A brain-computer interface-based application system, method, and electronic device

Abstract

This invention discloses an application system, method, and electronic device based on a brain-computer interface. The system includes: an EEG sampling module for collecting and uploading EEG signals via the brain-computer interface; a preprocessing module for preprocessing the collected EEG signals and classifying them according to a preset classification standard; a brainwave database for pre-constructing brainwave data with multiple categories of features and associating corresponding emotional information with each feature; a matching unit for: matching the preprocessed signals with the brainwave database to obtain a matching result; and obtaining corresponding push data based on the matching result; and an output control module for generating a corresponding output signal based on the push data and transmitting it through the brain-computer interface to achieve emotional intervention and alleviate pain. The effect is that it can obtain the patient's true emotional feelings and generate a corresponding output signal to achieve emotional intervention and alleviate pain.

Description

Technical Field

[0001] This invention relates to the field of brain-computer interface technology, and more specifically to an application system, method, and electronic device based on a brain-computer interface. Background Technology

[0002] Brain-computer interface (BCI) is a technology that enables the human brain to interact with external devices based on electroencephalogram (EEG) signals; also known as brain-computer fusion sensing technology, it is a new type of communication and control technology that uses external devices such as computers, electrodes, and chips to replace conventional intermediaries such as nerves and muscles to realize the interaction of information between the brain and the outside world.

[0003] Brain-computer interface (BCI) technology enables direct interaction between the brain and external devices, opening up unconventional pathways for brain information output. Currently, it is mostly used in device control or daily life, where users are conscious and able to express themselves. However, in critical and special medical scenarios, it is difficult to directly obtain the patient's true emotional feelings, making the issues of monitoring and end-of-life care increasingly prominent. Existing methods rely on monitoring various indicators for judgment, which can easily lead to patients experiencing great suffering but being unable to express it. Summary of the Invention

[0004] In view of the technical deficiencies in the prior art, the purpose of this invention is to provide an application system, method and electronic device based on brain-computer interface, so as to overcome the deficiency of the prior art in some critical and special medical scenarios where it is difficult to directly know the true emotional feelings of patients.

[0005] To achieve the above objectives, in a first aspect, embodiments of the present invention provide an application system based on a brain-computer interface, including an EEG sampling module for collecting and uploading EEG signals via a brain-computer interface;

[0006] The preprocessing module is used to preprocess the acquired EEG signals and classify them according to preset classification criteria.

[0007] A brainwave database is used to pre-construct brainwave data with multiple categories of features and associate corresponding emotional information with each category of features; the emotional information includes positive emotional information and negative emotional information.

[0008] Matching unit, used for:

[0009] The preprocessed signal is then matched against the brainwave database to obtain a matching result;

[0010] The corresponding push data is obtained based on the matching results and displayed on the smart screen.

[0011] The output control module is used to generate corresponding output signals based on the pushed data and transmit them through the brain-computer interface to achieve emotional intervention and alleviate pain.

[0012] As an optional implementation of this application, the preprocessing includes: median filtering, band-stop filtering, and Hilbert-Huang transform;

[0013] The preset classification criteria use frequency for classification.

[0014] As an optional implementation of this application, the preprocessing module further processes the preprocessed signal;

[0015] The preprocessed signals are divided into stationary signals and non-stationary signals.

[0016] The stationary signal is processed using short-time Fourier transform, while the non-stationary signal is processed using multifractal trend fluctuation analysis.

[0017] As an optional implementation of this application, when the matching result is negative emotional information, brainwave data opposite to the negative emotional information is pushed.

[0018] The system processes the pushed brainwave data to create personalized data to meet the needs of different individuals.

[0019] As an optional implementation of this application, the system is also used to acquire external monitoring data and combine it with the obtained matching results to obtain the current status data; the output control module also outputs corresponding prompt information according to the status data so that the caregiver can carry out targeted treatment to alleviate pain.

[0020] Secondly, embodiments of the present invention also provide a brain-computer interface-based control method, applied to a brain-computer interface-based application system as described in the first aspect, the method comprising:

[0021] Acquire electroencephalogram (EEG) signals collected and uploaded via a brain-computer interface;

[0022] The obtained EEG signals are preprocessed and categorized according to preset classification criteria;

[0023] The preprocessed signal is matched with a pre-constructed brainwave database to obtain a matching result; wherein, the brainwave database is constructed with brainwave data of various categories of features, and corresponding emotional information is associated with each category of features; the emotional information includes positive emotional information and negative emotional information;

[0024] The corresponding push data is obtained based on the matching results and displayed on the smart screen.

