CN111477299A - Method and device for regulating and controlling sound-electricity stimulation nerves by combining electroencephalogram detection and analysis control - Google Patents

Abstract

The invention discloses a noninvasive sound and electricity stimulation nerve regulation and control method and device combined with electroencephalogram objective detection analysis feedback control, and the method comprises the following steps: the system comprises an electroencephalogram detection and signal processing and analyzing system, an intelligent diagnosis and treatment control system, an acoustic stimulation treatment system, a transcranial electric pulse stimulation treatment system and a cognitive behavior psychotherapy system, wherein the electroencephalogram detection of a plurality of brain functional areas is carried out on a human body before, during and after noninvasive acoustic stimulation and/or electric pulse stimulation and/or cognitive psychotherapy, and a closed loop for detecting, analyzing and feeding back treatment regulation and control is realized through electroencephalogram signal acquisition, real-time electroencephalogram space-time modeling, objective analysis of characteristic parameters of a neural network and establishment of a correlation model with curative effect evaluation. The method and the device disclosed by the invention can be used for establishing a tracking objective evaluation and prediction method of the multi-course or multi-time nerve regulation and control treatment effect, qualitatively and quantitatively regulating and optimizing a method for implementing a subsequent treatment scheme and selecting scheme parameters according to an expected curative effect, and objectively evaluating and predicting the nerve function state.

Description

Method and device for regulating and controlling sound-electricity stimulation nerves by combining electroencephalogram detection and analysis control

Technical Field

The invention relates to the technical field of medical treatment, in particular to a noninvasive acoustic-electric stimulation neural regulation method (non-invasive acoustic-electric stimulation therapy) and a device thereof, which are combined with electroencephalogram (EEG) objective detection analysis feedback control.

Background

Cranial nerve dysfunction is a change and/or abnormal pattern of changes in the cranial nervous system that is diseased and unable to function properly, resulting in a decrease and/or an increase in neural activity and/or cross-reactivity between brain regions. Neurological disorders or disorders are the third-leading chronic diseases of the elderly, ranked behind cancer, cardio-and cerebrovascular, involving a number of associations and cross-interactions of objective neuroelectrophysiology (neuroelectrophysiology) states, subjective psycho-perception and subjective psycho-activity of the physiological and/or auditory and/or psychological and/or emotional and/or memory and/or attention and awareness and/or sensory motor brain areas, and the conversion of the compensatory and decompensated processes of nerve function, which is accompanied by cognitive psychology (cognitive psychor psychology) disorder, is a traditional miscellaneous disease, and clinically presents symptoms and signs of corresponding nerve damage, including but not limited to tinnitus, deafness, sleep disorder, anxiety, depression, vertigo, ear blockage, nervous headache, mental fatigue, alzheimer disease (dementia), parkinson disease and the like. About 80% of elderly people aged 65 and older suffer from at least one neurological disorder. Neuromodulation therapy is directed delivery of stimuli to specific neural sites of the body, actively stimulating the target nerve to produce a natural biological response or controlling physiological levels of neurotransmitters to alter neural activity.

Tinnitus is the result of different pathological abnormalities and changes of many diseases which involve or do not involve the auditory system, the cause of the disease is complex, the mechanism is unclear, the disease is mainly characterized in that no corresponding external sound source or electric stimulation exists, sound sensation is subjectively felt in ears or intracranial, the tinnitus sound appears in one ear or two ears, the tinnitus sound is continuous or discontinuous, and the sound volume usually does not exceed 20 decibels of hearing threshold, and compound sound or noise with changed melody or tone and tone color is generated. Tinnitus can cause a patient to have an emotional or negative response of restlessness, anxiety, tension, fear, or depression, while an adverse emotional state can exacerbate tinnitus, resulting in a vicious circle between tinnitus and the adverse mood, with psychological factors playing an important role in the onset and development of tinnitus. At present, specific drugs are lacking in the treatment of tinnitus, and acoustic stimulation therapy (acoustic stimulation therapy), transcranial electric pulse stimulation therapy (transcranial electric pulse stimulation therapy) and cognitive behavioral psychotherapy (cognitive behavioral psychotherapy) are gradually adopted clinically:

