Brain-computer interface- and mindfulness meditation-based motion sickness intervention method

Abstract

A brain-computer interface- and mindfulness meditation-based motion sickness intervention method, comprising: acquiring in real time a multi-channel / single-channel electroencephalogram signal of a current user (S1); determining a motion sickness level from the multi-channel / single-channel electroencephalogram signal, wherein the motion sickness level comprises a first level and a second level (S2); when the motion sickness level is the first level, executing a suspend-and-wait operation (S3); and when the motion sickness level is the second level, selecting a meditation feedback scene, and guiding the current user to meditate to relieve a motion sickness symptom (S4).

Description

Motion sickness intervention method based on brain-computer interface mindfulness meditation

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on February 1, 2024, with application number 202410143539.6, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] This application belongs to the field of brain-computer interface technology, for example, to a motion sickness intervention method based on brain-computer interface mindfulness meditation. Background Art

[0003] When riding in a car, boat, or plane, some passengers may experience symptoms of motion sickness such as fainting, nausea, and loss of appetite, causing great distress to the passengers. To solve or prevent motion sickness, the common practice is medication. However, medication can have side effects on passengers, which many passengers are unwilling to accept, and the effectiveness varies from person to person. Passengers can also relieve or prevent motion sickness by wearing special anti-motion sickness glasses, but the effect is not obvious for many passengers. In short, there is no effective method to relieve / prevent motion sickness without side effects.

[0004] Summary of the Invention

[0005] The purpose of this application is to provide a motion sickness intervention method based on brain-computer interface mindfulness meditation to prevent or substantially alleviate the user's motion sickness symptoms when riding in a vehicle.

[0006] To achieve the above objectives, this application provides the following technical solutions: a motion sickness intervention method based on brain-computer interface mindfulness meditation, comprising:

[0007] Real-time collection of the current user's multi-channel / single-channel EEG signals;

[0008] determining a motion sickness level from the multi-channel / single-channel EEG signal, the motion sickness level including a first level and a second level;

[0009] When the motion sickness level is the first level, performing a suspend waiting operation;

[0010] When the motion sickness level is the second level, a meditation feedback scene is selected to guide the current user to meditate to alleviate the motion sickness symptoms.

[0011] As an optional technical solution of the present application, before the real-time acquisition of the multi-channel / single-channel EEG signals of the current user, the method further includes:

[0012] Collecting multi-channel / single-channel EEG signals of historical users as training data, wherein the historical users include non-motion sickness users and motion sickness users;

[0013] constructing a training set, the training set including pairs of training data and user-reported motion sickness symptoms;

[0014] Based on the data pairs, a motion sickness assessment model is constructed.

[0015] As an optional technical solution of the present application, determining the motion sickness level from the multi-channel / single-channel EEG signal includes:

[0016] Inputting the multi-channel / single-channel EEG signal of the current user into the motion sickness assessment model;

[0017] Outputting the motion sickness score of the current user;

[0018] When the motion sickness score is within the first preset interval, determining that the motion sickness level is the first level, indicating that the current user does not experience motion sickness or only has mild motion sickness symptoms;

[0019] When the motion sickness score is within the second preset interval, determining that the motion sickness level is the second level, indicating that the current user has experienced obvious motion sickness symptoms;

[0020] Output the motion sickness level of the current user.

[0021] As an optional technical solution of the present application, outputting the motion sickness score of the current user includes:

[0022] Get multiple preset motion sickness score ranges;

[0023] A target score interval corresponding to the motion sickness score is determined according to the multiple motion sickness score intervals, wherein the multiple score intervals respectively correspond to different motion sickness severities.

[0024] As an optional technical solution of the present application, inputting the multi-channel / single-channel EEG signal of the current user into the motion sickness assessment model includes:

[0025] Preprocessing the multi-channel / single-channel EEG signals of the current user to obtain EEG signal segments to be tested, wherein the preprocessing includes filtering;

[0026] Extracting features from the EEG signal to be tested, wherein the feature extraction includes extracting at least one of the following features: time domain features, frequency domain features, and time-frequency features;

[0027] The result of the feature extraction is input into the motion sickness assessment model.

