise, electromagnetic wave interference, etc.), the EEG signal needs to be preprocessed by the preprocessing module before analysis. The preprocessing module is used to preprocess the collected EEG signals to remove noise and artifacts and improve signal quality. The preprocessing steps include but are not limited to: filtering (removing noise outside a specific frequency band, using a bandpass filter to filter out low-frequency and high-frequency noise, and retaining valid signals in the range of 0.5 to 100 Hz), notch filtering (removing power frequency interference), artifact removal (for example, using independent component analysis (ICA) to remove electrooculographic artifacts and electromyographic artifacts, retaining valid EEG signals), re-reference (such as average reference, Laplace reference), segmentation and standardization (dividing EEG signals into time windows of fixed length (such as 2 seconds), and performing standardization to reduce the impact of individual differences on subsequent analysis). The preprocessing module can use algorithms such as independent component analysis (ICA), principal component analysis (PCA) and wavelet transform to remove artifacts and reduce noise.

[0039] The emotion recognition module is used to analyze the preprocessed EEG signals and identify the driver's emotional state. Specifically, the emotion recognition module first extracts EEG features based on CFC technology. CFC technology can reflect the interaction between neural activities in different brain regions or different frequency bands, and can reveal the neural mechanism of emotional state in more depth. For example, the phase-amplitude coupling (PAC) between theta wave and the gamma wave, the phase-phase coupling (PPC) between the alpha wave and the beta wave, etc. can be calculated. Then, based on the extracted EEG features, the driver's emotional state is identified using a deep learning model, such as anger, anxiety, fatigue, calmness, concentration, etc. The deep learning model can be a convolutional neural network (CNN), a recurrent neural network (RNN), a long short-term memory network (LSTM), a Transformer, etc., or a combination of multiple deep learning models. In order to improve the accuracy of emotion recognition, a large amount of labeled data is needed to train and optimize the deep learning model. The training data set can be obtained through public EEG emotion data sets, data collected from laboratory simulated driving scenarios, or data collected from actual road driving.

[0040] In some embodiments, the emotion recognition module is specifically used to:

[0041] Based on CFC analysis, feature extraction (such as fast Fourier transform (FFT), wavelet transform (WT)) is used from the preprocessed brain wave signals to obtain brain wave characteristics such as real-time frequency and phase information of different wave frequency bands, and the driver's emotional state (anger / anxiety / fatigue, etc.) is identified using a preset deep learning model.

[0042] Specifically, CFC analysis can identify brain wave features associated with anger, anxiety, and fatigue, etc. For example, increased coupling between theta and gamma or alpha and gamma is often associated with anxious emotions, while high-intensity low-frequency theta wave coupling may reflect states of anger and distraction.

[0043] By analyzing the driver's brain wave characteristics in real time, early signs of emotional fluctuations can be detected. The specific analysis process is as follows:

[0044] Baseline measurement. Record the driver's brain wave baseline before driving to understand the brain wave characteristics of his normal emotional state.

[0045] Emotion detection. By comparing with the EEG baseline data, when abnormal EEG characteristics are detected (such as increased theta-gamma coupling, or weakened alpha wave coupling), it can be identified that the driver is in an abnormal emotional state.

[0046] In the present invention, the deep learning model adopts a structure combining convolutional neural network (CNN) and recurrent neural network (RNN) to improve the emotion recognition ability of time series EEG signals. The specific implementation process is as follows:

[0047] CNN feature extraction: The convolutional neural network extracts features from the preprocessed EEG signal to obtain spatial features of different frequency bands.

[0048] RNN time series modeling. Recurrent neural networks process time series features through sequence modeling and identify emotion change patterns.

[0049] Classification layer: Based on the above features, output the driver’s emotional state category (such as anger, anxiety, fatigue, etc.).

[0050] The feedback training module is used to perform synchronous training and feedback adjustment when the emotional state of the driver identified by the emotion recognition module is abnormal (such as anger, anxiety, fatigue, etc.), that is, to generate a stimulation signal synchronized with the driver's target brain waves to guide the driver's brain waves to synchronize with the stimulation signal, thereby adjusting the driver's emotional state, thereby intervening and guiding the driver. Synchronous training includes personalized customization based on the driver's brain wave characteristics and emotional state.