1. the application of sound therapy is relatively wide, such as various tinnitus therapeutic equipments, and patent application No. 201810011553.5 discloses a method for fitting hearing aids, which uses a biological acoustic stimulation device to send out an acoustic stimulation signal to a subject and obtains FPT data through the response of the subject; importing the FPT data into a computer, and correcting the FPT data for the FPT data through an acoustic testing module running in the computer based on a preset verification algorithm; after the bioacoustic stimulation device is connected with a computer, the corrected FPT data are sent to the bioacoustic stimulation device, so that an acoustic stimulation signal sent to a subject is adjusted to be in a comfortable state by a DSP chip in the bioacoustic stimulation device, and the treatment effect is evaluated by the subjective feeling of the subject;

2. the utility model discloses an electro-acoustic stimulator for treating tinnitus, which is used for stimulating treatment by electric pulses, and the utility model application number is 201420311215.0, wherein the computer is used for storing electric pulse parameters for treating tinnitus, the computer can generate electric pulses with different waveforms according to the mode selected by a patient, and the electro-acoustic stimulator outputs electric stimulation through an invasive electro-acupuncture device to treat tinnitus, and the treatment effect is evaluated through the subjective feeling of the patient;

3. cognitive behavioral psychotherapy, tinnitus generally causes a subjective sensation different from that of the natural voice, and the patient produces an unpleasant psychological response to the appearance of tinnitus such as an incomprehension, doubt or fear. Tinnitus is accompanied with cognitive psychological problems and is a good indication of cognitive behavioral psychotherapy to tinnitus treatment, and the cognitive behavioral psychotherapy adopts the modes of relaxation, thinking of cognitive structure adjustment, correction of maladaptive behaviors and the like, so that tinnitus patients can achieve the aim of psychotherapy. In the patent application No. 201910861100.6, a tinnitus deafness detection testing and treatment system based on a shared cloud computing platform is disclosed, comprising: the system comprises an online cloud computing platform and a plurality of offline intelligent terminals, wherein the cloud computing platform comprises a cloud server and a hearing aid fitting programmer module connected with the cloud server, and an expert or intelligent robot remote technology support, operation and supervision module, a big data module, a hearing test module, a fitting programming software module, a tinnitus physiological acoustic detection and personalized acoustic treatment scheme manufacturing module and a tinnitus acoustic treatment and cognitive behavior psychotherapy scheme library module are arranged in the cloud server. The tinnitus sound treatment and cognitive behavior psychotherapy scheme library module stores an individualized exclusive tinnitus sound treatment scheme, a shared adjustable universal tinnitus sound treatment scheme and a cognitive behavior psychotherapy scheme, the individualized tinnitus sound treatment scheme and the cognitive behavior psychotherapy scheme are formulated according to results of the hearing test module and the tinnitus physiological acoustic detection module, tinnitus treatment is carried out, and the treatment effect is still evaluated through subjective feelings of patients.

In fact, due to the lack of targeted drugs and the problem of drug safety, cranial nerve physiotherapy or non-invasive neuromodulation therapy is currently an important safe and effective physical therapy for treating neurological dysfunction, and the physical factors used include, but are not limited to, sound, light, electricity, magnetism, nuclear rays, heat, cold, and the like. However, the detection and treatment evaluation of the existing neurological dysfunction are subjective, and a diagnosis, treatment and evaluation technology based on objective detection and neural regulation is needed in the market, so that an integrated system diagnosis and treatment means of objective physiology, subjective psychology and subjective soul is strengthened, the diagnosis and treatment process and treatment effect evaluation are objective, quantitative, visual, standardized and intelligent, qualitative and quantitative adjustment of diagnosis and treatment schemes and scheme parameters is guided to achieve the expected diagnosis and treatment effect, the treatment effect is objectively and quantitatively evaluated, the development trend of multiple treatment courses is predicted, the confidence of doctors and patients is enhanced, and the compliance of clinical services is improved.

Disclosure of Invention

The invention mainly solves the technical problem of providing the sound-electricity stimulation nerve regulation and control method and device combined with electroencephalogram detection analysis feedback control, improving the objectivity, quantitative, visual, standardized and intelligent diagnosis and treatment process and treatment effect evaluation, feeding back, guiding, qualitatively and quantitatively adjusting and optimizing diagnosis and treatment schemes and scheme parameters to achieve expected diagnosis and treatment effects, and achieving objective and quantitative evaluation of treatment effects and prediction of multi-course development trend.