[0028] As an optional technical solution of the present application, when the motion sickness level is the first level, performing a suspend waiting operation includes:

[0029] Determining that the motion sickness level is the first level;

[0030] Collecting road condition information, including bumpy and congested road conditions and smooth and steady road conditions;

[0031] When the road condition is smooth and stable, re-evaluating the motion sickness level of the current user at preset time intervals;

[0032] In the case of bumpy and congested road conditions, selecting a meditation feedback scene to guide the current user to meditate;

[0033] In the case of bumpy and congested road conditions, selecting a meditation feedback scene to guide the current user to meditate includes:

[0034] Collect gyroscope signals;

[0035] receiving traffic condition information, wherein the traffic condition information includes congestion conditions;

[0036] As an optional technical solution of the present application, when the gyroscope signal meets the preset bumpy condition and the traffic condition information meets the preset condition, a meditation feedback scene is selected to guide the current user to meditate to prevent or relieve motion sickness.

[0037] As an optional technical solution of the present application, when the motion sickness level is the second level, a meditation feedback scene is selected to guide the current user to meditate to alleviate the motion sickness symptoms, including:

[0038] Determining that the motion sickness level is the second level;

[0039] Voice reminding the current user to start meditation;

[0040] Select a meditation feedback scenario;

[0041] Calculating a meditation effect score based on the multi-channel / single-channel EEG signal of the current user;

[0042] updating the meditation feedback scenario according to the meditation effect score;

[0043] The meditation feedback scenario includes at least one of the following: visual feedback, auditory feedback, visual and auditory feedback, olfactory feedback, tactile feedback, and electrical stimulation feedback;

[0044] Before calculating the meditation effect score, also include:

[0045] Collect multi-channel / single-channel EEG signals of historical users in relaxation and meditation states as training data;

[0046] Based on the training data, a meditation level assessment model is constructed.

[0047] As an optional technical solution of this application, the meditation effect score is calculated based on the multi-channel / single-channel EEG signal of the current user, including:

[0048] Inputting the multi-channel / single-channel EEG signal of the current user into a meditation degree assessment model;

[0049] Output the meditation effect score of the current user.

[0050] As an optional technical solution of the present application, updating the meditation feedback scenario according to the meditation effect score includes:

[0051] When the meditation effect score decreases, reducing the expressiveness of the meditation feedback scene;

[0052] When the meditation effect score increases, the expressiveness of the meditation feedback scene is improved.

[0053] As an optional technical solution of the present application, the current user is riding in a vehicle that induces motion sickness, and the vehicle that induces motion sickness includes at least one of the following: a car, a ship, and an airplane. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] FIG1 is a flow chart of a motion sickness intervention method provided in this application. DETAILED DESCRIPTION

[0055] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0056] Please refer to Figure 1. This application provides a motion sickness intervention method based on brain-computer interface mindfulness meditation, including:

[0057] S1, real-time collection of the current user's multi-channel / single-channel EEG signals;

[0058] S2. determining a motion sickness level from the multi-channel / single-channel EEG signal, where the motion sickness level includes a first level and a second level;

[0059] S3. When the motion sickness level is the first level, executing a suspend waiting operation;

[0060] S4. When the motion sickness level is the second level, a meditation feedback scene is selected to guide the current user to meditate to alleviate the motion sickness symptoms.

[0061] Motion sickness levels can be divided into Level 1 and Level 2. Level 1 refers to no motion sickness, or mild but acceptable motion sickness. Level 2 refers to more severe (unbearable) motion sickness.

[0062] The suspend waiting operation refers to an operation of continuing to determine the motion sickness level from the multi-channel / single-channel EEG signal.