[0051] Specifically, the feedback training module can generate sound or light stimulation that is synchronized with the driver's target brain wave frequency. For example, if the target brain wave is an activity that enhances alpha waves, a flashing light or pulse sound that is synchronized with the alpha wave frequency (8-13Hz) can be generated. In this way, the driver's brain waves can be guided to gradually synchronize with the stimulation signal, thereby regulating the emotional state, such as improving attention, relieving anxiety, and improving mood. Feedback adjustment methods include but are not limited to:

[0052] Visual feedback: Displaying soothing images or animations on the vehicle’s display, or changing the color and brightness of the interior lighting.

[0053] Auditory feedback: Play soothing music or nature sounds, or provide voice guidance and prompts.

[0054] Haptic feedback: Provide tactile feedback through seat vibration or steering wheel vibration.

[0055] Olfactory Feedback: Releases soothing aroma.

[0056] Combined feedback: Combining multiple feedback methods above.

[0057] Furthermore, when the emotion recognition module identifies that the driver has abnormal emotions, the feedback training module immediately starts synchronous training and generates stimulation signals that are synchronized with the driver's target brain waves. For example, the driver's brain waves are synchronized with the stimulation signals by changing the rhythm of the light in the car, playing sounds with synchronized rhythms, vibrating the seats, and releasing aromatherapy, so as to adjust the driver's emotional state and restore the driver's emotional state to a normal calm state. The specific implementation process is as follows:

[0058] Target brainwave determination

[0059] Based on the results of emotion recognition, the target brain waves and target frequencies that need to be enhanced are determined.

[0060] Anxiety state: Select alpha wave (8-12 Hz) as the target frequency to guide the driver into a stable and relaxed state.

[0061] Angry state: Select gamma waves (30-100 Hz) to help drivers concentrate and enhance self-control.

[0062] Fatigue: Enhance beta waves (14-30Hz) to improve alertness and concentration.

[0063] Stimulus signal generation and presentation

[0064] According to the frequency and phase information of the driver's target brainwave, a stimulation signal is generated that is synchronized with it or has a certain phase difference. For example, if the frequency of the target brainwave is 10Hz, the frequency of the generated auditory stimulation signal is also 10Hz. The introduction of phase difference can enhance the synchronous training effect. For example, the phase of the stimulation signal can be slightly ahead of the phase of the brainwave. In order to effectively guide the driver into a relaxed state and regulate emotions, multi-sensory stimulation (hearing, vision, touch, taste, etc.) is used to generate synchronized interactive feedback stimulation.

[0065] In addition, the parameters of the stimulus signal are dynamically adjusted according to the individual differences and real-time emotional state of the driver, such as:

[0066] Frequency, fine-tune the frequency of the stimulation signal to guide brain waves closer to the target frequency.

[0067] Amplitude / Brightness: adjusts the intensity of the stimulation signal so that it can effectively guide brain waves without causing discomfort.

[0068] Duration: control the duration of the stimulation signal to avoid overstimulation.

[0069] Specifically, the stimulation signal includes at least one of visual stimulation, auditory stimulation, tactile stimulation and olfactory stimulation.

[0070] Auditory stimulation

[0071] Auditory stimulation, also known as sound stimulation, can use white noise of different frequencies, binaural beats, and melodies with specific rhythms to help drivers gradually enter a state of relaxation. Binaural beats play sounds with slightly different frequencies through the left and right ear canals, producing low-frequency alternating rhythms that form a synchronized sense of rhythm in the brain.

[0072] According to the negative emotional state (such as anger, anxiety, fatigue), the corresponding sound frequency range is selected to generate binaural beats of appropriate frequency, and the volume and rhythm are adjusted to make it have a progressive beat intensity to enhance the synchronization effect.

[0073] Anger: The 20-30 Hz beta wave frequency band is often associated with anger, so use low-frequency binaural beats (such as 6-10 Hz).

[0074] Anxiety: Anxiety involves alpha and theta waves, so use binaural beats at a relaxing frequency (4-7 Hz).

[0075] Fatigue: Fatigue is accompanied by an increase in theta waves. Binaural beats in the mid-frequency range (8-10 Hz) can be used to improve alertness.

[0076] Visual stimulation

[0077] Visual stimulation, also known as light stimulation, can affect the synchronization of brain waves through strobe lights or soft light color changes with low-frequency flashes, and effectively promote a state of relaxation in the brain.

[0078] Select the appropriate light frequency according to the negative emotional state, and synchronize the flashing frequency with binaural beats to ensure the rhythm of light and sound is consistent and enhance the integration effect of the stimulation signal.

[0079] Angry emotion: The frequency is set to a low-frequency flash of 8 to 12 times per second.