In order to solve the technical problems, the invention adopts a technical scheme that: the noninvasive nerve regulation and control method combining electroencephalogram (EEG) objective detection, analysis and feedback control is provided, and comprises the following steps:

performing brain cortex (cortix) electroencephalogram detection and signal acquisition on a plurality of brain functional areas corresponding to physiology, hearing, psychology, emotion, memory, attention, consciousness and/or sensory movement on a human body before, during and after noninvasive acoustic stimulation (acoustic stimulation) and/or transcranial electrical pulse stimulation (transcranial electrical pulse stimulation) and/or cognitive behavioral psychotherapy (cognitive behavor psychotherapy);

through electroencephalogram signal decoding analysis feedback, acquiring electroencephalogram characteristic parameters (EEG eigenvalue) which are associated with treatment effects and treatment times, acoustoelectric stimulation treatment parameters and/or cognitive behavioral psychotherapy methods, wherein the electroencephalogram characteristic parameters comprise an electroencephalogram Power Spectrum (PSD), electroencephalogram neural network space topology (neural network) and related network statistical attribute parameters (network statistical attribute parameters) and evaluation indexes;

the method comprises the steps of feeding back and guiding to make a tracking evaluation and prediction method of multi-course or multi-time neural regulation treatment effect through objective analysis of electroencephalogram characteristic parameters, qualitatively and quantitatively regulating and optimizing a subsequent treatment scheme implementation and scheme parameter selection method according to an expected treatment effect, establishing an associated model of electroencephalogram characteristic parameters and neural function states according to objective analysis feedback of physical examination or screening or monitoring of electroencephalogram characteristic parameters, qualitatively and quantitatively evaluating and predicting the neural function states, and carrying out early warning;

and performing auxiliary verification by means of numerical simulation modeling and analysis of a neural network computer, functional nuclear magnetic imaging (fMRI), animal experiments and the like.

In a preferred embodiment of the invention, the onset of the patient with neurological dysfunction involves physiological and/or auditory and/or psychological and/or emotional and/or memory and/or attention and consciousness and/or sensorimotor interaction among a plurality of brain functional areas, and after intervention such as acoustic stimulation and/or transcranial electric pulse stimulation and/or cognitive psychotherapy, relevant brain electrical signals are changed and remodelling of brain neural networks is carried out (remodelling of brain neural networks). Therefore, by collecting the electroencephalogram signals corresponding to a plurality of brain functional cortex, extracting corresponding electroencephalogram space-time and network characteristics, and performing decoding analysis, the disease of the patient with the neurological dysfunction can be objectively, qualitatively and quantitatively evaluated and predicted. In the project, the brain electrical signals can be obtained by arranging dry or wet brain electrode arrays with the total number of not less than 2 leads in the main brain functional area, and the electrode signal transmission and amplifier signal acquisition parameters are as follows: the sampling bit number is not less than 10bits, the input impedance should reach G omega level, the equivalent minimum input noise should not be more than 10 muV, the data transmission rate is not less than 1Mbps, and the signal amplitude range is not more than 200 muV.

In a preferred embodiment of the present invention, component information related to the neural function regulation and control is obtained from the originally recorded electroencephalogram signals, task-related components are described by means of feature vectors (eigenvectors), a machine learning algorithm (machine learning algorithm) is used to classify the feature vectors related to different tasks, decoding of different brain region activity states from the electroencephalogram signals is achieved, and electroencephalogram components closely related to clinical indicators of the neural dysfunction of a patient (such as tinnitus disability table, sleep table, anxiety table, depression table, and the like) are obtained. The decoding precision depends on how much the features extracted by the feature algorithm represent related tasks and how accurately the classification algorithm can distinguish the categories of different tasks, and the electroencephalogram decoding and analyzing method comprises but is not limited to the following steps:

A. preprocessing an electroencephalogram signal: obtaining high-quality electroencephalogram signals, particularly signals including but not limited to delta, theta, alpha, beta and gamma bands, by means of signal filtering screening, denoising discarding, removing ocular and muscle artifacts, principal component analysis and signal reconstruction;