[0063] Selecting a meditation feedback scene means choosing from a preset meditation feedback scene. When using the app for the first time, you can select the initial default meditation scene from the meditation feedback scene. Of course, users can customize the meditation feedback scene based on their own usage habits.

[0064] This application proposes a motion sickness warning method based on multi-channel / single-channel EEG signals and real-time traffic information. When the user is detected to be experiencing obvious motion sickness symptoms, or when the current road conditions are bumpy and congested, a voice reminder is provided to the user to begin mindfulness meditation to promptly alleviate or prevent motion sickness. The method has a wide range of applications and is not dependent on a specific mode of transportation. It can be used in scenes prone to motion sickness, such as cars, ships, and airplanes. By determining the level of motion sickness and performing continuous monitoring or guided meditation operations, users can practice mindfulness meditation based on a brain-computer interface while riding in public transportation, receive multi-sensory feedback, adjust their attention targets in real time, relax their body and mind, and effectively prevent and alleviate motion sickness. Compared with related visual compensation and drug intervention methods, the present application has the advantages of no side effects and significant intervention effects.

[0065] In this embodiment, optionally, before collecting the multi-channel / single-channel EEG signals of the current user in real time, the process further includes:

[0066] Collect multi-channel / single-channel EEG signals from historical users as training data. The historical users include both non-motion sickness users and motion sickness users.

[0067] Constructing a training set, the training set includes pairs of training data and user-reported motion sickness symptoms;

[0068] Based on the data pairs, a motion sickness assessment model is constructed.

[0069] The steps to build the motion sickness assessment model are as follows:

[0070] 1) Signal acquisition: Collect multi-channel / single-channel EEG signals from multiple users with and without motion sickness (e.g., 100 users without motion sickness and 100 users with motion sickness) while riding in public transportation. The acquisition equipment includes Neuroscan (EEG / EEG acquisition system) and the South China Brain Control multi-channel / single-channel headband.

[0071] 2) Signal processing:

[0072] 2.1 Preprocessing the collected EEG signals: Divide the collected EEG signals into EEG signal segments of equal length (e.g., 5 seconds), each EEG signal segment constitutes a sample;

[0073] 2.2 Perform band-pass filtering on each sample. The filter can be a Chebyshev filter, a Butterworth band-pass filter, etc.

[0074] 2.3 Extract features from the preprocessed samples, such as time domain features, frequency domain features, or a combination of one or more time-frequency features;

[0075] 3) Data labeling: Based on the motion sickness symptoms reported by the user, EEG signal segments are labeled as no obvious motion sickness symptoms or motion sickness status;

[0076] 4) Model Training: Build a machine learning model, such as a support vector machine model or a random forest model; or build a deep network model, such as a convolutional neural network or a recurrent neural network. Use the feature extraction results and the corresponding motion sickness labels to train the model, and output a fixed-parameter motion sickness assessment model. This model can be used for motion sickness assessment within subjects, across subjects, and over time.

[0077] In this embodiment, optionally, determining the motion sickness level from the multi-channel / single-channel EEG signal includes:

[0078] Input the current user's multi-channel / single-channel EEG signal into the motion sickness assessment model;

[0079] Output the current user's motion sickness score;

[0080] When the motion sickness score is within the first preset range, the motion sickness level is determined to be the first level, indicating that the current user does not experience motion sickness or only has mild motion sickness symptoms;

[0081] When the motion sickness score is within the second preset range, the motion sickness level is determined to be the second level, indicating that the current user has experienced obvious motion sickness symptoms;

[0082] Outputs the current user's motion sickness level.

[0083] The motion sickness score can be simply divided into two levels: the first level and the second level. For example, if the motion sickness score is 0-10, the first preset interval can be set to [0, 3), meaning that the first preset interval includes 0 but excludes 3. The second preset interval can be set to [3, 10], meaning that the second preset interval includes both 3 and 10. The current user's motion sickness score is used to determine the user's motion sickness level.

