Anti-vertigo method, device, storage medium, processing equipment and virtual reality equipment

Abstract

The present invention provides an anti-vertigo method, apparatus, storage medium, processing equipment, and virtual reality device. The anti-vertigo method includes: obtaining an electroencephalogram (EEG) signal at a target location of a user while viewing a display screen of the virtual reality device; extracting a target frequency domain characteristic signal of the EEG signal, and determining whether the user is in a vertigo state based on a change characteristic of the target frequency domain characteristic signal at the target location; and adjusting the display screen of the virtual reality device to adjust the vertigo state if the user is in a vertigo state. The present invention can monitor whether the user is in a vertigo state based on the EEG signal while viewing the display screen of the virtual reality device, and timely adjust the display screen of the virtual reality device if the user is in a vertigo state, thereby effectively adjusting the user's vertigo state, reducing the possibility of motion sickness, and improving the user's virtual reality viewing experience.

Description

Technical Field

[0001] The present invention relates to the field of virtual reality, and in particular to an anti-vertigo method, device, storage medium, processing equipment and virtual reality equipment. Background Art

[0002] In the related art, when a user uses a virtual reality (VR) device to view its display image, for example, when a user uses a head-controlled virtual reality system, he or she may experience motion sickness symptoms such as eye fatigue, disorientation, upper abdominal discomfort, nausea, and dizziness, thereby affecting the user's virtual reality viewing experience. Summary of the Invention

[0003] The present invention provides an anti-dizziness method, device, storage medium, processing equipment and virtual reality device, which can monitor whether a user is in a dizzy state based on electroencephalogram signals when the user is viewing the display screen of the virtual reality device, and timely adjust the display screen of the virtual reality device in the dizzy state, so that the user's dizzy state can be effectively adjusted, the possibility of motion sickness is reduced, and the user's virtual reality viewing experience is improved.

[0004] In a first aspect, an embodiment of the present invention provides an anti-vertigo method, comprising:

[0005] Acquiring an EEG signal of a user's target position while viewing a display image of a virtual reality device;

[0006] extracting a target frequency domain characteristic signal of the EEG signal, and determining whether the user is in a dizzy state based on a change characteristic of the target frequency domain characteristic signal at the target position;

[0007] When the user is in a dizzy state, the display image of the virtual reality device is adjusted to adjust the dizzy state.

[0008] In some implementations, the target location includes at least one location among the parietal lobe, the frontal lobe, the temporal lobe, and the central point; and the target frequency domain characteristic signal includes at least one of α wave, β wave, θ wave, and γ wave.

[0009] In some implementations, the target location includes multiple locations, and the target frequency domain characteristic signals include multiple locations. The step of determining whether the user is in a dizzy state based on a change characteristic of the target frequency domain characteristic signals at the target locations includes:

[0010] Determining the score of each target frequency domain characteristic signal based on the change characteristics of each target frequency domain characteristic signal at each target position;

[0011] Calculate the total score based on the preset weight of each target position, the preset weight of each target frequency domain characteristic signal, and the score of each target frequency domain characteristic signal;

[0012] Determining whether the total score exceeds a preset threshold;

[0013] When the total score exceeds a preset threshold, it is determined that the user is in a dizzy state.

[0014] In some implementations, determining the score of each target frequency domain characteristic signal based on the change characteristics of each target frequency domain characteristic signal at each target position includes:

[0015] Determine whether the change characteristics of each target frequency domain characteristic signal at each target position meet the corresponding preset conditions;

[0016] The target frequency domain feature signal whose change characteristics meet the corresponding preset conditions is recorded as a first score, and the target frequency domain feature signal whose change characteristics do not meet the corresponding preset conditions is recorded as a second score.

[0017] In some implementations, the target frequency domain characteristic signals include: α wave, β wave, θ wave, and γ wave, and determining whether the change characteristics of each target frequency domain characteristic signal at each target position meet corresponding preset conditions includes:

[0018] Determine whether the changing characteristics of the alpha wave at each target position show a downward trend;

[0019] Determine whether the changing characteristics of the beta wave at each target position show a downward trend;

[0020] Determine whether the change characteristics of the theta wave at each target position show a downward trend;

[0021] Determine whether the changing characteristics of the gamma wave at each target position show an upward trend.

[0022] In some implementations, calculating the total score based on the preset weight of each target position, the preset weight of each target frequency domain feature signal, and the score of each target frequency domain feature signal includes:

[0023] For each target position, a score of each target position is calculated based on the score of each target frequency domain characteristic signal and the preset weight of each target frequency domain characteristic signal;

[0024] The total score is calculated based on the score of each target position and the preset weight of each target position.