B. extraction of electroencephalogram signal features: various feature information of the electroencephalogram signals is extracted to the maximum extent through analysis including but not limited to electroencephalogram network reconstruction, multi-dimensional discrete wavelet transform (multi-dimensional discrete wavelet transform) and the like, and feature analysis is carried out on the electroencephalogram signals, so that more, more accurate, more comprehensive and more comprehensive information is provided for diagnosis and treatment evaluation;

C. the electroencephalogram characteristic identification and characteristic parameter classification comprises the steps of adopting characteristics including but not limited to brain neural network topology, electroencephalogram spatio-temporal information (including amplitude, energy and the like), adopting a deep neuron network, a long-time network, a short-time network, a support vector machine (support vector machine), a neural network and the like to perform characteristic identification and classification to obtain electroencephalogram characteristic parameters, including but not limited to converting electroencephalogram waves of which the signal amplitude of a brain area where a target electroencephalogram electrode is located changes along with time into a power spectrum PSD of which the electroencephalogram power changes along with frequency, obtaining a network topology structure of the electroencephalogram and related network statistics values including Coh-network coherence, Clu-clustering coefficient, L-characteristic path length, Ge-global efficiency, L e-local efficiency and the like through network analysis, obtaining electroencephalogram characteristic parameters with good regulation and control on neural dysfunction of electroencephalogram by longitudinally comparing electroencephalogram detection and signal decoding analysis of the same state and the brain area after a plurality of treatments, and obtaining electroencephalogram characteristic parameters with good regulation and control and association of neural dysfunction of the brain areas, thereby more intuitively and quantifiably observing and understanding brain area neural activity changes of different brain area associations and evaluating the abnormal effects between a plurality of brain area associations.

D. Obtaining the decoding and analyzing results of electroencephalogram signals of patients with nerve dysfunction, healthy people and the same patient, including but not limited to delta wave band and/or theta wave band and/or alpha wave band and/or beta wave band and/or gamma wave band, obtained before and/or during and/or after each treatment, obtaining the power spectrum average PSD and/or network statistical attributes (Clu-clustering coefficient, L-characteristic path length, Ge-global efficiency, L e-local efficiency) and/or neural network topological attributes or network correlation Coh and the variation value thereof in the same brain area before and after a plurality of treatments, and/or after the treatment, wherein the decoding and analyzing results can be used as the evaluation index of treatment effect;

E. comparing the PSD values of the power spectrums of the patients and healthy people and the characteristic values thereof in the relevant frequency or all frequency ranges, evaluating the relevance between the change amount of the PSD of the power spectrum of the same patient and the increase of treatment times or the improvement of treatment effect, and reflecting the abnormal activity of the nerves in the brain area by one or more frequencies corresponding to the peaks or the troughs of the power spectrum; the larger the power spectrum change amount is, the more obvious the corresponding abnormal activity change of the nerves in the brain area is, namely, the more obvious the treatment effect of the neurological dysfunction is;

F. the change amount of the network attributes Clu, Ge, L e and L of the same patient is related to the increase of treatment times or treatment effect, the more significant the network attribute difference is, the larger the improvement of the symptom of the nerve dysfunction is, the short connection between an auditory area and a sensory area and the long connection between the auditory area and an emotional area exist in the network attribute difference, which shows that the difference mode of the abnormal response may be related to tinnitus and nerve fatigue, attention weakness, sensory disturbance, anxiety and depression caused by the nerve dysfunction, the treatment effect can be systematically evaluated by organically linking the acoustic stimulation treatment with electric pulse treatment and cognitive behavior psychotherapy through the analysis of the nerve network attribute, and the network correlation Coh difference reflects the network space topological difference between brain areas, namely the strength difference of the correlation activity degree, which can help to evaluate the effect of different treatment schemes and the effect after a plurality of treatments;