[0080] Anxiety: The flashing frequency is 4 to 6 times per second, which helps to relax and relieve tension.

[0081] Fatigue mood: Set the flashing frequency to 10 times / second to improve alertness.

[0082] Tactile stimulation

[0083] Tactile stimulation includes vibration stimulation, which provides vibration feedback to the driver's seat or steering wheel through a low-frequency vibration device, helping to relieve tension and anxiety, especially anger and anxiety.

[0084] The vibration frequency and area are selected according to the negative emotional state, and the intensity and frequency of the vibration are dynamically adjusted by the changes in the recognized emotional state. The vibration device is synchronized with the binaural beats and light flashing frequency to form a multi-channel linkage stimulation.

[0085] Angry emotions: Low-frequency vibrations, concentrated on the steering wheel and seat backs, at a frequency of 2 to 3 times per second, help the driver relax tension in the upper limbs and spinal area.

[0086] Anxiety: Vibrates at a medium frequency, concentrated on the sides of the seat and the lower back, at a frequency of 3 to 4 times per second.

[0087] Fatigue: Use short high-frequency vibrations (5 to 6 times per second) at the bottom of the seat to increase alertness.

[0088] Olfactory stimulation

[0089] Olfactory stimulation includes aromatherapy stimulation, which helps drivers relieve anxiety and anger through mild aromas (such as lavender, citrus, and mint), and can also increase drivers' alertness through refreshing smells.

[0090] The type of aromatherapy is selected according to the negative emotional state, and when a negative emotional state is identified, the aromatherapy device automatically releases the corresponding aroma. The concentration of the aromatherapy should be adjusted according to the driver's breathing rate and emotional fluctuations to avoid over-stimulation.

[0091] Anger: Use calming lavender or chamomile to help soothe your nerves.

[0092] Anxiety: Choose citrus scents with soothing effects (such as orange and lemon) to stabilize your emotions.

[0093] Fatigue: Choose refreshing aromatherapy such as mint or rosemary to improve concentration.

[0094] Specifically, the feedback training module is also used to evaluate the synchronization between the stimulation signal and the driver's target brain wave in real time through the phase locking value (PLV) or coherence index, and then dynamically adjust the parameters of the stimulation signal according to the synchronization evaluation results. For example, if the synchronization is low, the stimulation intensity can be increased or the stimulation frequency can be fine-tuned; if the synchronization is high, the stimulation intensity can be reduced or the current parameters can be maintained. This process forms a closed-loop feedback control to continuously optimize the stimulation signal to achieve the best synchronization training effect.

[0095] The higher the PLV value, the better the synchronization effect of brain waves with the target frequency. According to the change of PLV value, adjust the stimulation intensity such as volume and light stimulation brightness, and gradually weaken the stimulation until the PLV value reaches the set threshold (such as above 0.8), indicating that the brain waves are basically synchronized to the target frequency.

[0096] Among them, the calculation method of PLV is:

[0097]

[0098] Among them, θ n is the driver’s current brainwave phase, θ target is the target phase, N is the number of sampling points, and j is the imaginary unit.

[0099] When the PLV value reaches the threshold, the system can remind the driver through voice that "your emotions have stabilized, please continue to maintain it", and gradually weaken the stimulation signals such as audio and light frequency stimulation. When the PLV value is stabilized in the target frequency band, the system will stop the synchronous stimulation and end the synchronous training.

[0100] The storage and analysis module is used to store the driver's brain wave signals, emotional state, stimulation signals and other data, and analyze and evaluate the adjustment effect of the feedback training module. That is, to determine the synchronous training parameters, evaluate the synchronous training effect, and provide a basis for the adjustment and optimization of the personalized synchronous training program. Database technology can be used to manage and analyze the above data and generate visual reports, such as: driver emotion change trends, synchronous training effect evaluation, etc.

[0101] Furthermore, the storage and analysis module is also used to evaluate the overall effect of the synchronous training after the synchronous training is completed. The effect of the synchronous training can be evaluated by comparing the driver's emotional state, brain wave characteristics, and performance in simulated driving scenarios in the virtual simulation cockpit before and after the synchronous training. According to the evaluation results, the synchronous training is optimized, such as adjusting the target brain wave selection strategy, the combination of stimulation modes, and the stimulation parameter adjustment algorithm.

