WO2018176962A1 - Robot control system and method based on brainwave signals, and head-mounted apparatus - Google Patents

Abstract

A robot control system and method based on brainwave signals, and a head-mounted apparatus (10). The method comprises: collecting brainwave signals by means of an electrode sensor (11) and a reference electrode (12) on the head-mounted apparatus (10); after the brainwave signals are processed, obtaining a brain activity index; transmitting the activity index of the human brain to a robot; and the robot controlling actions of the robot according to corresponding control instructions. The head-mounted apparatus (10) has a simple structure, and the method is simple and practicable, thereby laying the foundation for the development of fields such as bioassay, medical rehabilitation, mind games, smart homes, and intelligent control.

Description

Robot control system and method based on brain wave signal, and wearing device

The present application claims the priority of the Chinese patent application filed on Apr. 1, 2017, the application number is: 201710215999.5, the invention is entitled "A brain wave wearing device and a remote control robot and a method for exercising the brain", the entire contents of which are incorporated in this.

Technical field

The invention relates to the technical field of artificial intelligence robots, in particular to a robot control system and method based on brain wave signals, and a head wear device.

Background technique

Brain waves are the bioelectrical signals generated by humans in the brain during their thinking activities. They are mainly formed by the synchrony of the postsynaptic potentials of a large number of neurons in the cortex, and are the result of the joint activities of many neurons. The development of brainwave technology enables brainwave signals to be applied in a variety of fields, such as biometrics, medical rehabilitation, mind games, smart homes, and intelligent controls.

Summary of the invention

The problem to be solved by the present invention is to provide a robot control system and method based on brain wave signals, and a head-wearing device, which can collect human brain wave signals through a head-mounted device and realize remote control of the robot according to the characteristic values of the corresponding brain wave signals. control.

In order to achieve the above object, the present invention adopts the following technical solutions:

A head-wearing device comprising: a circular arc-shaped body, an electrode sensor is disposed on an inner side surface of the circular-shaped body; and a reference electrode is electrically connected to the electrode sensor.

The present disclosure also provides a robot control system based on an electroencephalogram signal, comprising: a headset and a robot, the headset being communicatively coupled to the robot; the headset comprising: a signal acquisition module for collecting brain a radio signal; a calculation module, configured to process the collected brain wave signal to obtain a brain activity index; and an information sending module, configured to: when in the first working mode, the information sending module: the brain activity The index is sent to the robot; the robot is configured to perform a corresponding operation according to the received brain activity index.

Further, the calculation module is configured to process the collected brain wave signal, and the brain activity index specifically includes: the calculating module, configured to convert the collected brain wave signal into a corresponding digital signal sequence, and Performing digital low-pass filtering to obtain an output sequence; and performing fast Fourier transform on the output sequence to obtain a corresponding spectrum; and calculating a corresponding power spectrum intensity according to the spectrum; and, according to the power spectrum intensity Calculating energy corresponding to each of the δ wave, the θ wave, the α wave, and the β wave; and obtaining the brain activity index according to energy corresponding to each of the δ wave, the θ wave, the α wave, and the β wave.

Further, the calculation module is configured to perform fast Fourier transform on the output sequence, and obtain a specific formula of the corresponding spectrum as follows:

Where X(e ^iω ) is the spectrum, y(n) is the output sequence, and N is a preset number of samples;

The specific formula for calculating the corresponding power spectrum intensity according to the spectrum is as follows:

In the formula, S _x (e ^iω ) is the power spectrum intensity of the brain wave signal, X(e ^iω ) is the spectrum, and N is a preset number of sampling points.

Further, the calculation module is configured to calculate, according to the power spectrum intensity, a specific formula for the energy corresponding to each of the δ wave, the θ wave, the α wave, and the β wave, as follows:

E _δ = ∑S _x (k), 0.1 Hz ≤ f (k) ≤ 3 Hz;

E _θ =∑S _x (k), 4Hz≤f(k)≤7Hz;

E _α =∑S _x (k), 8Hz≤f(k)≤15Hz;

E _β =∑S _x (k), 16Hz≤f(k)≤30Hz;

In the formula, E _δ is the energy corresponding to the δ wave in the brain wave signal, E _θ is the energy corresponding to the θ wave in the brain wave signal, and E _α is the energy corresponding to the α wave in the brain wave signal, E _β Is the energy corresponding to the β wave in the brain wave signal, S _x (k) is the power spectrum intensity of the brain wave signal at each frequency point, and f(k) is the brain wave signal corresponding to the spectrum in the spectrum Frequency point,

N is the preset number of sampling points.

Further, the calculation module is configured to obtain a specific formula of the brain activity index according to the energy corresponding to each of the delta wave, the theta wave, the alpha wave, and the beta wave:

Where V is the brain activity index, E _δ is the energy corresponding to the delta wave in the brain wave signal, E _θ is the energy corresponding to the θ wave in the brain wave signal, and E _α is the brain wave signal The energy corresponding to the medium alpha wave, E _β is the energy corresponding to the beta wave in the brain wave signal.

Further, the robot, configured to perform a corresponding operation according to the received brain activity index, includes: the robot, configured to acquire a preset activity range corresponding to the brain activity index; and, for The control instruction corresponding to the preset activity range is executed, and the corresponding operation is performed.

Further, the robot, configured to perform a corresponding operation according to the received brain activity index, further includes: the robot, when the preset activity range corresponding to the brain activity index cannot be obtained, The robot feeds back the matching failure information to the headset.

