CN108965625B - Automatic calling device based on brain-computer interface and training method thereof - Google Patents
Automatic calling device based on brain-computer interface and training method thereof Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M11/00—Telephonic communication systems specially adapted for combination with other electrical systems
- H04M11/02—Telephonic communication systems specially adapted for combination with other electrical systems with bell or annunciator systems
- H04M11/022—Paging systems
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M11/00—Telephonic communication systems specially adapted for combination with other electrical systems
- H04M11/02—Telephonic communication systems specially adapted for combination with other electrical systems with bell or annunciator systems
- H04M11/027—Annunciator systems for hospitals
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/80—Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/90—Services for handling of emergency or hazardous situations, e.g. earthquake and tsunami warning systems [ETWS]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M2242/00—Special services or facilities
- H04M2242/04—Special services or facilities for emergency applications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M2242/00—Special services or facilities
- H04M2242/18—Automated outdialling systems
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Abstract
The invention relates to an automatic pager based on a brain-computer interface and a training method thereof, belonging to the field of human-computer interaction. The invention comprises a wireless earphone and a pager. When the user has the call demand under a certain condition, can wear wireless earphone, open the earphone switch can, wireless earphone will be in user's brain electricity collection under this condition and send to the calling set through the bluetooth, and the calling result that the calling set produced is analyzed out according to the eigenvalue of brain electricity signal to its inside brain computer interface module. The invention combines the brain-computer interface and the neural network, so that the automation degree of the pager is increased, and meanwhile, great convenience is provided for users, especially users who are not convenient to move. The device has the advantages of convenience and rapidness, and is quite simple to use.
Description
Technical Field
The invention relates to an automatic calling device based on a brain-computer interface and a training method thereof, belonging to the technical field of human-computer interaction.
Background
In recent years, a brain-computer interface has attracted attention from various fields as a new communication device. The brain-computer interface device can better transmit the information of the brain directly to the outside, and the process can be completed without the participation of limbs and muscles. The brain electrical signal analysis device can analyze the brain electrical signal and then directly act on external equipment, and the external equipment can complete a certain task according to the instructions of the brain electrical signal. Especially in the field of rehabilitation, the application and research of the medicine are more extensive.
The RBF neural network has wide application in many aspects, such as prediction and classification. Its mapping capability is relatively strong, and when its number of layers is appropriate and its amount of data trained is sufficient, it can approximate some non-linear function.
The traditional caller needs to hold the caller or call the caller to the calling station, but is somewhat inconvenient for the persons who are not convenient in action. In addition, when a dangerous condition is met, the user can automatically call for help through the wireless earphone under the dangerous condition. The traditional caller shows great inconvenience and is difficult to meet the needs of life.
Disclosure of Invention
The invention provides an automatic calling device based on a brain-computer interface, which is used for solving the problem that the traditional calling device is inconvenient for a user who is inconvenient to move.
The technical scheme of the invention is as follows: the calling device based on the brain-computer interface comprises a wireless earphone and a calling device, wherein the calling device comprises a microprocessor, and a display screen and button keys are arranged on the surface of the calling device; the microprocessor is used for preprocessing the electroencephalogram signals transmitted by the wireless earphone, extracting and classifying features, and transmitting decision information of the brain-computer interface to a called object through the 4G network module; the display screen and the button keys complete the input and display work together. The wireless earphone is used for collecting electroencephalogram signals generated by a user in a certain environment.
Furthermore, the caller comprises a microprocessor, and a display screen and button keys are arranged on the surface of the microprocessor; the microprocessor is respectively connected with the preprocessing module, the feature extraction module, the feature classification module, the Bluetooth receiving device, the power module, the 4G network module and the storage module. The preprocessing module is used for denoising and signal amplification of the electroencephalogram signals transmitted by the wireless earphone; the characteristic extraction module analyzes and extracts the characteristic values of the maximum peak value, the minimum peak value, the large fluctuation time of the electroencephalogram signal and the like of the received electroencephalogram signal; the characteristic classification module is used for making a decision on an event characteristic value of a user call by using an rbf neural network algorithm; the power supply module is used for providing electric energy for the normal operation of the caller; the Bluetooth receiving device is used for receiving the electroencephalogram signals sent by the wireless earphone; the storage module is used for storing information set by a user; the 4G network module is used for communicating with the object called by the user. The user character characteristic value setting module is used for setting the characteristics of the inward and outward characters of the user; the calling object selection module is used for selecting an object needing calling by a user; the called object information storage module is used for storing some information of the called object.