G. relative to the analysis of power spectrum, the network coherence and network attributes reflect the overall difference between the same brain area and/or different brain areas, and can reflect the abnormal activity change of the brain area, which is the connection strength between the physiological and/or auditory and/or psychological and/or emotional and/or memory and/or attention and consciousness and/or sensory-motor brain functional areas, further reveals that the generation of tinnitus neurological dysfunction is a comprehensive pathogenesis of physiological and cognitive behavioral-psychological interaction, provides relatively reliable evaluation standard parameters for treatment, and further guides the writing of qualitative and quantitative algorithms for tracking, evaluation and prediction of treatment effect, and makes a comprehensive system estimate the curative effect difference, multiple therapeutic effects and progress of the respective or combined implementation of three treatment schemes of acoustic stimulation, cranial electrical pulse stimulation and cognitive behavioral psychological stimulation, or sets therapeutic targets and evaluation standards, predicts the number of required treatment or the number of treatment times, optimizes the treatment effect according to the expected treatment and/or the subsequent treatment state, and/or optimizes the early warning and/or early warning method for taking a simple and/or early warning method for health state detection and/or early warning of neurological dysfunction, and/or early warning of brain dysfunction;

H. through clinical treatment of a large sample amount of patients, a large database is established, deep data mining is carried out, artificial intelligent AI analysis is carried out, and a more general curative effect evaluation method and indexes related to the neural dysfunction characteristic classification are obtained;

I. and performing auxiliary verification on the neural activity of the brain functional region before and after the sound and electricity stimulation neural regulation and control treatment, the change of the neural activity, the curative effect evaluation and the like by means of numerical simulation modeling and analysis of a neural network computer and/or functional nuclear magnetic imaging (fMRI) and/or animal experiments and the like.

In a preferred embodiment of the present invention, the method is applied to qualitatively and quantitatively guiding and optimizing diagnosis and/or physical examination and/or screening and/or monitoring and/or predicting the diagnosis and/or diagnosis of neurological dysfunction, wherein the neurological dysfunction is the abnormal change and/or change rule of change of brain nervous system, which leads to the reduction and/or enhancement of brain regional nerve activity and/or brain regional cross action, and the corresponding symptoms and signs of nerve damage in clinic, including but not limited to tinnitus, deafness, sleep disorder, anxiety, depression, vertigo, ear blockage, nervous headache, mental fatigue, epilepsy, Alzheimer's disease and Parkinson's disease.

In order to solve the technical problem, the invention adopts another technical scheme that: an acoustoelectric stimulation neural regulation diagnosis and treatment device combined with electroencephalogram detection and analysis feedback control is provided, which includes but is not limited to: the brain electricity detection and signal processing and analyzing system, the intelligent diagnosis and treatment control system, the acoustic stimulation treatment system, the transcranial electric pulse stimulation treatment system and the cognitive behavior psychotherapy system, the brain electricity detection and signal processing and analyzing system comprises but is not limited to a brain electricity electrode array module, a brain electricity amplifier module, a brain electricity signal analyzing module, a brain electricity equipment control module, a display module and a power supply module, the intelligent diagnosis and treatment control system comprises but is not limited to an acoustic signal following brain electricity signal change control module, an electric pulse signal following brain electricity signal change control module and a cognitive psychotherapy scheme following brain electricity signal change control module, the intelligent diagnosis and treatment control system shares or independently carries out qualitative and quantitative analysis on intelligent brain electricity algorithm embedded software through treatment feedback of each control module, and manually or automatically realizes adjustment and optimization on parameters of each corresponding therapy scheme and/or a therapy scheme using method, the acoustic stimulation treatment system comprises but is not limited to a physiological acoustic detection (physiological acoustic stimulation) module, an acoustic stimulation treatment scheme manufacturing module, an acoustic stimulation treatment module and an acoustic stimulation treatment display screen module, the transcranial electric pulse stimulation treatment system comprises but is not limited to an electric pulse signal generator module and an electric pulse treatment electrode array module, the cognitive behavior psychotherapy system comprises but is not limited to an inquiry scale module, a cognitive behavior psychotherapy module and a cognitive behavior psychotherapy display screen module, the acoustic-electric stimulation neural regulation and control treatment device combined with electroencephalogram detection analysis feedback control adopts an integrated device or a separated device or a wearable device, and each system module is independent or shares a display screen and/or a power supply.