[0025] In some implementations, the anti-vertigo method further includes:

[0026] When the user is in a dizzy state, determining a dizziness level according to the score interval of the total score;

[0027] The adjusting the display screen of the virtual reality device includes: determining a method for adjusting the display screen of the virtual reality device according to the dizziness level, and adjusting the display screen of the virtual reality device in this method.

[0028] In some implementations, before extracting the target frequency domain feature signal of the EEG signal, the method further includes:

[0029] The EEG signal is preprocessed to filter out noise in the EEG signal, complete data, and / or remove noise generated by eye movements.

[0030] In some implementations, the method of adjusting the display screen of the virtual reality device includes changing the image processing mode, reducing the image delay, changing the image display method, limiting the user's viewing angle, and cutting off the display screen.

[0031] In a second aspect, an embodiment of the present invention provides an anti-vertigo device, comprising:

[0032] An acquisition module, used to acquire the EEG signal of the user's target position while watching the display image of the virtual reality device;

[0033] a determination module, configured to extract a target frequency domain characteristic signal of the EEG signal, and determine whether the user is in a dizzy state based on a change characteristic of the target frequency domain characteristic signal at the target position;

[0034] The adjustment module is used to adjust the display image of the virtual reality device to adjust the dizziness state when the user is in a dizzy state.

[0035] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by one or more processors, the anti-vertigo method described in the first aspect is implemented.

[0036] In a fourth aspect, an embodiment of the present invention provides a processing device, comprising a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the anti-vertigo method described in the first aspect is implemented.

[0037] In a fifth aspect, an embodiment of the present invention provides a virtual reality device, including:

[0038] An EEG acquisition device includes at least one EEG acquisition electrode, wherein the at least one EEG acquisition electrode is connected to a target position of the user and is used to acquire EEG signals at the target position of the user;

[0039] The host comprises the processing device described in the fourth aspect.

[0040] In some implementations, the host and the EEG acquisition device are integrally connected so that when the user wears the host, the at least one EEG acquisition electrode can be connected to the target position.

[0041] In some implementations, the EEG acquisition device further includes:

[0042] an amplifier, connected to the EEG acquisition electrode, and configured to amplify the EEG signal acquired by the EEG acquisition electrode;

[0043] a filter connected to the amplifier and configured to filter out interference signals from the amplified EEG signal;

[0044] The analog-to-digital converter is connected to the filter and is used to perform analog-to-digital conversion on the EEG signal after the interference signal is filtered out, and transmit the signal to the host in the form of a digital signal.

[0045] In some implementations, the host further includes:

[0046] a signal processing device, one end of which is connected to the analog-to-digital converter and the other end of which is connected to the processor, for receiving the EEG signal input by the analog-to-digital converter and transmitting it to the processor, so that the processor obtains the EEG signal of the user's target position;

[0047] a correction circuit, one end of which is connected to the signal processing device and the other end of which is connected to the analog-to-digital converter, for correcting a DC offset effect;

[0048] A detection circuit, one end of which is connected to the signal processing device and the other end of which is connected to the EEG acquisition electrode, is used to detect whether the EEG acquisition electrode is effectively connected.

[0049] One or more embodiments of the present invention can bring at least the following beneficial effects:

[0050] The present invention obtains an EEG signal at a user's target location while the user is viewing a display screen of a virtual reality device; extracts a target frequency domain characteristic signal from the EEG signal, and determines whether the user is in a dizzy state based on the change characteristics of the target frequency domain characteristic signal at the target location; and if the user is in a dizzy state, adjusts the display screen of the virtual reality device to adjust the dizzy state. The present invention can monitor in real time whether the user is in a dizzy state based on the EEG signal while the user is viewing the display screen of the virtual reality device, and promptly adjusts the display screen of the virtual reality device if the user is in a dizzy state, effectively adjusting the user's dizzy state, reducing the possibility of motion sickness, and improving the user's virtual reality viewing experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope.

[0052] Figure 1 This is a flow chart of an anti-vertigo method provided by an embodiment of the present invention;

[0053] Figure 2 This is a schematic diagram of EEG acquisition points provided by an embodiment of the present invention;

[0054] Figure 3 This is a block diagram of an anti-vertigo device provided by an embodiment of the present invention;

[0055] Figure 4 is a block diagram of a virtual reality device provided by an embodiment of the present invention;

[0056] Figure 5 Schematic diagram of a head-mounted virtual reality device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0058] Example 1

[0059] Figure 1 A flow chart of an anti-vertigo method is shown, as shown in FIG. Figure 1 As shown, the anti-vertigo method provided in this embodiment includes at least steps S101 to S104:

[0060] Step S101: Acquire an EEG signal of a user's target position while viewing a display image of a virtual reality device.