Further, the information sending module is further configured to: when in the second working mode, the headset device uses the brain activity index, the energy corresponding to the delta wave, the energy corresponding to the θ wave, and the energy corresponding to the α wave The energy corresponding to the β wave is sent to the robot; the robot performs one step for when the brain activity index is lower than the first preset value, and the energy corresponding to the θ wave and the energy corresponding to the δ wave are consistent with the first When the condition is preset, the robot issues the first prompt information; and when the brain activity index is higher than the second preset value, and the energy corresponding to the beta wave meets the second preset condition, the robot issues the first Two prompt information.

The present disclosure also provides a robot control method based on an electroencephalogram signal, comprising the following steps: 1: a head-mounted device collects a brain wave signal; 2: the head-mounted device processes the brain wave signal to obtain a brain activity index 3: The headgear transmits the brain activity index to the robot when in the first working mode; 4: the robot performs a corresponding operation according to the received brain activity index.

Further, the step 2: the headset device processes the brain wave signal to obtain a brain activity index, and specifically includes the following steps: 21: converting the collected brain wave signal into a corresponding digital signal sequence, and Performing digital low-pass filtering to obtain an output sequence; 22: performing fast Fourier transform on the output sequence to obtain a corresponding spectrum; 23: calculating a corresponding power spectrum intensity according to the spectrum; 24: according to the power spectrum intensity Calculate the energy corresponding to each of the δ wave, the θ wave, the α wave, and the β wave; 25: obtain the brain activity index according to the energy corresponding to each of the δ wave, the θ wave, the α wave, and the β wave.

Further, the step 22: performing a fast Fourier transform on the output sequence to obtain a corresponding formula of the corresponding spectrum is as follows:

Where X(e ^iω ) is the spectrum, y(n) is the output sequence, and N is a preset number of samples;

Step 23: Calculate the specific formula of the corresponding power spectrum intensity according to the spectrum:

In the formula, S _x (e ^iω ) is the power spectrum intensity of the brain wave signal, X(e ^iω ) is the spectrum, and N is a preset number of sampling points.

Further, in the step 24, according to the power spectrum intensity, a specific formula for calculating the energy corresponding to each of the δ wave, the θ wave, the α wave, and the β wave is as follows:

E _δ = ∑S _x (k), 0.1 Hz ≤ f (k) ≤ 3 Hz;

E _θ =∑S _x (k), 4Hz≤f(k)≤7Hz;

E _α =∑S _x (k), 8Hz≤f(k)≤15Hz;

E _β =∑S _x (k), 16Hz≤f(k)≤30Hz;

In the formula, E _δ is the energy corresponding to the δ wave in the brain wave signal, E _θ is the energy corresponding to the θ wave in the brain wave signal, and E _α is the energy corresponding to the α wave in the brain wave signal, E _β Is the energy corresponding to the β wave in the brain wave signal, S _x (k) is the power spectrum intensity of the brain wave signal at each frequency point, and f(k) is the brain wave signal corresponding to the spectrum in the spectrum Frequency point,

N is the preset number of sampling points.

Further, in step 25, according to the energy corresponding to each of the delta wave, the theta wave, the alpha wave, and the beta wave, the specific formula of the brain activity index is:

Where V is the brain activity index, E _δ is the energy corresponding to the delta wave in the brain wave signal, E _θ is the energy corresponding to the θ wave in the brain wave signal, and E _α is the brain wave signal The energy corresponding to the medium alpha wave, E _β is the energy corresponding to the beta wave in the brain wave signal.

Further, the step 4: the robot performing the corresponding operation according to the received brain activity index includes the following steps: 41: the robot acquires a preset activity range corresponding to the brain activity index; 42: The robot performs a corresponding operation according to the control instruction corresponding to the preset activity range.

Further, the step 4: the performing the corresponding control operation by the robot according to the received brain activity index further includes the following steps: 43: when the preset activity range corresponding to the brain activity index cannot be obtained, The robot feeds back the matching failure information to the headset.

Further, after the step 2, the method further includes the following steps: 5: when in the second working mode, the headset device associates the brain activity index, the energy corresponding to the δ wave, the energy corresponding to the θ wave, and the α wave The energy corresponding to the energy of the beta wave is sent to the robot; 6: when the brain activity index is lower than the first preset value, and the energy corresponding to the θ wave and the energy corresponding to the δ wave meet the first preset condition The robot sends a first prompt message; 7: when the brain activity index is higher than the second preset value, and the energy corresponding to the beta wave meets the second preset condition, the robot issues a second prompt message .

The invention relates to a robot control system and method based on brain wave signals, and a wearing device, which collects brain wave signals through electrode sensors and reference electrodes on the headwear device, amplifies and filters and removes ocular electricity and myoelectric signals in brain wave signals. After additional interference such as power frequency, the power spectrum intensity in the brain wave signal is calculated after fast Fourier transform, and the brain activity index is obtained according to the respective power spectrum intensities of the brain wave signals in each frequency band, and the brain is active. The degree index is transmitted to the robot master control system, and the robot master control system generates control commands to control the motion of the robot. The head-wearing device of the present disclosure has a simple structure and a simple and easy method, and lays a foundation for the development of biological detection, medical rehabilitation, mind-seeking games, smart home, intelligent control and the like.

DRAWINGS

Figure 1 is a schematic diagram of waveforms of various bands of brain waves;

2 is a schematic structural view of a headset of the present invention;

3 is a schematic diagram of an internal module of a headset of the present invention;

4 is a schematic diagram of a module of an internal system of the robot of the present invention;

5 is a flow chart of an embodiment of a robot control method based on an electroencephalogram signal according to the present invention;

6 is a flow chart of another embodiment of a robot control method based on an electroencephalogram signal according to the present invention;

7 is a flow chart of another embodiment of a robot control method based on an electroencephalogram signal according to the present invention;

FIG. 8 is a flow chart of another embodiment of a robot control method based on an electroencephalogram signal according to the present invention.

detailed description

Hereinafter, a robot control system and method based on an electroencephalogram signal and a wearing device according to the present invention will be described in detail with reference to the accompanying drawings.