Further, the wireless earphone comprises a switch module and an electrode module which are integrated with a Bluetooth switch, a power switch and an electroencephalogram acquisition switch; the switch module integrating the Bluetooth switch, the power switch and the electroencephalogram acquisition switch is used for the functions of the Bluetooth switch, the power switch and the electroencephalogram acquisition switch; the electrode module is used for inducing brain electrical signals generated by the brain on the head.
The automatic caller training method based on the brain-computer interface comprises the following steps: firstly, enabling a tester to meet the requirement of calling in general daily life, collecting electroencephalogram signals of the tester under general daily conditions by using a wireless earphone, and extracting the characteristic values of the electroencephalogram signals of the tester at the moment through a characteristic extraction module, namely maximum peak values, minimum peak values and time values of large-amplitude fluctuation of the electroencephalogram signals; setting the character characteristic value of the tester on the caller; obtaining a group of training data at the moment, wherein the input data are a maximum peak value, a minimum peak value, a large-amplitude fluctuation time value of the electroencephalogram signal and a character characteristic value of a tester, and the output data are characteristic values corresponding to general conditions and are 1; at least 500 different testers were tested on a general daily basis. Secondly, testing in emergency, extracting the electroencephalogram characteristic value of the tester in emergency, setting the character characteristic value of the tester, outputting 2 in emergency, and testing at least 500 different testers. And finally, testing under the ultra-emergency condition, extracting the characteristic value of the electroencephalogram signal of the tester under the ultra-emergency condition, setting the character characteristic value of the tester, outputting to be 3 under the ultra-emergency condition, and testing at least 500 different testers. And training an rbf neural network of a characteristic classification module of the caller according to at least 1500 groups of input and output data obtained by the test, wherein the structure of the rbf neural network is shown in figure 2. The rbf neural network comprises a hidden layer, an input layer and an output layer, wherein the output of the hidden layer is shown as the formula (1)
In equation (1) i ∈ [ 110]10 neurons representing the first layer hidden layer; xcRepresents the center value of the Gaussian function, which is a column vector co-dimensional with X; sigmaiIs the width of the gaussian function; x is an input vector set, and is shown as an equation (2):
the output of the network is thus obtained as shown in equation (3):
training rbf according to at least 1500 data provided above, and performing at least 1500 times of iterative training according to gradient descent method to obtain final sigmai、Xc、ωi1。
The invention has the beneficial effects that: the pager can set the character characteristic value and the object which the user wants to call according to the character characteristics of the user, and provides great convenience for the user by combining with the brain-computer interface.
Drawings
FIG. 1 is a schematic diagram of a pager;
FIG. 2 is a diagram of an rbf neural network architecture for a feature classification module;
fig. 3 is a schematic diagram of the operation of the trained pager.
Detailed Description
The following detailed description of the invention refers to the accompanying drawings.
Example 1: the automatic calling device comprises a wireless earphone and a calling device. As shown in fig. 1, the wireless earphone comprises a wireless earphone and a pager, wherein a microprocessor is arranged in the pager, the microprocessor is respectively connected with a bluetooth receiving device, a preprocessing module, a feature extraction module, a feature classification module, a 4G network module, a power supply module and a storage module, a display screen and button keys are arranged on the surface of the pager, corresponding operations can be completed on the display screen according to corresponding prompts, and the button keys are connected with an internal microprocessor; the Bluetooth receiving device is used for receiving the electroencephalogram signals sent by the wireless earphone; the preprocessing module is used for denoising and signal amplification of the received electroencephalogram signals; the characteristic extraction module is used for analyzing and extracting the maximum peak value and the minimum peak value of the received electroencephalogram signal and the large fluctuation time of the electroencephalogram signal; the power supply module is used for providing electric energy for the normal operation of the caller; the characteristic classification module is used for deciding an object which needs to be called by a user by utilizing an rbf neural network; the storage module is used for storing information set by a user; the 4G network module is used for communicating with the object called by the user.
The wireless earphone is used for acquiring electroencephalogram information of a user and sending the electroencephalogram information to the caller through Bluetooth so as to provide the electroencephalogram information for the microprocessor in the caller for further processing; the microprocessor is used for analyzing the received brain electrical signal and transmitting the brain electrical signal to the communication equipment of the called object.