[0061] In practical applications, Figure 2In the schematic diagram of EEG acquisition points shown, the parietal region includes Pz, P3, and P4; the frontal region includes Fz, FPz, FP1, FP2, F3, and F4; the temporal region includes T3 and T4; and the central point includes Cz. Alpha waves are EEG signals with a frequency range of 9Hz to 13Hz, beta waves are EEG signals with a frequency range of 14Hz to 30Hz, theta waves are EEG signals with a frequency range of 4Hz to 8Hz, and gamma waves are EEG signals with a frequency range of 27Hz to 80Hz.

[0062] In some implementations, the target location includes at least one of the parietal lobe, the frontal lobe, the temporal lobe, and the central point; and the target frequency domain characteristic signal includes at least one of alpha wave, beta wave, theta wave, and gamma wave.

[0063] In some cases, one point in each area is selected as the target position of the head. For example, when the target positions include the parietal lobe, frontal lobe, temporal lobe and central point, the four points Pz, Fz, T3 and Cz can be selected as target positions for EEG signal collection.

[0064] Step S102: extracting target frequency domain characteristic signals of the EEG signal.

[0065] In some implementations, before extracting the target frequency domain feature signal of the EEG signal, the method further includes:

[0066] The EEG signals are preprocessed to filter out noise, complete the data, and / or remove noise caused by eye movements.

[0067] In some cases, the following preprocessing is performed on the EEG signals obtained from the EEG acquisition device:

[0068] First, the EEG signal is filtered using a bandpass filter or a notch filter to remove noise in the EEG signal, including filtering out noise and / or extreme values ​​of specific frequencies, such as noise above 100 Hz, below 0.5 Hz, and equal to 50 Hz;

[0069] Secondly, the data were completed using interpolation to complete the data that were missed during the EEG acquisition process.

[0070] Thirdly, independent component analysis (ICA) is used to remove the noise generated by eye movements, so as to eliminate the influence of the noise generated by eye movements within a certain threshold range.

[0071] Based on the above preprocessing, it is possible to avoid interference such as noise in the EEG signal from affecting the judgment of the dizziness state.

[0072] In some implementations, extracting the target frequency domain characteristic signal of the EEG signal in step S102 may include: converting the EEG signal from time domain data to frequency domain data using any one of power spectral density analysis, fast Fourier transform, sparse Fourier transform, and wavelet analysis, and extracting the target frequency domain characteristic signal based on the frequency domain corresponding to different target frequency domain characteristic signals.

[0073] Step S103: Determine whether the user is in a dizzy state based on the change characteristics of the target frequency domain characteristic signal at the target position; if the user is in a dizzy state, execute step S104; if the user is not in a dizzy state, execute step S101, and continue to obtain the EEG signal of the user's target position while watching the display screen of the virtual reality device to monitor whether the user is in a dizzy state.

[0074] In some implementations, the target location includes multiple ones of the parietal lobe, the frontal lobe, the temporal lobe, and the central point, and the target frequency domain characteristic signal includes multiple ones of alpha waves, beta waves, theta waves, and gamma waves, and then determining whether the user is in a dizzy state based on the change characteristics of the target frequency domain characteristic signal at the target location may further include:

[0075] Step S103a, determining the score of each target frequency domain characteristic signal based on the change characteristics of each target frequency domain characteristic signal at each target position;

[0076] In some implementations, determining the score of each target frequency domain characteristic signal based on the change characteristics of each target frequency domain characteristic signal at each target position includes:

[0077] Step S103a-1, determining whether the change characteristics of each target frequency domain characteristic signal at each target position meet corresponding preset conditions;

[0078] In the case where the target frequency domain characteristic signals include α waves, β waves, θ waves, and γ waves, determining whether the change characteristics of each target frequency domain characteristic signal at each target position meet corresponding preset conditions may include:

[0079] Determine whether the changing characteristics of the alpha wave at each target position show a downward trend;

[0080] Determine whether the changing characteristics of the beta wave at each target position show a downward trend;

[0081] Determine whether the change characteristics of the theta wave at each target position show a downward trend;

[0082] Determine whether the changing characteristics of the gamma wave at each target position show an upward trend.

[0083] In practical applications, for the analysis of rising or falling trends of waveforms of different frequencies, any of the following methods can be used: derivative of waveform data over a period of time (if the sum of derivatives is greater than zero, it is an rising trend; if the sum of derivatives is less than zero, it is a falling trend), slope summation (if the sum of slopes is positive, it is an rising trend; if the sum of slopes is negative, it is a falling trend), and first-order difference summation (if the sum of differences is positive, it is an rising trend; if the sum of differences is negative, it is a falling trend). Other methods can also be used as needed.