The present disclosure is based on brain waves, and our brains generate brain waves all the time. As shown in Table 1 (the information of Gamma is not shown in the figure) and Figure 1, the frequency range of these spontaneous bioelectric signals is usually between 0.1 Hz and 30 Hz, which can be divided into several bands, Delta (0.1-3). Hertz), Theta (4-7 Hz), Alpha (8-12 Hz), Low Beta (12-15 Hz), Midrange Beta (16-20 Hz), High Beta (20-29.75 Hz), Low Gamma (31 -39.75 Hz), Midrange Gamma (41-49.75 Hz). Therefore, the brain wave signal can be measured by the sensor placed at the head, and the external device can be controlled according to the intensity of each band of the brain wave signal. The state of human mental activity can be reflected in these four brainwave signals.

Table 1

As shown in FIG. 2, a wearing device 10 includes an arc-shaped body (which may be a semi-circular annular frame for the user to wear on the head), and an electrode sensor 11 is disposed on the inner side of the circular arc-shaped body. One side of the human body is an inner side surface, and the reference electrode 12 is electrically connected to the electrode sensor. The reference electrode may be in the shape of a clip. When worn, the electrode sensor is in close contact with the center of the forehead of the person, and the other reference electrode is clamped on the earlobe of the person.

In an embodiment of the present disclosure, a brainwave signal-based robot control system includes: a headset and a robot, the headset being communicatively coupled to the robot;

The headset includes:

a signal acquisition module for collecting brain wave signals;

a calculation module, configured to process the collected brain wave signal to obtain a brain activity index;

An information sending module, configured to send the brain activity index to the robot when in the first working mode;

The robot is configured to perform a corresponding operation according to the received brain activity index.

Specifically, the signal acquisition module is composed of an electrode sensor and a reference electrode. The acquired brain wave signal includes: a first brain wave signal collected by the electrode sensor and a second brain wave signal collected by the reference electrode.

The calculation module is composed of a signal amplification filter circuit and a DSP operation unit. The brain wave signal collected by the signal acquisition module is processed to calculate a corresponding brain activity index.

The first mode of operation refers to a module that controls the robot to perform motion operations through a brain activity index. The brain activity index is sent to the robot by the information sending module, and the robot controls the body to perform the corresponding motion operation according to the brain activity index. In use, the headset and the robot are simultaneously set to the first mode of operation.

The function of the information sending module can be realized by the Bluetooth transceiver module, and can of course be implemented by other communication modules.

As shown in FIG. 3, the internal module of the headset includes a DSP operation unit, and a Bluetooth transceiver module electrically connected to the DSP operation unit, a human-machine interaction unit, a signal amplification filter circuit, a power management unit, an electrode sensor and a reference electrode, and a signal amplification. The filter circuit is electrically connected. The Bluetooth transceiver module can be replaced by other communication modules.

As shown in FIG. 4, the robot includes: a robot master control system; a robot power management system, which is electrically connected with the robot master control system; a robot Bluetooth transceiver module, which is electrically connected with the robot master control system; a robot human-computer interaction system, and a robot master control system. The system is electrically connected; the robot motion control system is electrically connected to the robot master control system; a plurality of servo motor control units, each of which is electrically connected to the robot motion control system.

When the robot Bluetooth transceiver module receives the brain activity index sent by the headset, the robot master control system sends the corresponding control command to the robot motion control system, so that the robot motion control system controls the corresponding servo motor control unit to execute. The operation allows the robot to perform motion operations such as back, forward, left turn, and right turn.

In this embodiment, the brain wave signal of the user is collected by the wearing device, so that the robot performs the corresponding operation according to the brain wave signal of the user, and the user does not need to manually input the control command through the mobile phone or the like, and is convenient to use. Make the robot's control smarter.

In another embodiment of the present disclosure, in addition to the same as the above, a calculation module is configured to process the collected brain wave signal to obtain a brain activity index, which specifically includes:

The calculating module is configured to convert the collected brain wave signal into a corresponding digital signal sequence, and perform digital low-pass filtering to obtain an output sequence;

And performing fast Fourier transform on the output sequence to obtain a corresponding spectrum;

And calculating a corresponding power spectrum intensity according to the spectrum;

And calculating, according to the power spectrum intensity, energy corresponding to each of the δ wave, the θ wave, the α wave, and the β wave;

And the brain activity index is obtained based on energy corresponding to each of the delta wave, theta wave, the alpha wave, and the beta wave.

Specifically, the collected brain wave signal is an analog signal sequence, which is converted into a corresponding digital signal sequence X(n), and digital low-pass filtering is performed to remove clutter and noise unrelated to brain waves, and an output sequence y is obtained. n):

In equation (1), n≥0, and n is the sampling signal at the current time; a _i , b _i is the filter coefficient, N is the order of the filter, M is the M output unit before the filter, n ≥ M .

Preferably, the calculation module is configured to perform fast Fourier transform on the output sequence to obtain a corresponding formula of the corresponding spectrum as follows:

In the formula (2), X(e ^iω ) is a spectrum corresponding to the electroencephalogram signal, y(n) is the output sequence, and N is a preset number of sampling points. The spectrum is also in the form of a sequence.

The specific formula for calculating the corresponding power spectrum intensity according to the spectrum is as follows:

In the formula (3), S _x (e ^iω ) is the power spectrum intensity of the brain wave signal, X(e ^iω ) is the spectrum, and N is a preset number of sampling points.