The training method of the automatic caller of the brain-computer interface, as shown in fig. 3, includes the following steps:
step 1: determining character characteristic value of the tested person and setting the character characteristic value on the display screen of the pager, wherein the character characteristic value is x4；
Step 2: wearing a wireless earphone and turning on a switch;
and step 3: the method comprises the steps that an electroencephalogram signal of a tester in a certain specific environment is transmitted to a pager by using a wireless earphone, a characteristic extraction module of the pager extracts an electroencephalogram signal characteristic value of the tester in the environment, namely a maximum peak value, a minimum peak value and a large-amplitude electroencephalogram signal fluctuation time value of the electroencephalogram signal, a wireless earphone switch is turned off, and the maximum peak value, the minimum peak value and the large-amplitude electroencephalogram signal fluctuation time value are set to be x1、x2、x3；
And 4, step 4: determining the corresponding event characteristic value under the specific environment, and setting the event characteristic value as yd；
And 5: the characteristic value of an event called by a caller is used as ideal output data of a characteristic classification module, the maximum peak value, the minimum peak value, the large fluctuation time of an electroencephalogram signal and the characteristic value of the user character are used as input data, and the data are utilized to train the rbf neural network of the characteristic module;
step 6: and (3) restarting to replace the training of the rbf neural network of the feature classification module by different testers from the step 1.
Example 2: the caller needs to store the information of the calling object and the event information which are possibly used on the caller according to the corresponding prompts on the display screen before the caller uses the caller, and can complete the storage by referring to tables 1 and 2, and then the setting of the character characteristic value and the setting of the characteristic value of the calling object are carried out according to the character characteristics of the caller, and the settings are carried out under the corresponding prompts on the display screen, as shown in fig. 1. The information is stored under the guidance of the related technicians, and the user can use the information for calling by setting the character characteristic value and the called object according to the character characteristics of the user.
The working principle of the invention is as follows: before using the caller, the user firstly sets the calling object and setting the character characteristic value x according to the requirement4And the storage of event information and called object information, which can be accomplished under the guidance of the skilled person with reference to tables 1 and 2.
TABLE 1 Caller feature value Table
| Called object feature value | Name of called object | Called object contact |
| 0 | Father and father | *** |
| 1 | Mother | *** |
| 2 | Brother | *** |
| 3 | Sister of a patient | *** |
| 4 | Brother | *** |
| 5 | Sister | *** |
| … | … | … |
Table 2 event information table
The characteristic value of the event information needs to be consistent with the characteristic value of the event when the caller trains; then, the user wears the wireless earphone and turns on the switch, when calling is needed under a certain condition, the wireless earphone collects the electroencephalogram signals of the user under the condition, the signals are sent to the caller through the Bluetooth device, and the electroencephalogram signals are extracted by the feature extraction module of the microprocessorFeature value extraction, i.e. x1、x2、x3And obtaining the event characteristic value under the current situation according to the trained rbf neural network in the characteristic classification module, as shown in formula (4):
y is the event characteristic value under the current condition, the control unit sends the information corresponding to the condition characteristic value in the table 2 to calling object communication equipment preset by the user through a 4G module, and the schematic diagram of the caller trained by the user is shown in fig. 2.
Example 3: the use of the beeper is described by way of example when property is threatened. At the moment, assuming that the property of the user is threatened and needs to be called for requesting help, the user wears the wireless earphone to turn on the switch, the microprocessor of the pager carries out feature extraction on the received electroencephalogram signal, the trained rbf in the feature classification module predicts that the event feature value of the current situation is 2 according to the current input data, and the microprocessor of the pager can effectively transmit information to the communication device of the called object according to the stored information.
When the calling device is used, when a user has a calling requirement in a certain specific environment, the user only needs to wear the wireless earphone and turn on the switch for 3 to 5 seconds, and then the calling device can automatically send calling information to a callee.
In the using process, the steps which need to be operated by the user are few, and the user can input the information of the called object by himself or can be finished by a technician who provides the product. The input of information for the called object can be operated with reference to table 1. The microprocessor may be an ARM family of processors.
Although the present invention has been described in detail with reference to the drawings and the accompanying tables, the present invention is not limited thereto, and is not limited to the above embodiments. So long as they do not constitute a basis for the present invention and they do not constitute a departure from the spirit and scope of the invention as defined by the appended claims.