[0025] The system generates corresponding output signals based on the pushed data and transmits them through the brain-computer interface to achieve emotional intervention and alleviate pain.

[0026] As an optional embodiment of this application, the method further includes: reprocessing the preprocessed signal;

[0027] The preprocessed signals are divided into stationary signals and non-stationary signals.

[0028] The stationary signal is processed using short-time Fourier transform, while the non-stationary signal is processed using multifractal trend fluctuation analysis.

[0029] As an optional implementation of this application, when the matching result is negative emotional information, brainwave data opposite to the negative emotional information is pushed.

[0030] The system processes the pushed brainwave data to create personalized data to meet the needs of different individuals.

[0031] As an optional implementation of this application, the method further includes:

[0032] Obtain external monitoring data and combine it with the obtained matching results to obtain the current status data;

[0033] Then, based on the status data, corresponding prompts are output so that the caregiver can take targeted measures to alleviate the pain.

[0034] Thirdly, embodiments of the present invention also provide an electronic device, comprising:

[0035] One or more processors;

[0036] and a memory associated with the one or more processors, the memory being used to store program instructions that, when read and executed by the one or more processors, perform any of the methods described in the second aspect.

[0037] By implementing the embodiments of the present invention, brainwave signals are collected and uploaded, categorized according to preset classification standards, and then sent to a pre-constructed brainwave database for matching to obtain corresponding emotional information. This enables the direct acquisition of patients' true emotional feelings in some critical and special medical scenarios. Finally, push data is generated based on the matching results to generate corresponding output signals, which are transmitted through the brain-computer interface to achieve emotional intervention and alleviate pain. Attached Figure Description

[0038] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below.

[0039] Figure 1 This is a structural diagram of an application system based on a brain-computer interface provided in an embodiment of the present invention;

[0040] Figure 2 This is a flowchart of a brain-computer interface-based control method provided in an embodiment of the present invention. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] It should be noted that, unless otherwise stated, the technical or scientific terms used in this application should have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0043] Firstly, please refer to Figure 1 The present invention provides an application system based on a brain-computer interface, comprising:

[0044] The EEG sampling module is used to collect and upload EEG signals via a brain-computer interface;

[0045] When applied, the brain-computer interface can be used in an invasive or non-invasive manner, without any restrictions; for example, implanted prostheses or other devices; at the same time, it has corresponding communication functions to achieve uploading and corresponding positioning functions.

[0046] The preprocessing module is used to preprocess the acquired EEG signals and classify them according to preset classification criteria.

[0047] Specifically, the preprocessing includes: median filtering, band-stop filtering, and Hilbert-Huang transform;

[0048] The median filtering is a nonlinear signal processing technique based on sorting statistics theory that can effectively suppress noise. The basic principle of median filtering is to replace the value of a point in a digital image or digital sequence with the median value of all points in a neighborhood of that point, so that the surrounding pixel values ​​are close to the true value, thereby eliminating isolated noise points.

[0049] A band-stop filter is used for frequency removal that is not within a specific range, hence it is called a suppression filter.

[0050] A band-stop filter allows frequencies of a specific bandwidth to pass through with maximum attenuation.

[0051] For a given order and a flat frequency response in the passband, different types of bandstop filters will produce the largest roll-off rate.

[0052] The purpose of the Hilbert-Huang transform is to obtain the instantaneous frequency components in the signal that have actual physical meaning, thereby achieving high-resolution time-frequency analysis.

[0053] The preset classification criteria use frequency for classification;

[0054] Currently, there are four basic types of electroencephalogram (EEG) waveforms: alpha waves, beta waves, theta waves, and delta waves. 1. Alpha waves: Frequency 8–13 Hz, amplitude 20–100 microvolts. When the eyes are open or stimulated, alpha waves disappear, replaced by fast waves, a phenomenon known as wave block. 2. Beta waves: Frequency 14–30 Hz, amplitude 5–20 microvolts. These waveforms appear when the eyes are open, when suddenly stimulated by sound, or when thinking. 3. Theta waves: Frequency 4–7 Hz, amplitude 100–150 microvolts. These typically appear during drowsiness, deep anesthesia, or hypoxia. 4. Delta waves: Frequency 0.5–3 Hz, amplitude 20–200 microvolts. These appear during sleep in adults but not when awake, and can also appear during deep anesthesia or hypoxia.