Preferably, the calculation module is configured to calculate, according to the power spectrum intensity, a specific formula of energy corresponding to each of the delta wave, the theta wave, the alpha wave, and the beta wave, as follows:

E _δ =∑S _x (k), 0.1Hz≤f(k)≤3Hz (4);

E _θ =∑S _x (k), 4Hz≤f(k)≤7Hz (5);

E _α =∑S _x (k), 8Hz≤f(k)≤15Hz (6);

E _β =∑S _x (k), 16Hz≤f(k)≤30Hz (7);

In the formulas (4), (5), (6), and (7), E _δ is the energy corresponding to the δ wave in the brain wave signal, and E _θ is the energy corresponding to the θ wave in the brain wave signal, E _α The energy corresponding to the α wave in the brain wave signal, E _β is the energy corresponding to the β wave in the brain wave signal, and S _x (k) is the power spectrum intensity of the brain wave signal at each frequency point, f(k) ) is the corresponding frequency point of the brain wave signal in the spectrum sequence,

(The result obtained by fast Fourier transform is symmetrical, so only need to look at half), N is the preset number of samples.

During the acquisition, the signals are collected according to the preset sampling points. Different sampling points have different frequencies. Therefore, one frequency point is equivalent to the frequency corresponding to one sampling point. For example, the number of sampling points is N=100, the frequency corresponding to the sampling point of 1-20 is in the range of 0.1Hz-3Hz, the frequency corresponding to the sampling point of 21-25 is in the range of 4Hz-7Hz, and the sampling point of 26-38 corresponds. The frequency is between 8Hz and 15Hz, and the sampling point of 39-50 corresponds to the frequency in the range of 16Hz-30Hz. The meaning of the formula (4) is that the power spectrum intensities corresponding to the frequency points (1-20) in 0.1 Hz - 3 Hz are added. Different sampling points will have different power spectral intensities, and the energy corresponding to the delta waves is the sum of the power spectral intensities in the range of 0.1 Hz to 3 Hz.

Preferably, the calculation module is configured to obtain a specific formula of the brain activity index according to energy corresponding to each of the delta wave, the theta wave, the alpha wave, and the beta wave:

In equation (8), V is the brain activity index, E _δ is the energy corresponding to the δ wave in the brain wave signal, E _θ is the energy corresponding to the θ wave in the brain wave signal, and E _α is the corresponding wave of the α wave in the brain wave signal. Energy, E _β is the energy corresponding to the beta wave in the brain wave signal.

In the present disclosure, the energy of each of the four waves is calculated according to the frequency of the four waves, and then the brain activity index is calculated according to the formula (8). The specific calculation of the brain activity index is disclosed. The beta wave and the alpha wave react to the brain activity. The θ wave and the δ wave reflect the degree of brain dullness. The energy of these four bands changes with the activity of the brain. When the person is in a certain state, the activity index of the human brain is relatively stable. Therefore, the brain activity index is calculated by using equation (8).

In another embodiment of the present disclosure, in addition to the same as above, the robot, for performing the corresponding operation according to the received brain activity index, includes:

The robot is configured to acquire a preset activity range corresponding to the brain activity index;

And, for performing a corresponding operation according to the control instruction corresponding to the preset activity range.

Specifically, a plurality of different activity ranges are stored on the robot end, and different control commands corresponding to each activity range are matched to the active activities after the robot receives the activity index sent by the headset. The range of degrees, thereby obtaining corresponding control commands, and controlling the robot to perform corresponding operations.

For example, the robot stores four preset activity ranges, which are [0-20), which correspond to the back control command; [20-45), which corresponds to the forward control command; [45-70), which corresponds to the left turn control. Command; [70-100], which corresponds to the music control command. When the brain activity index obtained by the robot from the wearing device is 50, the corresponding preset activity range is determined to be [45-70), and the control command corresponding to the preset activity range is a left turn control command. Therefore, the robot is controlled to perform a left turn operation according to this control command.

The specific preset activity range and the control command corresponding to each preset activity range are customized by the user according to actual needs, and the control command may also be changed according to functions that the robot can implement, for example, singing, reading, dancing, and advancing. , back, turn left 30 degrees, and so on.

In other embodiments, the preset activity range can be learned by the robot itself. For example, the user maintains a specific mental state, collects a brain wave activity signal n times, obtains a corresponding brain activity index n times, reads and saves. The range of brain activity at this time saves the maximum and minimum values of the brain activity index corresponding to a specific mental state, and uses it as a preset activity range; and so on, obtains a plurality of preset activity ranges, and then Each preset activity range sets a corresponding control instruction.

In the embodiment, the robot performs corresponding control operations according to the received brain activity index in the above manner, so that the robot is more intelligent, and the user experience is greatly improved.

Preferably, the robot, configured to perform the corresponding operation according to the received brain activity index, further includes: the robot, configured to: when the preset activity range corresponding to the brain activity index cannot be obtained, the robot A matching failure message is fed back to the headset.

Specifically, considering that the received brain activity index may not match the corresponding preset activity range, the logic of feeding back the matching failure information to the head device is added, so that the head device can collect the brain wave again. Signal, make a new loop.

In another embodiment of the present disclosure, in addition to the same as the above, the information sending module is further configured to: when in the second working mode, the headset device corresponds to the brain activity index and the delta wave Energy, the energy corresponding to the θ wave, the energy corresponding to the α wave and the energy corresponding to the β wave are sent to the robot;

The robot performs one step for when the brain activity index is lower than a first preset value, and the energy corresponding to the θ wave and the energy corresponding to the δ wave meet the first preset condition, the robot issues the first The prompt information; and, when the brain activity index is higher than the second preset value, and the energy corresponding to the beta wave meets the second preset condition, the robot issues the second prompt information.