Claims (5)
1. A training method of an automatic calling device based on a brain-computer interface is characterized in that the automatic calling device comprises a wireless earphone and a calling device, a microprocessor is arranged in the calling device, the microprocessor is respectively connected with a Bluetooth receiving device, a preprocessing module, a feature extraction module, a feature classification module, a 4G network module, a power supply module and a storage module, a display screen and button keys are arranged on the surface of the calling device, corresponding operations can be completed on the display screen according to corresponding prompts, and the button keys are connected with the internal microprocessor; the Bluetooth receiving device is used for receiving the electroencephalogram signals sent by the wireless earphone; the preprocessing module is used for denoising and signal amplification of the received electroencephalogram signals; the characteristic extraction module is used for analyzing and extracting the maximum peak value and the minimum peak value of the received electroencephalogram signal and the large fluctuation time of the electroencephalogram signal; the power supply module is used for providing electric energy for the normal operation of the caller; the characteristic classification module is used for deciding the object to be called by the user by using an RBF algorithm; the storage module is used for storing information set by a user; the 4G network module is used for communicating with an object called by a user;
the wireless earphone is used for acquiring electroencephalogram information of a user and sending the electroencephalogram information to the caller through Bluetooth so as to provide the electroencephalogram information for the microprocessor in the caller for further processing; the microprocessor is used for analyzing the received brain electrical signal and transmitting the brain electrical signal to the communication equipment of the called object;
the training method of the automatic caller comprises the following steps:
step 1: determining character characteristic value of the tested person and setting the character characteristic value on the display screen of the pager, wherein the character characteristic value is x4；
Step 2: wearing a wireless earphone and turning on a switch;
and step 3: the wireless earphone is utilized to transmit the EEG signal of a tester in a certain specific environment to the caller, and the feature extraction module of the caller extracts the EEG signal feature value of the tester in the environment, namely the EEG signalThe maximum peak value, the minimum peak value and the electroencephalogram signal large fluctuation time value are simultaneously closed, and the maximum peak value, the minimum peak value and the electroencephalogram signal large fluctuation time value are set as x1、x2、x3；
And 4, step 4: determining the corresponding event characteristic value under the specific environment, and setting the event characteristic value as yd；
And 5: the characteristic value of an event called by a caller is used as ideal output data of a characteristic classification module, the maximum peak value, the minimum peak value, the large fluctuation time of an electroencephalogram signal and the characteristic value of the user character are used as input data, and the data are utilized to train the rbf neural network of the characteristic module;
step 6: and (3) restarting to replace the training of the rbf neural network of the feature classification module by different testers from the step 1.
2. The method for training an automatic pager based on brain-computer interface as claimed in claim 1, wherein: the setting of the character characteristic value and the setting of the event characteristic value are finished through the prompt of a display screen of a calling device, the setting of the characteristic value of the event is only used for training the calling device by technicians, and common users cannot use the setting.
3. The method for training an automatic pager based on brain-computer interface as claimed in claim 1, wherein: the specific environments are divided into three categories: the system comprises a general case, an emergency case and a super-emergency case, wherein the general case takes an event characteristic value as 1, the emergency case takes an event characteristic value as 2, the super-emergency case takes an event characteristic value as 3, and an event characteristic value table can be stored in a storage unit in advance, wherein the general case is defined as trivial matters needing help in daily life; an emergency is defined as a situation where property or interest is compromised; a super-emergency is defined as a situation where life safety is compromised.
4. The method for training an automatic pager based on brain-computer interface as claimed in claim 1, wherein: the character characteristic values comprise 0, 1 and 2, wherein 0 is a character which represents inward comparison, 1 represents a character which is neither inward nor outward, and 2 represents outward character, and the character characteristic values need to be evaluated by a user according to self-understanding and people around the user in daily life.
5. The method for training an automatic pager based on brain-computer interface as claimed in claim 1, wherein: the training of the rbf neural network of the feature classification module comprises the following specific steps: the training algorithm has four inputs, a hidden layer and an output, the hidden layer uses a Gaussian function as a radial basis function, and the calculation formula of the output of the first hidden layer is as follows:
wherein i ∈ [1, 10 ]]10 neurons representing the first hidden layer, XcRepresenting the central value of a Gaussian function, which is a column vector, σ, co-dimensional with XiIs the width of the gaussian function, X is the set of input vectors,
the output of the network is thus:
wherein sigmai、XcAnd ωi1Randomly giving to calculate the actual output y of the first group of input data1Then update σ according to a gradient descent algorithmi、Xc、ωi1Continuing the second group until all the input and output data are trained on the rbf neural network to obtain the final sigmai、Xc、ωi1And (4) finishing.
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| US10945654B2 (en) * | 2015-02-14 | 2021-03-16 | Massachusetts Institute Of Technology | Methods, systems, and apparatus for self-calibrating EEG neurofeedback |
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| CN1833616A (en) * | 2005-12-15 | 2006-09-20 | 西安交通大学 | Multi-conduction brain biological information feedback instrument |
| CN101641660A (en) * | 2007-03-23 | 2010-02-03 | 诺基亚公司 | Apparatus, method and computer program product providing a hierarchical approach to command-control tasks using a brain-computer interface |
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