In a preferred embodiment of the invention, the wearable device is simplified, including but not limited to wearable electroencephalogram detection and analysis system, cognitive behavior psychotherapy system, remote medical system based on electroencephalogram analysis feedback and smart phone or tablet computer connected with the system in a wired or wireless manner, the electroencephalogram analysis feedback and evaluation are written into APP to be implanted into the smart phone or tablet computer, and the APP can be used for neurological disorder physical examination and/or screening and/or monitoring and/or prediction, early warning is made, medical inquiry is asked and/or positive preventive measures are taken; the cognitive behavior psychotherapy system is implanted into a smart phone or a handheld panel through an APP and used for guiding a wearable device user to select and implement a cognitive behavior psychotherapy training and/or education and/or consultation and/or therapy scheme preset in the APP, and meanwhile, the APP is also used for connecting the wearable device user with a remote expert through a wireless network, providing remote control and/or professional consultation service and carrying out bidirectional communication.

In a preferred embodiment of the present invention, a composite acoustic conditioning regimen (composite acoustic conditioning therapy) of amplitude or phase angle or mixture of amplitude and phase angles is formulated by an acoustic stimulation treatment system for bioacoustic testing of the unilateral or bilateral ears, including but not limited to hearing, sleep, anxiety, depression, concentration tests or questionnaires, and tinnitus sound matching, plus background natural sounds; for non-otogenic neurological dysfunction, the method mainly guides to select input parameters and formulate a composite acoustic regulation and control scheme according to the results of electroencephalogram detection and analysis; the acoustic regulation and control scheme is that corresponding electric signals in an acoustic stimulation treatment scheme setting module are converted into sound wave signals through an earphone in the acoustic stimulation treatment module and input into an ear canal to vibrate a tympanic membrane and a cochlea to drive hair cells in the cochlea to fluctuate to generate electric stimulation signals, the electric stimulation signals enter a brain central system and connected brain functional regions such as a thalamus, a hippocampus and the like through an auditory pathway to drive or activate electrical excitability of neurons, further amplified electric signals desynchronize disordered signals of nerve functions, a brain nerve network is remolded, and nerves of all brain functional regions are recovered to a normal working state; the adjustable parameters of the acoustic stimulation treatment system comprise but are not limited to hearing threshold, frequency, wave amplitude, phase angle, wave crest sharpening, wave trough filling, wave delay, gap knocking, background noise and/or natural sound, the treatment effect is objectively detected and evaluated according to electroencephalogram, software is embedded through a qualitative and quantitative algorithm of an intelligent diagnosis and treatment control system, acoustic stimulation treatment scheme selection and treatment scheme parameters are manually or automatically adjusted and optimized, the purposes of enhancing the treatment effect and accelerating rehabilitation are achieved, or the required treatment times or treatment process number is predicted according to the expected treatment effect.

In a preferred embodiment of the invention, the electric pulse signal generator module of the transcranial electric pulse stimulation treatment system sends out electric pulse signals, and the electric pulse signals are released to stimulate the cortex or the nervous system by the transcranial electric pulse through the electric pulse treatment electrode array module arranged on the body surface of the related brain functional region, so as to achieve the purpose of relieving the symptom of the nervous dysfunction; electrical stimulation therapy regimen parameters include, but are not limited to, pulse frequency, pulse waveform, pulse amplitude, pulse width, pulse delay, firing sequence, duration, rest interval, number of repetitions; according to the brain electricity objective detection and the treatment effect evaluation, software is embedded through an intelligent diagnosis and treatment control system qualitative and quantitative algorithm, the parameters of an electrical stimulation treatment scheme are manually or automatically adjusted and optimized, and an amplitude modulation and/or frequency modulation transcranial electrical stimulation scheme is formulated, so that the purposes of enhancing the treatment effect and accelerating the rehabilitation are achieved, or the required treatment times or treatment course number is predicted according to the expected treatment effect.