Specifically, the second working mode can be understood as a mode in which the robot is used for reminding. In use, both the headset and the robot are set to the second mode of operation.

In the second mode of operation, after calculating the brain activity index, the headset will send the brain activity index to the robot, and also the energy corresponding to the delta wave, theta wave, the alpha wave, and the beta wave. Send to the robot. After receiving this information, the robot will separately judge the brain activity index and the energy corresponding to each of the four waves. In this embodiment, there are two kinds of prompt information, and according to the received information, it is judged whether the prompt information needs to be sent, and if it needs to be sent, which one is to be sent.

When the θ wave, δ wave energy is strong, and the brain activity is low, it is usually inattention, sleepiness, fatigue, sloppy, drowsiness, and the robot can prompt the user to maintain concentration; that is, when the brain activity index is low When the energy corresponding to the θ wave and the energy corresponding to the δ wave meet the first preset condition at the first preset value, the robot issues the first prompt information. The first prompt information is information prompting the user to maintain concentration. The first preset value may be set according to experience, for example: 40; the first preset condition is greater than the energy corresponding to the alpha wave, and greater than the energy corresponding to the beta wave, that is, the energy corresponding to the θ wave is greater than the energy corresponding to the alpha wave, When the energy corresponding to the β wave is also greater than the energy corresponding to the β wave, the energy corresponding to the θ wave is consistent with the first preset condition; the energy corresponding to the δ wave is greater than the energy corresponding to the α wave, and is greater than the energy corresponding to the β wave, indicating that the energy corresponding to the δ wave is consistent. The first preset condition.

When the brain's beta wave energy is strong and the brain is overactive, we are usually in a state of anger, agitation, nervousness and anxiety. The number of neuronal deaths will continue to increase, the brain's oxygen consumption will accelerate, and human immunity will decline. This state is difficult to control. Own, the robot prompts the user to remain calm; that is, when the energy of the received beta wave meets the second preset condition and the brain activity index is higher than the second preset value, the robot will issue a second prompt message. The second prompt message is information prompting the user to remain calm. When the energy of the received beta wave exceeds the preset beta wave energy threshold, the energy of the beta wave is considered to be in accordance with the second predetermined condition.

It should be noted that the first preset value is smaller than the second preset value, for example, the first preset value is set to 30, and the second preset value is set to 80.

When the brain activity index or the energy corresponding to a particular wave does not meet the conditions, the robot does not issue a prompt message.

The robot in this embodiment can also send corresponding prompt information through the received brain activity index and the energy corresponding to each of the four waves, so that the user can get a reminder and adjust his mental state in time.

This embodiment discloses a robot control method based on a brain wave signal, as shown in FIG. 5, including the following steps:

1: the headgear device collects brain wave signals;

2: the headgear device processes the brain wave signal to obtain a brain activity index;

3: the headset wears the brain activity index to the robot when in the first working mode;

4: The robot performs a corresponding operation according to the received brain activity index.

Specifically, the collected brain wave signal includes: a first brain wave signal collected by the electrode sensor and a second brain wave signal collected by the reference electrode.

The first mode of operation refers to a module that controls the robot to perform motion operations through a brain activity index. The brain activity index is sent to the robot by the information sending module, and the robot controls the body to perform the corresponding motion operation according to the brain activity index. In use, the headset and the robot are simultaneously set to the first mode of operation.

The communication method between the headset and the robot may be a Bluetooth communication method, a Wi-Fi communication method, or the like, and is not limited herein. When the headgear sends the brain activity index to the robot, it will judge whether it is necessary to end the collection of brain waves. If it is, the headset will be closed and stop working. If it is not finished, it will continue to return to step 1 to collect brain waves. signal.

When the robot receives the brain activity index sent by the headset, the robot master system sends a corresponding control command to the robot motion control system, so that the robot motion control system controls the corresponding servo motor control unit to perform the operation, thereby Let the robot complete motion operations such as back, forward, left turn, and right turn.

In this embodiment, the brain wave signal of the user is collected by the wearing device, so that the robot performs the corresponding operation according to the brain wave signal of the user, and the user does not need to manually input the control command through the mobile phone or the like, and is convenient to use. Make the robot's control smarter.

In another embodiment of the present disclosure, in addition to the above, a brain wave signal based robot control method, step 2: the headset device processes the brain wave signal to obtain a brain activity index specific Includes the following steps:

21: Convert the collected brain wave signal into a corresponding digital signal sequence, and perform digital low-pass filtering to obtain an output sequence;

22: Perform fast Fourier transform on the output sequence to obtain a corresponding spectrum;

23: Calculate a corresponding power spectrum intensity according to the spectrum;

24: calculating, according to the power spectrum intensity, energy corresponding to each of the δ wave, the θ wave, the α wave, and the β wave;

25: The brain activity index is obtained according to energy corresponding to each of the delta wave, theta wave, the alpha wave, and the beta wave.

Specifically, the collected brain wave signal is an analog signal sequence, which is converted into a corresponding digital signal sequence X(n), and digital low-pass filtering is performed to remove clutter and noise unrelated to brain waves, and an output sequence y is obtained. n):

In equation (1), n≥0, and n is the sampling signal at the current time; a _i , b _i is the filter coefficient, N is the order of the filter, M is the M output unit before the filter, n ≥ M .