[0084] This embodiment of the present application utilizes a cross-user, cross-time motion sickness symptom and meditation state assessment model. This eliminates the need to pre-collect EEG training data for the current user when using the motion sickness assessment model. The user can directly use the model to assess their current state and receive a motion sickness score and a meditation effect score. This application also allows for pre-collection of multi-channel or single-channel EEG signals from the current user for training or fine-tuning the motion sickness symptom and meditation state assessment models.

[0085] In this embodiment, optionally, outputting the current user's motion sickness score includes:

[0086] Get multiple preset motion sickness score ranges;

[0087] A target score interval corresponding to the motion sickness score is determined according to a plurality of motion sickness score intervals, wherein the plurality of score intervals respectively correspond to different motion sickness severities.

[0088] In addition to simply dividing the motion sickness score into two segments, first and second, the motion sickness score range can also be divided into multiple segments according to actual needs. For example, using a motion sickness score of 0-10, one preset range can be set to [0, 3), where 0 is included but 3 is excluded, indicating that the current user is not experiencing motion sickness. Another preset range can be set to [3, 6), where 3 is included but 6 is excluded, indicating that the current user is only experiencing mild motion sickness. Another preset range can be set to [6, 10], where both 6 and 10 are included, indicating that the current user is experiencing significant motion sickness.

[0089] In this embodiment, optionally, inputting the current user's multi-channel / single-channel EEG signal into the motion sickness assessment model includes:

[0090] Preprocess the multi-channel / single-channel EEG signals of the current user to obtain EEG signal segments to be tested, where the preprocessing includes filtering.

[0091] Extracting features of the EEG signal segment to be tested, wherein the feature extraction includes one or more of time domain features, frequency domain features, and time-frequency features;

[0092] The results of feature extraction are input into the motion sickness assessment model.

[0093] In this embodiment, optionally, when the motion sickness level is the first level, performing a suspend waiting operation includes:

[0094] The motion sickness level was determined to be level 1;

[0095] Collecting road condition information, including bumpy and congested road conditions and smooth and steady road conditions;

[0096] When the road conditions are smooth and stable, the user's motion sickness level will be reassessed at preset time intervals.

[0097] In the case of bumpy and congested roads, a meditation feedback scene is selected to guide the current user to meditate;

[0098] In the case of bumpy and congested roads, meditation feedback scenarios are selected to guide the current user to meditate, including:

[0099] Collect gyroscope signals;

[0100] receiving traffic condition information, including traffic congestion conditions;

[0101] When the gyroscope signal meets the preset bump condition and the traffic condition information meets the preset condition, a meditation feedback scene is selected to guide the current user to meditate to prevent or relieve motion sickness.

[0102] When the motion sickness level reaches level 1, road condition information is collected. Generally, gyroscope signals and traffic condition information from third-party software can be used as road condition information. Road condition information includes bumpy and congested roads and smooth and smooth roads. Bumpy and congested roads refer to situations such as traffic jams, road construction, traffic accidents, and continuous turns. Smooth and smooth roads refer to no traffic anomalies and allow the vehicle to travel smoothly. When the road is smooth and smooth, the user's motion sickness level is reassessed at preset intervals. In the case of bumpy and congested roads, a meditation feedback scenario is selected to guide the user in meditation, thereby promptly alleviating or preventing motion sickness.

[0103] In this embodiment, optionally, when the motion sickness level is the second level, a meditation feedback scene is selected to guide the current user to meditate to alleviate the motion sickness symptoms, including:

[0104] The motion sickness level was determined to be level 2;

[0105] Voice reminds the current user to start meditation;

[0106] Select a meditation feedback scenario;

[0107] Calculate the meditation effect score based on the current user's multi-channel / single-channel EEG signals;

[0108] Update the meditation feedback scene based on the meditation effect score;

[0109] Meditation feedback scenarios, including: one or more of visual feedback, auditory feedback, visual and auditory feedback, olfactory feedback, tactile feedback, and electrical stimulation feedback;

[0110] Before calculating the meditation effect score, also include:

[0111] Collect multi-channel / single-channel EEG signals of historical users in relaxation and meditation states as training data;

[0112] Based on the training data, a meditation level assessment model is constructed.