[0084] Step S103a-2: Record the target frequency domain characteristic signal whose change characteristics meet the corresponding preset conditions as a first score, and record the target frequency domain characteristic signal whose change characteristics do not meet the corresponding preset conditions as a second score.

[0085] In one example, the first score is 1 and the second score is 0. For example, for the acquisition point Pz in the parietal lobe, if the change characteristic of the α wave is a downward trend, the α wave is recorded as 1, if the change characteristic of the β wave is a downward trend, the β wave is recorded as 1, if the change characteristic of the θ wave is not a downward trend, the θ wave is recorded as 0, and if the change characteristic of the γ wave is not an upward trend, the γ wave is recorded as 0.

[0086] Step S103b: Calculate the total score according to the preset weight of each target position, the preset weight of each target frequency domain characteristic signal, and the score of each target frequency domain characteristic signal.

[0087] In some implementations, calculating the total score based on the preset weights of the target positions, the preset weights of the target frequency domain feature signals, and the scores of the target frequency domain feature signals may include:

[0088] Step S103b-1: for each target position, calculate the score of each target position based on the score of each target frequency domain characteristic signal and the preset weight of each target frequency domain characteristic signal;

[0089] In one example, the target locations include the parietal lobe, frontal lobe, temporal lobe, and central point, and the target frequency domain characteristic signals include alpha waves, beta waves, theta waves, and gamma waves, with the weight of alpha waves being 19%, beta waves being 25%, theta waves being 31%, and gamma waves being 25%. The weight of the frontal lobe is 50%, the weight of the parietal lobe is 25%, the weight of the central point is 15%, and the weight of the temporal lobe is 10%. Accordingly, if each target frequency domain characteristic signal at each target location meets the corresponding preset conditions, the total score should be:

[0090] (1*19%+1*25%+1*31%+1*25%)*(50%+25%+15%+10%)=1.

[0091] Step S103b-2: Calculate the total score based on the score of each target position and the preset weight of each target position.

[0092] In some implementations, the score of each target position is multiplied by the corresponding preset weight, and then the sum is calculated to obtain the total score.

[0093] Continuing with the previous example, for the parietal lobe sampling point Pz, if the alpha wave shows a downward trend, the alpha wave is scored as 1; if the beta wave shows a downward trend, the beta wave is scored as 1; if the theta wave does not show a downward trend, the theta wave is scored as 0; and if the gamma wave does not show an upward trend, the gamma wave is scored as 0. The parietal lobe score is: (1 * 19% + 1 * 25% + 0 * 31% + 0 * 25%) * 25% = 0.11. The frontal, temporal, and central lobe score calculations are similar and will not be given as examples here.

[0094] Step S103c: Determine whether the total score exceeds a preset threshold.

[0095] If the total score exceeds the preset threshold, execute step S103d. If the total score does not exceed the preset threshold, determine that the user is not in a dizzy state, return to execute step S101, and continue to obtain the EEG signal of the user's target position while watching the display screen of the virtual reality device to monitor whether the user is in a dizzy state.

[0096] Step S103d: Determine whether the user is in a dizzy state. If the user is in a dizzy state, adjust the display screen of the virtual reality device to adjust the dizzy state.

[0097] Step S104: Adjust the display screen of the virtual reality device to adjust the dizziness state.

[0098] In some implementations, adjusting the display image of the virtual reality device includes one of changing an image processing mode, reducing image latency, changing an image display mode, limiting the user's viewing angle, and cutting off the display image. Changing the image processing mode may include increasing the refresh rate and / or increasing the resolution, changing the image display mode may include changing a 3D display mode to a 2D display mode, and limiting the user's viewing angle may include one of fixing the viewing angle, reducing viewing angle changes, and reducing the field of view.

[0099] In some implementations, the anti-vertigo method of this embodiment further includes: when the user is experiencing vertigo, determining a vertigo level based on the score range of the total score. Accordingly, adjusting the display of the virtual reality device may include: determining a method for adjusting the display of the virtual reality device based on the vertigo level, and adjusting the display of the virtual reality device in that method.