Preferably, step 22: performing a fast Fourier transform on the output sequence to obtain a corresponding formula of the corresponding spectrum is as follows:

In the formula (2), X(e ^iω ) is a spectrum corresponding to the electroencephalogram signal, y(n) is the output sequence, and N is a preset number of sampling points.

Step 23: Calculate the specific formula of the corresponding power spectrum intensity according to the spectrum:

In the formula (3), S _x (e ^iω ) is the power spectrum intensity of the brain wave signal, X(e ^iω ) is the spectrum, and N is a preset number of sampling points.

Preferably, in step 24, according to the power spectrum intensity, a specific formula for calculating the energy corresponding to each of the delta wave, the theta wave, the alpha wave, and the beta wave is as follows:

E _δ =∑S _x (k), 0.1Hz≤f(k)≤3Hz (4);

E _θ =∑S _x (k), 4Hz≤f(k)≤7Hz (5);

E _α =∑S _x (k), 8Hz≤f(k)≤15Hz (6);

E _β =∑S _x (k), 16Hz≤f(k)≤30Hz (7);

In the formulas (4), (5), (6), and (7), E _δ is the energy corresponding to the δ wave in the brain wave signal, and E _θ is the energy corresponding to the θ wave in the brain wave signal, E _α The energy corresponding to the α wave in the brain wave signal, E _β is the energy corresponding to the β wave in the brain wave signal, and S _x (k) is the power spectrum intensity of the brain wave signal at each frequency point, f(k) ) is the corresponding frequency point of the brain wave signal in the spectrum sequence,

(The result obtained by fast Fourier transform is symmetrical, so only need to look at half), N is the preset number of samples.

Preferably, in step 25, according to the energy corresponding to each of the delta wave, theta wave, the alpha wave, and the beta wave, the specific formula of the brain activity index is:

In equation (8), V is the brain activity index, E _δ is the energy corresponding to the δ wave in the brain wave signal, E _θ is the energy corresponding to the θ wave in the brain wave signal, and E _α is the corresponding wave of the α wave in the brain wave signal. Energy, E _β is the energy corresponding to the beta wave in the brain wave signal.

In the present disclosure, according to the frequencies of the four waves, the energy corresponding to each of the four waves is calculated, and then the brain activity index is calculated according to the formula (8). The specific calculation of the brain activity index is disclosed. The beta wave and the alpha wave react to the brain activity. The θ wave and the δ wave reflect the degree of brain dullness. The energy of these four bands changes with the activity of the brain. When the person is in a certain state, the activity index of the human brain is relatively stable. Therefore, the brain activity index is calculated by using equation (8).

In another embodiment of the present disclosure, in addition to the same as described above, as shown in FIG. 7, step 4: the robot performs a corresponding operation according to the received brain activity index, including the following steps:

41: The robot acquires a preset activity range corresponding to the brain activity index;

42: The robot performs a corresponding operation according to the control instruction corresponding to the preset activity range.

Specifically, a plurality of different activity ranges are stored on the robot end, and different control commands corresponding to each activity range are matched to the active activities after the robot receives the activity index sent by the headset. The range of degrees, thereby obtaining corresponding control commands, and controlling the robot to perform corresponding operations. For specific implementation examples, please refer to the corresponding method embodiments, and details are not described herein.

The specific preset activity range and the control command corresponding to each preset activity range are customized by the user according to actual needs, and the control command may also be changed according to functions that the robot can implement, for example, singing, reading, dancing, and advancing. , back, turn left 30 degrees, and so on.

In other embodiments, the preset activity range can be learned by the robot itself. For example, the user maintains a specific mental state, collects a brain wave activity signal n times, obtains a corresponding brain activity index n times, reads and saves. The range of brain activity at this time saves the maximum and minimum values of the brain activity index corresponding to a specific mental state, and uses it as a preset activity range; and so on, obtains a plurality of preset activity ranges, and then Each preset activity range sets a corresponding control instruction.

In the embodiment, the robot performs corresponding control operations according to the received brain activity index in the above manner, so that the robot is more intelligent, and the user experience is greatly improved.

Preferably, step 4: the performing the corresponding control operation by the robot according to the received brain activity index further comprises the following steps:

43: When the preset activity range corresponding to the brain activity index cannot be obtained, the robot feeds back the matching failure information to the wearing device.

Specifically, considering that the received brain activity index may not match the corresponding preset activity range, the logic of feeding back the matching failure information to the head device is added, so that the head device can collect the brain wave again. Signal, make a new loop.

In another embodiment of the present disclosure, in addition to the same as described above, as shown in FIG. 8, the robot control method based on the device signal, the step 2 device processes the brain wave signal to obtain a brain. The activity index also includes the following steps:

5: when in the second working mode, the headset transmits the brain activity index, the energy corresponding to the δ wave, the energy corresponding to the θ wave, the energy corresponding to the α wave, and the energy corresponding to the β wave to the robot;

6: when the brain activity index is lower than the first preset value, and the energy corresponding to the θ wave and the energy corresponding to the δ wave meet the first preset condition, the robot issues the first prompt information;

7: When the brain activity index is higher than the second preset value, and the energy corresponding to the beta wave meets the second preset condition, the robot issues the second prompt information.

Specifically, the second working mode can be understood as a mode in which the robot is used for reminding. When in use, both the headset and the robot are set to the second mode of operation.

In the second mode of operation, after calculating the brain activity index, the headset will send the brain activity index to the robot, and also the energy corresponding to the delta wave, theta wave, the alpha wave, and the beta wave. Send to the robot. After receiving this information, the robot will separately judge the brain activity index and the energy corresponding to each of the four waves. In this embodiment, there are two kinds of prompt information, and according to the received information, it is judged whether the prompt information needs to be sent, and if it needs to be sent, which one is to be sent.