[0055] A brainwave database is used to pre-construct brainwave data with multiple categories of features and associate corresponding emotional information with each category of features; the emotional information includes positive emotional information and negative emotional information.

[0056] Specifically, various brainwave data are obtained in advance from external physical examinations, medical records, or research data, and corresponding category characteristics and emotional information are obtained based on the diagnosis and research results; the brainwave database can be deployed in the cloud or locally, without any restrictions.

[0057] Alternatively, the samples from the collected data can be used to train a neural network and labeled according to different psychological states (training set); then, the trained model is used to label new neurophysiological data (test set).

[0058] The emotional information includes anger, fear, sadness, disgust, surprise, curiosity, joy, pleasure, calmness, and happiness, and is divided into positive and negative.

[0059] Matching unit, used for:

[0060] The preprocessed signal is then matched against the brainwave database to obtain a matching result;

[0061] The corresponding push data is obtained based on the matching results and displayed on the smart screen.

[0062] The output control module is used to generate corresponding output signals based on the pushed data and transmit them through the brain-computer interface to achieve emotional intervention and alleviate pain.

[0063] When applied, the smart screen can display specific information including matching results, pushed data, and processed collected signals, allowing users, doctors, and caregivers to understand their current physical condition and what they see and get. The smart screen includes computer and mobile terminals for medical workers; mobile terminals for users; and smart screens for smart home devices.

[0064] To better target the processing of the obtained signals, the preprocessing module further processes the preprocessed signals.

[0065] The preprocessed signals are divided into stationary signals and non-stationary signals.

[0066] The stationary signal is processed using short-time Fourier transform, while the non-stationary signal is processed using multifractal trend fluctuation analysis.

[0067] Meanwhile, when the matching result is negative emotional information, brainwave data opposite to the negative emotional information is pushed; for example, when angry, calm brainwave data is pushed.

[0068] The system processes the pushed brainwave data to create personalized data to meet the needs of different individuals.

[0069] This can help patients with aphasia, those on mechanical ventilation in the ICU, and other special populations with severe expression deficiencies to make further judgments, making it easier for doctors to determine the next treatment plan; secondly, without harming the body, it is possible to try to intervene in the brain signals through simulated stimulation and use physical therapy to relieve pain.

[0070] Furthermore, in another embodiment, the output control module also acquires the brainwaves of multiple caregivers to act on the individual requiring intervention in order to achieve pain relief; for example, it has been shown that the physical pain felt when shaking hands with a partner does indeed decrease.

[0071] The above technical solution collects and uploads electroencephalogram (EEG) signals, categorizes them according to preset classification standards, and then sends them to a pre-constructed brainwave database for matching to obtain corresponding emotional information. This enables the direct acquisition of patients' true emotional feelings in critical and special medical scenarios. Finally, based on the matching results, push data is generated to produce corresponding output signals, which are then transmitted through the brain-computer interface to achieve emotional intervention and alleviate pain.

[0072] Furthermore, to provide more accurate suggestions and reminders and reduce unnecessary suffering, based on the above solution, the system is also used to acquire external monitoring data and combine it with the obtained matching results to obtain the current status data; the output control module also outputs corresponding prompt information according to the status data, so that the caregiver can take targeted actions to alleviate suffering.

[0073] The status data includes states such as difficulty breathing, difficulty swallowing, and pain;

[0074] For example, terminally ill patients are often dehydrated and have difficulty swallowing, which causes a sharp decrease in the amount of blood circulating in the body. As a result, their skin is wet and cold to the touch. At this time, do not assume that the patient needs to be covered with blankets to keep warm because they are cold. On the contrary, even if you only add a little weight to their hands and feet, most terminally ill patients will find it too heavy and unbearable.

[0075] Dehydration and malnutrition cause ketones to accumulate in the blood, producing a pain-relieving effect that gives the patient an abnormal sense of euphoria. Even administering a small amount of glucose at this time will counteract this euphoria. Feeding the patient at this stage can also lead to vomiting, food entering the trachea and causing choking, and the patient struggling and resisting.

[0076] If the patient frequently makes whimpering or stridor sounds while breathing, suctioning will often fail and cause more suffering. Instead, turn the patient to one side, elevate their head, or administer pain medication to allow them to continue talking with family members, thus alleviating their pain and providing end-of-life care.