[0100] In some implementations, pre-set dizziness level thresholds w1, w2, and w3 are defined, with w<w1<w2<w3, where w is the preset threshold, w≤total score<w1 corresponds to a dizziness level of level one, w1≤total score<w2 corresponds to a dizziness level of level two, w2≤total score<w3 corresponds to a dizziness level of level three, and w3≤total score<1 corresponds to a dizziness level of level four. The user's current dizziness level is determined based on the score interval in which the total score falls. Higher levels indicate a higher degree of dizziness, and the corresponding virtual reality device display screen adjustment method may differ. For example, when the dizziness level is level one, an adjustment method may be used to change the image processing mode; when the dizziness level is level two, an adjustment method may be used to reduce image latency; when the dizziness level is level three, an adjustment method may be used to change the image display method or restrict the user's viewing angle; and when the dizziness level is the highest level (level four), an adjustment method may be used to cut off the display screen to force the user to rest and recover from the dizziness.

[0101] Before the virtual reality display screen content watched by the user ends, the process of this method can be executed once at a fixed interval to detect the dizziness state, or the dizziness state can be detected in real time until the virtual reality display screen content watched by the user ends, thereby ensuring that the user has a good user experience while watching the virtual reality display screen.

[0102] In this embodiment, while the user is viewing a display screen on a virtual reality device, an EEG signal is acquired at a target location on the user's head; a target frequency domain characteristic signal of the EEG signal is extracted, and based on the changing characteristics of the target frequency domain characteristic signal at the target location, it is determined whether the user is experiencing a dizzy state; and if the user is experiencing a dizzy state, the display screen of the virtual reality device is adjusted to adjust the dizzy state. This embodiment can monitor in real time whether the user is experiencing a dizzy state based on the EEG signal while the user is viewing the display screen on the virtual reality device, and promptly adjust the display screen of the virtual reality device if dizzy is present, effectively adjusting the user's dizzy state, reducing the likelihood of motion sickness, and enhancing the user's virtual reality viewing experience.

[0103] Example 2

[0104] Figure 3 A block diagram of an anti-vertigo device is shown, as shown in FIG. Figure 3 As shown, the anti-vertigo device of this embodiment includes:

[0105] An acquisition module 201 is used to acquire an EEG signal of a user's target position while viewing a display image of a virtual reality device;

[0106] Determination module 202, for extracting a target frequency domain characteristic signal of the EEG signal, and determining whether the user is in a dizzy state based on a change characteristic of the target frequency domain characteristic signal at a target position;

[0107] The adjustment module 203 is used to adjust the display image of the virtual reality device to adjust the dizziness state when the user is in the dizziness state.

[0108] In some implementations, the target location includes at least one of the parietal lobe, the frontal lobe, the temporal lobe, and the central point; and the target frequency domain characteristic signal includes at least one of alpha wave, beta wave, theta wave, and gamma wave.

[0109] In some implementations, before extracting the target frequency domain characteristic signal of the EEG signal, the method further includes: preprocessing the EEG signal to filter out noise in the EEG signal, complete data, and / or remove noise generated by eye movements.

[0110] In some implementations, there are multiple target locations and multiple target frequency domain characteristic signals, and determining whether the user is in a dizzy state based on change characteristics of the target frequency domain characteristic signals at the target locations includes:

[0111] Determining the score of each target frequency domain characteristic signal based on the change characteristics of each target frequency domain characteristic signal at each target position;

[0112] Calculate the total score based on the preset weight of each target position, the preset weight of each target frequency domain characteristic signal, and the score of each target frequency domain characteristic signal;

[0113] Determining whether the total score exceeds a preset threshold;

[0114] When the total score exceeds a preset threshold, it is determined that the user is in a dizzy state.

[0115] In some implementations, determining the score of each target frequency domain characteristic signal based on the change characteristics of each target frequency domain characteristic signal at each target position includes:

[0116] Determine whether the change characteristics of each target frequency domain characteristic signal at each target position meet the corresponding preset conditions;

[0117] The target frequency domain feature signal whose change characteristics meet the corresponding preset conditions is recorded as a first score, and the target frequency domain feature signal whose change characteristics do not meet the corresponding preset conditions is recorded as a second score.

[0118] In some implementations, the target frequency domain characteristic signals include: α wave, β wave, θ wave, and γ wave, and determining whether the change characteristics of each target frequency domain characteristic signal at each target position meet corresponding preset conditions includes:

[0119] Determine whether the changing characteristics of the alpha wave at each target position show a downward trend;

[0120] Determine whether the changing characteristics of the beta wave at each target position show a downward trend;

[0121] Determine whether the change characteristics of the theta wave at each target position show a downward trend;

[0122] Determine whether the changing characteristics of the gamma wave at each target position show an upward trend.

[0123] In some implementations, calculating a total score based on a preset weight of each target position, a preset weight of each target frequency domain feature signal, and a score of each target frequency domain feature signal includes:

[0124] For each target position, a score of each target position is calculated based on the score of each target frequency domain characteristic signal and the preset weight of each target frequency domain characteristic signal;

[0125] The total score is calculated based on the score of each target position and the preset weight of each target position.