When the θ wave, δ wave energy is strong, and the brain activity is low, it is usually inattention, sleepiness, fatigue, sloppy, drowsiness, and the robot can prompt the user to maintain concentration; that is, when the brain activity index is low When the energy corresponding to the θ wave and the energy corresponding to the δ wave meet the first preset condition at the first preset value, the robot issues the first prompt information. The first prompt information is information prompting the user to maintain concentration. The first preset value may be set according to experience, for example: 35; the first preset condition is greater than the energy corresponding to the alpha wave, and greater than the energy corresponding to the beta wave, that is, the energy corresponding to the θ wave is greater than the energy corresponding to the alpha wave, When the energy corresponding to the β wave is also greater than the energy corresponding to the β wave, the energy corresponding to the θ wave is consistent with the first preset condition; the energy corresponding to the δ wave is greater than the energy corresponding to the α wave, and is greater than the energy corresponding to the β wave, indicating that the energy corresponding to the δ wave is consistent. The first preset condition.

When the brain's beta wave energy is strong and the brain is overactive, we are usually in a state of anger, agitation, nervousness and anxiety. The number of neuronal deaths will continue to increase, the brain's oxygen consumption will accelerate, and human immunity will decline. This state is difficult to control. Own, the robot prompts the user to remain calm; that is, when the energy of the received beta wave meets the second preset condition and the brain activity index is higher than the second preset value, the robot will issue a second prompt message. The second prompt message is information prompting the user to remain calm. When the energy of the received beta wave exceeds the preset beta wave energy threshold, the energy of the beta wave is considered to be in accordance with the second predetermined condition.

It should be noted that the first preset value is smaller than the second preset value, for example, the first preset value is set to 30, and the second preset value is set to 80.

When the brain activity index or the energy corresponding to a particular wave does not meet the conditions, the robot does not issue a prompt message.

The robot in this embodiment can also send corresponding prompt information through the received brain activity index and the corresponding energy of the four waves, so that the user can get a reminder and adjust his mental state in time. Through the prompts sent by the robot, the user can learn to control his emotional and mental state, so that he often maintains an excellent and healthy alpha wave state.

The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, improvements, etc., which are within the spirit and scope of the invention, are intended to be included within the scope of the appended claims.

Claims (17)

1. A headgear device, comprising:

   a circular arc body provided with an electrode sensor on an inner side surface of the circular arc body;

   a reference electrode electrically connected to the electrode sensor.
2. A robot control system based on an electroencephalogram signal, comprising: a headset and a robot, wherein the headset is in communication connection with the robot;

   The headset includes:

   a signal acquisition module for collecting brain wave signals;

   a calculation module, configured to process the collected brain wave signal to obtain a brain activity index;

   An information sending module, configured to send the brain activity index to the robot when in the first working mode;

   The robot is configured to perform a corresponding operation according to the received brain activity index.
3. The brainwave signal-based robot control system according to claim 2, wherein the calculation module is configured to process the collected brain wave signal, and the brain activity index comprises:

   The calculating module is configured to convert the collected brain wave signal into a corresponding digital signal sequence, and perform digital low-pass filtering to obtain an output sequence;

   And performing fast Fourier transform on the output sequence to obtain a corresponding spectrum;

   And calculating a corresponding power spectrum intensity according to the spectrum;

   And calculating, according to the power spectrum intensity, energy corresponding to each of the δ wave, the θ wave, the α wave, and the β wave;

   And the brain activity index is obtained based on energy corresponding to each of the delta wave, theta wave, the alpha wave, and the beta wave.
4. The brainwave signal-based robot control system according to claim 3, wherein the calculation module is configured to perform fast Fourier transform on the output sequence to obtain a corresponding formula of the corresponding spectrum as follows:

   

   Where X(e ^iω ) is the spectrum, y(n) is the output sequence, and N is a preset number of samples;

   The specific formula for calculating the corresponding power spectrum intensity according to the spectrum is as follows:

   

   In the formula, S _x (e ^iω ) is the power spectrum intensity of the brain wave signal, X(e ^iω ) is the spectrum, and N is a preset number of sampling points.
5. The brainwave signal-based robot control system according to claim 3, wherein the calculation module is configured to calculate corresponding values of the δ wave, the θ wave, the α wave, and the β wave according to the power spectrum intensity. The specific formula of energy is as follows:

   E _δ = ∑S _x (k), 0.1 Hz ≤ f (k) ≤ 3 Hz;

   E _θ =∑S _x (k), 4Hz≤f(k)≤7Hz;

   E _α =∑S _x (k), 8Hz≤f(k)≤15Hz;

   E _β =∑S _x (k), 16Hz≤f(k)≤30Hz;

   In the formula, E _δ is the energy corresponding to the δ wave in the brain wave signal, E _θ is the energy corresponding to the θ wave in the brain wave signal, and E _α is the energy corresponding to the α wave in the brain wave signal, E _β The energy corresponding to the β wave in the brain wave signal, S _x (k) is the power spectrum intensity of the brain wave signal at each frequency point, and f(k) is the frequency corresponding to the brain wave signal in the frequency spectrum. point,

   

   N is the preset number of sampling points.
6. The brainwave signal-based robot control system according to claim 3, wherein the calculation module is configured to obtain the brain according to energy corresponding to each of the delta wave, the theta wave, the alpha wave, and the beta wave. The specific formula for the activity index is:

   

   Where V is the brain activity index, E _δ is the energy corresponding to the delta wave in the brain wave signal, E _θ is the energy corresponding to the θ wave in the brain wave signal, and E _α is the brain wave signal The energy corresponding to the medium alpha wave, E _β is the energy corresponding to the beta wave in the brain wave signal.
7. The brain wave signal-based robot control method according to claim 2, wherein the robot is configured to perform a corresponding operation according to the received brain activity index:

   The robot is configured to acquire a preset activity range corresponding to the brain activity index;

   And, for performing a corresponding operation according to the control instruction corresponding to the preset activity range.
8. The brainwave signal-based robot control method according to claim 7, wherein the robot, for performing the corresponding operation according to the received brain activity index, further comprises:

   The robot is configured to feed back the matching failure information to the wearing device when the preset activity range corresponding to the brain activity index cannot be acquired.
9. The brain wave signal-based robot control method according to claim 2, wherein:

   The information sending module is further configured to: when in the second working mode, the headwear device uses the brain activity index, the energy corresponding to the δ wave, the energy corresponding to the θ wave, the energy corresponding to the α wave, and the β wave Corresponding energy is sent to the robot;

   The robot performs one step for when the brain activity index is lower than a first preset value, and the energy corresponding to the θ wave and the energy corresponding to the δ wave meet the first preset condition, the robot issues the first The prompt information; and, when the brain activity index is higher than the second preset value, and the energy corresponding to the beta wave meets the second preset condition, the robot issues the second prompt information.
10. A robot control method based on an electroencephalogram signal, comprising the following steps:

    1: the headgear device collects brain wave signals;

    2: the headgear device processes the brain wave signal to obtain a brain activity index;

    3: the headset wears the brain activity index to the robot when in the first working mode;

    4: The robot performs a corresponding operation according to the received brain activity index.
11. The brain wave signal-based robot control method according to claim 10, wherein the step 2: the headgear device processes the brain wave signal to obtain a brain activity index, and specifically includes the following steps:

    21: Convert the collected brain wave signal into a corresponding digital signal sequence, and perform digital low-pass filtering to obtain an output sequence;

    22: Perform fast Fourier transform on the output sequence to obtain a corresponding spectrum;

    23: Calculate a corresponding power spectrum intensity according to the spectrum;

    24: calculating, according to the power spectrum intensity, energy corresponding to each of the δ wave, the θ wave, the α wave, and the β wave;

    25: The brain activity index is obtained according to energy corresponding to each of the delta wave, theta wave, the alpha wave, and the beta wave.
12. The brain wave signal-based robot control method according to claim 11, wherein:

    Step 22: Perform fast Fourier transform on the output sequence to obtain a specific formula of the corresponding spectrum as follows:

    

    Where X(e ^iω ) is the spectrum, y(n) is the output sequence, and N is a preset number of samples;

    Step 23: Calculate the specific formula of the corresponding power spectrum intensity according to the spectrum:

    

    In the formula, S _x (e ^iω ) is the power spectrum intensity of the brain wave signal, X(e ^iω ) is the spectrum, and N is a preset number of sampling points.
13. The brain wave signal-based robot control method according to claim 11, wherein the step 24: calculating the energy corresponding to each of the δ wave, the θ wave, the α wave, and the β wave according to the power spectrum intensity. The specific formula is as follows:

    E _δ = ∑S _x (k), 0.1 Hz ≤ f (k) ≤ 3 Hz;

    E _θ =∑S _x (k), 4Hz≤f(k)≤7Hz;

    E _α =∑S _x (k), 8Hz≤f(k)≤15Hz;

    E _β =∑S _x (k), 16Hz≤f(k)≤30Hz;

    In the formula, E _δ is the energy corresponding to the δ wave in the brain wave signal, E _θ is the energy corresponding to the θ wave in the brain wave signal, and E _α is the energy corresponding to the α wave in the brain wave signal, E _β The energy corresponding to the β wave in the brain wave signal, S _x (k) is the power spectrum intensity of the brain wave signal at each frequency point, and f(k) is the frequency corresponding to the brain wave signal in the frequency spectrum. point,

    

    N is the preset number of sampling points.
14. The brain wave signal-based robot control method according to claim 11, wherein the step 25: obtaining the brain activity according to energy corresponding to each of the delta wave, theta wave, the alpha wave, and the beta wave. The specific formula of the index is:

    

    Where V is the brain activity index, E _δ is the energy corresponding to the delta wave in the brain wave signal, E _θ is the energy corresponding to the θ wave in the brain wave signal, and E _α is the brain wave signal The energy corresponding to the medium alpha wave, E _β is the energy corresponding to the beta wave in the brain wave signal.
15. The brain wave signal-based robot control method according to claim 10, wherein the step 4: the robot performs a corresponding operation according to the received brain activity index comprises the following steps:

    41: The robot acquires a preset activity range corresponding to the brain activity index;

    42: The robot performs a corresponding operation according to the control instruction corresponding to the preset activity range.
16. The brain wave signal-based robot control method according to claim 15, wherein the step 4: the robot performs a corresponding control operation according to the received brain activity index further comprises the following steps:

    43: When the preset activity range corresponding to the brain activity index cannot be obtained, the robot feeds back the matching failure information to the wearing device.
17. The headset control signal-based robot control method according to claim 10, wherein the step 2 further comprises the following steps:

    5: when in the second working mode, the headset transmits the brain activity index, the energy corresponding to the δ wave, the energy corresponding to the θ wave, the energy corresponding to the α wave, and the energy corresponding to the β wave to the robot;

    6: when the brain activity index is lower than the first preset value, and the energy corresponding to the θ wave and the energy corresponding to the δ wave meet the first preset condition, the robot issues the first prompt information;

    7: When the brain activity index is higher than the second preset value, and the energy corresponding to the beta wave meets the second preset condition, the robot issues the second prompt information.

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