[0102] Based on the same inventive concept, another embodiment of the present invention provides a method for regulating abnormal driver emotions, which is applied to the abnormal driver emotion regulation system of the above embodiment, and the method includes:

[0103] Collect the driver's brain wave signals;

[0104] Identify the driver’s emotional state based on brain wave signals;

[0105] When the driver's emotional state is abnormal, a stimulation signal synchronized with the driver's target brain waves is generated to guide the driver's brain waves to synchronize with the stimulation signal, thereby adjusting the driver's emotional state.

[0106] In summary, the system and method for regulating abnormal emotions of drivers provided by the present invention utilize a virtual simulation cockpit to collect the driver's brain wave signals in a simulated driving environment in real time, and identify the driver's emotional state through cross-frequency coupling analysis and deep learning models. When it is identified that the driver is in an abnormal emotional state, synchronous training is triggered through multiple sensory stimulations such as sight, hearing, touch, and smell, helping the driver to regulate emotions in a timely manner and relieve abnormal emotions such as anger, anxiety, and fatigue during driving, so as to ensure driving safety. It has at least the following advantages:

[0107] 1. Real-time emotional state recognition: Based on the cross-frequency coupling analysis method of brain wave signals, the driver's emotional state can be recognized in real time, and abnormal emotional states such as anger, anxiety, and tension can be distinguished.

[0108] 2. Personalized emotional feedback: Synchronous training is achieved through multi-modal sensory feedback stimulation in the virtual simulation cockpit, providing drivers with personalized emotional feedback and regulation solutions.

[0109] 3. Efficient emotion regulation. Based on synchronous training, the driver's attention and relaxation ability are improved, thereby alleviating abnormal emotions and restoring emotional stability.

[0110] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the embodiments disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art that are not disclosed by the present invention. It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A driver abnormal emotion control system, characterized in that: The system comprises an acquisition module, an emotion recognition module and a feedback training module. The acquisition module is used to acquire the driver's brain wave signals. The emotion recognition module is used to identify the driver's emotional state according to the brain wave signals. The feedback training module is used to generate a stimulation signal synchronized with the driver's target brain wave when the driver's emotional state is abnormal, so as to guide the driver's brain wave to synchronize with the stimulation signal, thereby adjusting the driver's emotional state.

2. The driver abnormal emotion regulation system according to claim 1, characterized in that: It also includes a virtual simulation cockpit, which is used to simulate a real driving scene.

3. The driver abnormal emotion regulation system according to claim 1, characterized in that: It also includes a preprocessing module, which is used to preprocess the brain wave signal to remove noise and artifacts.

4. The driver abnormal emotion regulation system according to claim 1, characterized in that: The stimulation signal includes at least one of visual stimulation, auditory stimulation, tactile stimulation and olfactory stimulation.

5. The driver abnormal emotion regulation system according to claim 4, characterized in that: The brain wave signals include delta wave, theta wave, alpha wave, beta wave and gamma wave brain waves.

6. The driver abnormal emotion regulation system according to claim 5, characterized in that: The emotion recognition module is specifically used for: extracting brain wave features from the brain wave signal based on cross-frequency coupling analysis; According to the brain wave characteristics, the driver's emotional state is identified using a preset deep learning model.

7. The driver abnormal emotion regulation system according to claim 5, characterized in that: The abnormal emotional state includes anger state, anxiety state, and fatigue state; When the abnormal emotional state is an anger state, the feedback training module generates a stimulation signal synchronized with the gamma wave brain wave; when the abnormal emotional state is an anxiety state, the feedback training module generates a stimulation signal synchronized with the alpha wave brain wave; when the abnormal emotional state is a fatigue state, the feedback training module generates a stimulation signal synchronized with the beta wave brain wave.

8. The driver abnormal emotion regulation system according to claim 1, characterized in that: It also includes a storage and analysis module, which is used to store the driver's brain wave signals, emotional states, and stimulation signals, and to analyze and evaluate the adjustment effect of the feedback training module.

9. The driver abnormal emotion regulation system according to claim 1, characterized in that: The feedback training module is also used to evaluate the synchronization between the stimulation signal and the driver's target brain wave in real time through a phase locking value or a coherence index, and then dynamically adjust the parameters of the stimulation signal according to the synchronization evaluation result.

10. A method for regulating abnormal emotions of a driver, characterized in that: Applied to the driver abnormal emotion regulation system according to any one of claims 1 to 9, the method comprises: Collect the driver's brain wave signals; identifying the driver's emotional state according to the brain wave signal; When the driver's emotional state is abnormal, a stimulation signal synchronized with the driver's target brain waves is generated to guide the driver's brain waves to synchronize with the stimulation signal, thereby adjusting the driver's emotional state.

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