[0126] In some implementations, the score of each target position is multiplied by the corresponding preset weight, and then the sum is calculated to obtain the total score.

[0127] In some implementations, adjusting the display image of the virtual reality device includes one of changing an image processing mode, reducing image latency, changing an image display mode, limiting the user's viewing angle, and cutting off the display image. Changing the image processing mode may include increasing the refresh rate and / or increasing the resolution, changing the image display mode may include changing a 3D display mode to a 2D display mode, and limiting the user's viewing angle may include one of fixing the viewing angle, reducing viewing angle changes, and reducing the field of view.

[0128] In some implementations, the determination module 202 is further configured to, if the user is experiencing dizziness, determine a dizziness level based on the score range in which the total score falls. Accordingly, the adjustment module 203 is further configured to determine a method for adjusting the display of the virtual reality device based on the dizziness level, and adjust the display of the virtual reality device accordingly.

[0129] In this embodiment, while the user is viewing a display screen on a virtual reality device, an EEG signal is acquired at a target location on the user's head; a target frequency domain characteristic signal of the EEG signal is extracted, and based on the changing characteristics of the target frequency domain characteristic signal at the target location, it is determined whether the user is experiencing a dizzy state; and if the user is experiencing a dizzy state, the display screen of the virtual reality device is adjusted to adjust the dizzy state. This embodiment can monitor in real time whether the user is experiencing a dizzy state based on the EEG signal while the user is viewing the display screen on the virtual reality device, and promptly adjust the display screen of the virtual reality device if dizzy is present, effectively adjusting the user's dizzy state, reducing the likelihood of motion sickness, and enhancing the user's virtual reality viewing experience.

[0130] Those skilled in the art will appreciate that the above modules or steps can be implemented using a general-purpose computing device, centralized on a single computing device, or distributed across a network of multiple computing devices. Alternatively, they can be implemented using program code executable by a computing device, which can then be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0131] Example 3

[0132] This embodiment provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by one or more processors, the anti-vertigo method of the first embodiment is implemented.

[0133] Among them, the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0134] Example 4

[0135] This embodiment provides a processing device, including a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the anti-dizziness method of the first embodiment is implemented.

[0136] In practical applications, the processor can be an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller unit (MCU), a microprocessor or other electronic components to execute the methods in the above embodiments.

[0137] Example 5

[0138] Figure 4 A block diagram of a virtual reality device is shown. This embodiment provides a virtual reality device, including:

[0139] an EEG acquisition device, comprising at least one EEG acquisition electrode, wherein the at least one EEG acquisition electrode is connected to a target position of the user and is used to acquire EEG signals at the target position of the user; and

[0140] A host includes the processing device of embodiment 4.

[0141] In some implementations, the host of the virtual reality device may be a head-mounted virtual reality device, and the virtual reality device may be controlled by different manipulation methods such as a mouse / touch screen / handle of the host. Figure 5 A schematic diagram of a head-mounted virtual reality device shows a main unit and an EEG acquisition device integrally connected. The main unit includes glasses 51, which are worn on the user's eyes to view the virtual reality display. The EEG acquisition device's electrodes are mounted on the head-mounted portion 52 of the device, and each electrode is positioned according to a target location, so that when the user wears the main unit, the EEG acquisition device's electrodes 521 connect to the target location on the user's head. By integrating the glasses and head-mounted portion, users can simultaneously view the virtual reality display and collect EEG signals to detect vertigo, allowing for timely adjustment.

[0142] In some examples, when the target location includes the frontal lobe, the EEG acquisition electrode for collecting EEG signals of the frontal lobe can be embedded in the area of ​​the glasses that contacts the forehead, which is easy to wear and has a simple shape. The head-mounted part can be set in the form of a cross-fixing belt, and the EEG acquisition electrode of the EEG acquisition device is embedded in the fixing belt. In addition, a reference electrode 522 is extended from the head-mounted part, and after wearing, the reference electrode is fixed behind the user's ear to collect the reference potential. It should be understood that Figure 5 This is only an illustration and does not constitute a limitation of this embodiment.

[0143] In some implementations, the EEG acquisition device further includes:

[0144] an amplifier, connected to the EEG acquisition electrodes, for amplifying the EEG signals collected by the EEG acquisition electrodes;

[0145] A filter, connected to the amplifier, for filtering out interference signals from the amplified EEG signal;

[0146] The analog-to-digital converter is connected to the filter and is used to convert the EEG signal after the interference signal is filtered out into digital form and transmit it to the host in the form of a digital signal.