[0077] Based on the same inventive concept, this invention also provides a brain-computer interface-based control method, applied to the brain-computer interface-based application system described above, the method comprising:

[0078] S101, acquires and uploads EEG signals via brain-computer interface;

[0079] S102, the obtained EEG signals are preprocessed and categorized according to preset classification criteria;

[0080] S103, the preprocessed signal is matched with a pre-constructed brainwave database to obtain a matching result; wherein, the brainwave database is constructed with brainwave data of various categories of features, and the corresponding emotional information is associated with each category of features; the emotional information includes positive emotional information and negative emotional information;

[0081] S104, Based on the matching result, obtain the corresponding push data and display it on the smart screen;

[0082] S105, generate a corresponding output signal based on the pushed data, and transmit it through the brain-computer interface to achieve emotional intervention and alleviate pain.

[0083] In practice, the method further includes: reprocessing the preprocessed signal;

[0084] The preprocessed signals are divided into stationary signals and non-stationary signals.

[0085] The stationary signal is processed using short-time Fourier transform, while the non-stationary signal is processed using multifractal trend fluctuation analysis.

[0086] When the matching result is negative emotional information, brainwave data opposite to the negative emotional information is pushed.

[0087] The system processes the pushed brainwave data to create personalized data to meet the needs of different individuals.

[0088] Furthermore, to enhance the comprehensiveness of the processing, the method also includes:

[0089] Obtain external monitoring data and combine it with the obtained matching results to obtain the current status data;

[0090] Then, based on the status data, corresponding prompts are output so that the caregiver can take targeted measures to alleviate the pain.

[0091] It should be noted that for a more detailed description of the control method's workflow, please refer to the aforementioned system implementation section, which will not be repeated here.

[0092] The aforementioned control method enables the direct acquisition of patients' true emotional feelings in critical and special medical scenarios. Finally, based on the matching results, push data is generated to produce corresponding output signals, which are then transmitted through the brain-computer interface to achieve emotional intervention and alleviate suffering.

[0093] In this embodiment, an electronic device is also provided, including:

[0094] One or more processors;

[0095] And a memory associated with the one or more processors, the memory being used to store program instructions that, when read and executed by the one or more processors, perform the steps of an embodiment of a brain-computer interface-based control method as described above.

[0096] In this embodiment of the invention, the processor may be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0097] The memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of the memory may also include non-volatile random access memory. For example, the memory may also store information about the device type.

[0098] Those skilled in the art will recognize that the steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0099] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and such modifications or substitutions should all be covered within the scope of protection of the present invention.

Claims

1. A brain-computer interface-based application system, characterized in that, include: The EEG sampling module is used to collect and upload EEG signals via a brain-computer interface; The preprocessing module is used to preprocess the acquired EEG signals and classify them according to preset classification criteria. A brainwave database is used to pre-construct brainwave data with multiple categories of features and associate corresponding emotional information with each category of features; the emotional information includes positive emotional information and negative emotional information. Matching unit, used for: The preprocessed signal is then matched against the brainwave database to obtain a matching result; The corresponding push data is obtained based on the matching results and displayed on the smart screen. The output control module is used to generate corresponding output signals based on the pushed data and transmit them through the brain-computer interface to achieve emotional intervention and alleviate pain. The preprocessing includes: median filtering, band-stop filtering, and Hilbert-Huang transform; Band-stop filters are used as frequency removers that are not within a specific range. Band-stop filters allow frequencies of a specific bandwidth to pass through with maximum attenuation. For a given order and a flat frequency response in the passband, different types of bandstop filters will produce the largest roll-off rate. The purpose of the Hilbert-Huang transform is to obtain the instantaneous frequency components with actual physical meaning in the signal, thereby achieving high-resolution time-frequency analysis; The preset classification criteria use frequency for classification; The output control module also acquires the brainwaves of multiple guardians to act on the individual requiring intervention; The preprocessing module further processes the preprocessed signal; The preprocessed signals are divided into stationary signals and non-stationary signals. The stationary signal is processed using short-time Fourier transform, and the non-stationary signal is processed using multifractal trend fluctuation analysis. When the matching result is negative emotional information, brainwave data opposite to the negative emotional information is pushed. The system processes the pushed brainwave data to create personalized data to meet the needs of different individuals.

2. The brain-computer interface-based application system according to claim 1, characterized in that, The system is also used to acquire external monitoring data and combine it with the obtained matching results to obtain the current status data; the output control module also outputs corresponding prompt information according to the status data so that the caregiver can take targeted measures to alleviate pain.

3. An electronic device, characterized in that, include: One or more processors; And a memory associated with the one or more processors, the memory being used to store program instructions that, when read and executed by the one or more processors, perform the functions of the system of any one of claims 1-2.

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