[0113] The steps for constructing the meditation level assessment model are as follows:

[0114] 1) Signal acquisition: Collect multi-channel / single-channel EEG signals from multiple users (e.g., 100 users) in a relaxed (or resting) and meditative state. The acquisition equipment includes Neuroscan, a South China Brain Control multi-channel / single-channel headband, etc.

[0115] 2) Signal processing:

[0116] 2.1 Preprocessing the collected EEG signals: Divide the collected EEG signals into EEG signal segments of equal length (e.g., 10 seconds), each EEG signal segment constitutes a sample;

[0117] 2.2 Perform band-pass filtering on each sample. The filter can be a Chebyshev filter, a Butterworth band-pass filter, etc.

[0118] 2.3 Extract features from the preprocessed samples, such as time domain features, frequency domain features, or a combination of one or more time-frequency features;

[0119] 3) Model Design: Build machine learning models, such as support vector machines and random forest models; or build deep network models, such as convolutional neural networks and recurrent neural networks. These models can be used to assess meditation levels within subjects, across subjects, and over time.

[0120] 4) Model output: Use the feature extraction results and their corresponding relaxation or meditation state labels to train the model built in step 3). After training, output a meditation state assessment model with fixed parameters.

[0121] In this embodiment, optionally, the meditation effect score is calculated based on the multi-channel / single-channel EEG signal of the current user, including:

[0122] Input the current user's multi-channel / single-channel EEG signal into the meditation level assessment model;

[0123] Output the current user's meditation effect score.

[0124] In this embodiment, optionally, updating the meditation feedback scene according to the meditation effect score includes:

[0125] Reduce the expressiveness of meditation feedback scenarios when meditation effectiveness scores decrease;

[0126] Improve the expressiveness of meditation feedback scenes when meditation effect scores increase.

[0127] Meditation feedback scenarios refer to scenarios that guide users in meditation and allow for self-adjustment based on user feedback (expressed as meditation effectiveness scores), including but not limited to visual, auditory, tactile, and olfactory scenarios. In visual scenarios, expressiveness generally refers to the clarity of the image; in auditory scenarios, expressiveness generally refers to the clarity of the sound and the volume and intensity of interfering sounds; in tactile scenarios, expressiveness generally refers to the vibration amplitude or electrical stimulation intensity of the wearable device; and in olfactory scenarios, expressiveness generally refers to the concentration and pleasantness of the odor.

[0128] It can recommend users to choose meditation scenes with different feedback types for different degrees of motion sickness, including visual, auditory, audio-visual, olfactory, tactile, electrical stimulation and other feedback types, and also support users to independently choose meditation feedback scenes, which can effectively improve the user's meditation effect and lower the threshold for entering the meditation state.

[0129] In this embodiment, optionally, the current user is riding in a vehicle that causes motion sickness, and the vehicle that causes motion sickness includes: any one of a car, a ship, and an airplane.

[0130] In a feasible implementation scenario:

[0131] Motion sickness intervention methods based on brain-computer interface mindfulness meditation include:

[0132] 1) The user first wears the EEG signal acquisition device, which transmits the EEG signal in real time to a computing device such as a car computer / laptop / tablet / mobile phone via Bluetooth or wired communication;

[0133] 2) The computing device performs signal processing on the EEG signal, including segmentation, baseline removal, filtering, feature extraction, etc.;

[0134] 3) Inputting the processed EEG signal into the aforementioned motion sickness assessment model to output a motion sickness assessment score;

[0135] 4) Determine the user's current motion sickness status based on the motion sickness score range, using a score of 1-100 as an example:

[0136] 4.1 When the motion sickness score is in the range [1, a], such as a can be 20, it means that the user has no obvious motion sickness symptoms, corresponding to the first level of motion sickness. When the motion sickness score is in the range (a, 100], such as a can be 20, it means that the user is in a state of motion sickness and has obvious motion sickness symptoms, corresponding to the second level of motion sickness.