[0147] In some implementations, the amplifier includes a pre-amplifier and a post-amplifier, which together amplify the signal by a factor of approximately 1,000 to 5,000. The filter includes an active filter circuit that filters out interference signals such as high-frequency noise, 50 Hz power frequency interference, and low-frequency DC components that are mixed in the amplified EEG signal.

[0148] In some implementations, the host further includes:

[0149] A signal processing device, one end of which is connected to the analog-to-digital converter and the other end of which is connected to the processor, is used to receive the EEG signal input by the analog-to-digital converter and transmit it to the processor, so that the processor can obtain the EEG signal of the user's target position;

[0150] a correction circuit, one end of which is connected to the signal processing device and the other end is connected to the analog-to-digital converter, for correcting the DC offset effect; and

[0151] The detection circuit has one end connected to the signal processing device and the other end connected to the EEG collection electrode, and is used to detect whether the EEG collection electrode is effectively connected.

[0152] In some implementations, the signal processing device includes a DSP, which receives the EEG signal transmitted by the analog-to-digital converter, performs digital signal processing on the signal, and then transmits the signal to the processor.

[0153] In some implementations, the DSP obtains the DC offset based on the voltage difference between the input and output of the correction circuit, calculates data without the influence of the DC offset based on the original data entering the correction circuit, and writes it into the analog-to-digital converter, thereby correcting the DC offset effect and preventing the EEG signal from being saturated and distorted.

[0154] In some implementations, a detection circuit is used to apply a weak excitation AC current source to the scalp surface, collect the voltage difference between the two ends of the EEG collection electrode, and then calculate the impedance of the scalp and EEG collection electrode when in contact through the DSP to determine whether the EEG collection electrode is collecting data properly. If the impedance is less than a preset value (e.g., 15K), the EEG signal is collected properly, that is, the EEG collection electrode is effectively connected to the scalp. Otherwise, the collected signal of the EEG collection electrode cannot be directly used, that is, the EEG collection electrode is not effectively connected to the scalp. The detection circuit can detect the impedance generated by each EEG collection electrode under the excitation of the AC current source to confirm whether the connection of each EEG collection electrode is effective, facilitating timely adjustment and replacement of the EEG collection electrode.

[0155] In actual applications, the host computer also includes an I / O interface and a display. The processor is connected to the display via the I / O interface. When the user is dizzy, the processor adjusts the virtual reality display image and transmits the adjusted display image to the display via the I / O interface for display. In some cases, the display can also display the corresponding test results when detecting the impedance generated by each EEG collection electrode under the stimulation of the AC current source and confirming whether the connection of each EEG collection electrode is valid, so that the user can adjust and replace the EEG collection electrodes in a timely manner.

[0156] In the several embodiments provided in the embodiments of the present invention, it should be understood that the disclosed device can also be implemented in other ways. The device embodiments described above are merely illustrative.

[0157] It should be noted that, in this document, the terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. The terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a..." does not exclude the presence of other identical elements in the process, method, article or device that includes the element.

[0158] Although the embodiments disclosed herein are as described above, the contents described herein are merely embodiments for facilitating understanding of the present invention and are not intended to limit the present invention. Any person skilled in the art may make any modifications and variations in the form and details of the embodiments without departing from the spirit and scope of the present invention. However, the scope of patent protection of the present invention shall remain subject to the scope defined by the appended claims.

Claims

1. A method for preventing vertigo, characterized in that: include: While viewing a display image of a virtual reality device, obtaining EEG signals of a user's target locations, the target locations including the parietal lobe, frontal lobe, temporal lobe, and central point; Extracting target frequency domain characteristic signals of the EEG signal, wherein the target frequency domain characteristic signals include α wave, β wave, θ wave and γ wave; determining whether a change characteristic of each target frequency domain characteristic signal at each target position satisfies corresponding preset conditions, the preset conditions including: whether a change characteristic of an alpha wave at each target position is a downward trend, whether a change characteristic of a beta wave at each target position is a downward trend, whether a change characteristic of a theta wave at each target position is a downward trend, and whether a change characteristic of a gamma wave at each target position is an upward trend; The target frequency domain feature signal whose change characteristics meet the corresponding preset conditions is recorded as a first score, and the target frequency domain feature signal whose change characteristics do not meet the corresponding preset conditions is recorded as a second score; Calculate the total score based on the preset weight of each target position, the preset weight of each target frequency domain characteristic signal, and the score of each target frequency domain characteristic signal; Determining whether the total score exceeds a preset threshold; When the total score exceeds a preset threshold, determining that the user is in a dizzy state; When the user is in a dizzy state, adjusting the display image of the virtual reality device to adjust the dizzy state; When the user is not in a dizzy state, the EEG signal of the user's target position continues to be obtained while the user is watching the display image of the virtual reality device to monitor whether the user is in a dizzy state.