[0137] 4.2 The motion sickness score can be further subdivided into different levels and the evaluation results can be output. For example, 1-20 points indicate no obvious motion sickness symptoms; 21-70 points indicate mild motion sickness; 71-100 points indicate severe motion sickness;

[0138] 5) If the user is experiencing the second level of motion sickness, a voice prompt will be given to the user to start mindfulness meditation. The mindfulness meditation system will then automatically activate and recommend a feedback scenario for the user to meditate on. Alternatively, the user can choose a suitable scenario based on their preferences to meditate on and alleviate motion sickness symptoms.

[0139] 5.1 Meditation feedback scenes are displayed through the display screen of the vehicle computer / laptop / tablet / mobile phone and audio, electrical stimulation and other devices;

[0140] 5.2 After entering the meditation feedback scenario, the computing device inputs the pre-processed EEG signal into the above-mentioned meditation state assessment model and outputs a meditation effect score;

[0141] 5.3 Controlling the expressiveness of the meditation feedback scene based on the meditation effect score. This includes changes in visual scene clarity and viewing angle, auditory volume, pitch, and sound type, tactile intensity, and electrical stimulation intensity, all of which can be summarized as changes in expressiveness. In one specific implementation, the user's meditation state is evaluated at intervals (e.g., 2 seconds). If the meditation effect score at this moment is higher than the previous one, i.e., an increase in the meditation effect score indicates that the user's meditation level has deepened or the effect has improved, the three-dimensional (3D) / two-dimensional (2D) visual scene will become clearer and more beautiful, and the sound will become louder. If the meditation effect score at this moment is lower than the previous one, i.e., a decrease in the meditation effect score indicates that the user's meditation level has shallowed or the effect has deteriorated or their mind has wandered, the visual 3D / 2D scene will become blurry and less beautiful, and the sound will become quieter.

[0142] 5.4 Visual feedback scenarios may include animations of the sky, clouds, campfires, ocean waves, and forests; auditory feedback scenarios may include pink noise, raindrops, running water, the sound of flames, music, etc.; visual and auditory feedback scenarios may include both visual and auditory feedback; olfactory feedback may include aromatherapy; tactile feedback may include vibration of portable devices and seat massage; and electrical stimulation feedback may include direct current stimulation. Each of the above sensory feedback scenarios is accompanied by meditation instructions to guide user training.

[0143] 6) If the user does not experience obvious motion sickness, collect gyroscope data and receive traffic status information (using third-party map software) and transmit it to the current computing device via Bluetooth or wireless network;

[0144] 6.1 In bumpy and congested road conditions, such as when the gyroscope angular velocity changes significantly or when the map software indicates traffic congestion ahead, a voice reminder to meditate will automatically activate the mindfulness meditation system and recommend a meditation scenario. Alternatively, the user can select an appropriate scenario to meditate, alleviating or preventing motion sickness.

[0145] 6.2 When the road conditions are smooth and steady, users will not be prompted to engage in mindfulness meditation.

[0146] Although embodiments of the present application have been shown and described, it will be understood from the above detailed description that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. Motion sickness intervention methods based on brain-computer interface mindfulness meditation, including: Real-time collection of the current user's multi-channel / single-channel EEG signals; determining a motion sickness level from the multi-channel / single-channel EEG signal, the motion sickness level including a first level and a second level; When the motion sickness level is the first level, performing a suspend waiting operation; When the motion sickness level is the second level, a meditation feedback scene is selected to guide the current user to meditate to alleviate the motion sickness symptoms.

2. The method according to claim 1, wherein: Before the real-time acquisition of the multi-channel / single-channel EEG signals of the current user, the method further includes: Collecting multi-channel / single-channel EEG signals of historical users as training data, wherein the historical users include non-motion sickness users and motion sickness users; constructing a training set, the training set including pairs of training data and user-reported motion sickness symptoms; Based on the data pairs, a motion sickness assessment model is constructed.

3. The method according to claim 2, wherein: Determining a motion sickness level from the multi-channel / single-channel EEG signal includes: Inputting the multi-channel / single-channel EEG signal of the current user into the motion sickness assessment model; Outputting the motion sickness score of the current user; When the motion sickness score is within the first preset interval, determining that the motion sickness level is the first level, indicating that the current user does not experience motion sickness or only has mild motion sickness symptoms; When the motion sickness score is within the second preset interval, determining that the motion sickness level is the second level, indicating that the current user has experienced obvious motion sickness symptoms; Output the motion sickness level of the current user.

4. The method according to claim 3, wherein: Outputting the motion sickness score of the current user includes: Get multiple preset motion sickness score ranges; A target score interval corresponding to the motion sickness score is determined according to the multiple motion sickness score intervals, wherein the multiple score intervals respectively correspond to different motion sickness severities.

5. The method according to claim 3, wherein: Inputting the multi-channel / single-channel EEG signal of the current user into the motion sickness assessment model includes: Preprocessing the multi-channel / single-channel EEG signals of the current user to obtain EEG signal segments to be tested, wherein the preprocessing includes filtering; Extracting features from the EEG signal to be tested, wherein the feature extraction includes extracting at least one of the following features: time domain features, frequency domain features, and time-frequency features; The result of the feature extraction is input into the motion sickness assessment model.

6. The method according to claim 1, wherein: When the motion sickness level is the first level, performing a suspend waiting operation, including: Determining that the motion sickness level is the first level; Collecting road condition information, including bumpy and congested road conditions and smooth and steady road conditions; When the road condition is smooth and stable, re-evaluating the motion sickness level of the current user at preset time intervals; In the case of bumpy and congested road conditions, selecting a meditation feedback scene to guide the current user to meditate; In the case of bumpy and congested road conditions, selecting a meditation feedback scene to guide the current user to meditate includes: Collect gyroscope signals; receiving traffic condition information, wherein the traffic condition information includes congestion conditions; When the gyroscope signal meets a preset bump condition and the traffic condition information meets a preset condition, a meditation feedback scene is selected to guide the current user to meditate to prevent or alleviate motion sickness.

7. The method according to claim 1, wherein: When the motion sickness level is the second level, selecting a meditation feedback scene to guide the current user to meditate to alleviate the motion sickness symptom, including: Determining that the motion sickness level is the second level; Voice reminding the current user to start meditation; Select a meditation feedback scenario; Calculating a meditation effect score based on the multi-channel / single-channel EEG signal of the current user; updating the meditation feedback scenario according to the meditation effect score; The meditation feedback scenario includes at least one of the following: visual feedback, auditory feedback, visual and auditory feedback, olfactory feedback, tactile feedback, and electrical stimulation feedback; Before calculating the meditation effect score, also include: Collect multi-channel / single-channel EEG signals of historical users in relaxation and meditation states as training data; Based on the training data, a meditation level assessment model is constructed.

8. The method according to claim 7, wherein: Calculate the meditation effect score based on the current user's multi-channel / single-channel EEG signals, including: Inputting the multi-channel / single-channel EEG signal of the current user into a meditation degree assessment model; Output the meditation effect score of the current user.

9. The method according to claim 7, wherein: Updating the meditation feedback scenario according to the meditation effect score, including: When the meditation effect score decreases, reducing the expressiveness of the meditation feedback scene; When the meditation effect score increases, the expressiveness of the meditation feedback scene is improved.

10. The method of claim 1, wherein: The current user is riding in a vehicle that causes motion sickness, and the vehicle that causes motion sickness includes at least one of the following: a car, a ship, and an airplane.

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