2. The anti-vertigo method according to claim 1, characterized in that: The calculating of the total score according to the preset weight of each target position, the preset weight of each target frequency domain characteristic signal, and the score of each target frequency domain characteristic signal includes: For each target position, a score of each target position is calculated based on the score of each target frequency domain characteristic signal and the preset weight of each target frequency domain characteristic signal; The total score is calculated based on the score of each target position and the preset weight of each target position.

3. The anti-vertigo method according to claim 1, characterized in that: Also includes: When the user is in a dizzy state, determining a dizziness level according to the score interval of the total score; The adjusting the display screen of the virtual reality device includes: determining a method for adjusting the display screen of the virtual reality device according to the dizziness level, and adjusting the display screen of the virtual reality device in this method.

4. The anti-vertigo method according to claim 1, characterized in that: Before extracting the target frequency domain characteristic signal of the EEG signal, the method further includes: The EEG signal is preprocessed to filter out noise in the EEG signal, complete data, and / or remove noise generated by eye movements.

5. The anti-vertigo method according to claim 1, characterized in that: The method of adjusting the display screen of the virtual reality device includes changing the image processing mode, reducing the image delay, changing the image display method, limiting the user's viewing angle, and cutting off the display screen.

6. An anti-vertigo device, characterized in that: include: An acquisition module is used to acquire EEG signals of target positions of a user while viewing a display image of a virtual reality device, wherein the target positions include the parietal lobe, the frontal lobe, the temporal lobe, and the central point; a determination module for extracting target frequency domain characteristic signals of the EEG signal, wherein the target frequency domain characteristic signals include α waves, β waves, θ waves and γ waves; determining whether the change characteristics of each target frequency domain characteristic signal at each target position meet corresponding preset conditions, wherein the preset conditions include: whether the change characteristics of the α waves at each target position are a downward trend, whether the change characteristics of the β waves at each target position are a downward trend, whether the change characteristics of the θ waves at each target position are a downward trend, and whether the change characteristics of the γ waves at each target position are an upward trend; recording the target frequency domain characteristic signals whose change characteristics meet the corresponding preset conditions as a first score, and recording the target frequency domain characteristic signals whose change characteristics do not meet the corresponding preset conditions as a second score; calculating a total score based on the preset weights of each target position, the preset weights of each target frequency domain characteristic signal and the scores of each target frequency domain characteristic signal; determining whether the total score exceeds a preset threshold; and determining that the user is in a dizzy state when the total score exceeds the preset threshold; The adjustment module is used to adjust the display screen of the virtual reality device to regulate the dizziness when the user is in a dizzy state; and when the user is not in a dizzy state, continue to obtain the electroencephalogram signal of the user's target position while watching the display screen of the virtual reality device to monitor whether the user is in a dizzy state.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by one or more processors, the anti-vertigo method according to any one of claims 1 to 5 is implemented.

8. A processing device, characterized in that The method comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the anti-vertigo method according to any one of claims 1 to 5 is implemented.

9. A virtual reality device, characterized in that: include: An EEG acquisition device includes at least one EEG acquisition electrode, wherein the at least one EEG acquisition electrode is connected to a target position of the user and is used to acquire EEG signals at the target position of the user; A host comprising the processing device according to claim 8.

10. The virtual reality device according to claim 9, wherein: The host and the EEG acquisition device are integrally connected so that when the user wears the host, the at least one EEG acquisition electrode can be connected to the target position.

11. The virtual reality device according to claim 9, wherein: The EEG acquisition device further comprises: an amplifier, connected to the EEG acquisition electrode, and configured to amplify the EEG signal acquired by the EEG acquisition electrode; a filter connected to the amplifier and configured to filter out interference signals from the amplified EEG signal; The analog-to-digital converter is connected to the filter and is used to perform analog-to-digital conversion on the EEG signal after the interference signal is filtered out, and transmit the signal to the host in the form of a digital signal.

12. The virtual reality device according to claim 11, wherein: The host also includes: a signal processing device, one end of which is connected to the analog-to-digital converter and the other end of which is connected to the processor, for receiving the EEG signal input by the analog-to-digital converter and transmitting it to the processor, so that the processor obtains the EEG signal of the user's target position; a correction circuit, one end of which is connected to the signal processing device and the other end of which is connected to the analog-to-digital converter, for correcting a DC offset effect; A detection circuit, one end of which is connected to the signal processing device and the other end of which is connected to the EEG acquisition electrode, is used to detect whether the EEG acquisition electrode is effectively connected.

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