In a preferred embodiment of the invention, the cognitive behavioral psychotherapy system makes a preliminary cognitive behavioral psychology consultation and/or education and/or training plan through a cognitive behavioral psychology inquiry scale, and the preliminary cognitive behavioral psychology consultation and/or education and/or training plan can be independently carried out or carried out in cooperation with sound-electricity stimulation therapy; according to the brain electricity objective detection and evaluation treatment effect, the cognitive behavior psychotherapy scheme is adjusted and optimized manually or automatically through the embedded software of the qualitative and quantitative algorithm of the intelligent diagnosis and treatment control system, and the cognitive behavior psychotherapy scheme comprises but is not limited to expert consultation, and consultation and/or training and/or education are carried out through reading data and/or audio data and/or video data compiled by experts and stored in the cognitive behavior psychotherapy module, so that the treatment effect is enhanced, the rehabilitation is accelerated, and the required treatment times or treatment course number is predicted according to the expected treatment effect.

The invention has the beneficial effects that: the invention provides a sound-electricity stimulation nerve regulation and control method and a device combining with electroencephalogram detection analysis feedback control, which can make a tracking objective evaluation and prediction method of multi-course or multi-time treatment effect by acquiring electroencephalogram signals, decoding and analyzing the electroencephalogram signals and analyzing electroencephalogram characteristic parameters of a human body in different periods of acoustic stimulation and/or electric pulse stimulation and/or cognitive behavior psychotherapy, and qualitatively and quantitatively regulate and optimize the implementation of a subsequent treatment scheme and the selection of scheme parameters according to the expected treatment effect; the nerve functional state can be qualitatively and quantitatively evaluated and predicted objectively in the physical examination and/or screening and/or monitoring of the nerve functional disorder, early warning is carried out, and the confidence of doctors and patients and the treatment effect are enhanced.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without inventive efforts, wherein:

FIG. 1 is a schematic structural diagram of a preferred embodiment of an acoustoelectric stimulation neural regulation diagnosis and treatment device combining electroencephalogram detection, analysis and feedback control;

FIG. 2 is a schematic diagram of the configuration of the electroencephalogram detection and signal processing and analyzing system of FIG. 1;

fig. 3 is a schematic structural diagram of the intelligent diagnosis and treatment control system in fig. 1;

FIG. 4 is a schematic diagram of the configuration of the acoustic stimulation treatment system of FIG. 1;

fig. 5 is a schematic diagram of the structure of the transcranial electrical pulse stimulation treatment system of fig. 1;

FIG. 6 is a schematic diagram of the cognitive behavioral psychotherapy system of FIG. 1;

FIG. 7 is a schematic diagram of the division of EEG electrode positions corresponding to brain regions during electroencephalogram signal acquisition;

FIG. 8 is a schematic diagram of a brain electrical signal analysis technology route or brain electrical signal decoding path, in which CNN-convolutional neural network, Db 6-six-dimensional discrete wavelet;

FIG. 9 is a schematic diagram of a pre-processing technique for electroencephalogram signals;

FIG. 10 is a schematic diagram of a big data intelligent detection technical route of feature recognition and classification or a brain electrical signal decoding path, in the diagram, CNN-convolutional neural network, L STM-long and short term memory, RBM-substraction method, SVM-support vector machine, KNN-K nearest neighbor algorithm, GPU-graphic processing unit;

FIG. 11 is a graph of the mean power spectrum PSD difference between a healthy population (lower curve) and a tinnitus patient population (upper curve);

fig. 12 is a graph of the difference in network topological coherence (Coh) after multiple acoustic stimulation treatments before pre and post acoustic stimulation, respectively, with functional network connectivity in the prefrontal, left temporal, and parietal regions significantly weaker than treatment 1;

FIG. 13 is a correlation diagram of the Pre network attribute change before acoustic stimulation and the treatment effect, wherein the network attributes Clu, Ge and L e decrease with the increase of the treatment times, the change amount is in positive marginal correlation with the treatment effect (the tinnitus induced residual THI fraction decreases on the vertical axis, namely the tinnitus reduction degree, the lower the better the change amount is), and the change amount L increases with the increase of the treatment times and is in negative marginal correlation with the treatment effect;

fig. 14 is a diagram of brain electrical detection waveforms.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Referring to fig. 1 to 14, an embodiment of the present invention includes:

the sound and electricity stimulation nerve regulation diagnosis and treatment device combined with electroencephalogram detection analysis feedback control shown in figure 1 is formed, and comprises the following components: the system comprises an electroencephalogram detection and signal processing and analyzing system 2, an intelligent diagnosis and treatment control system 1, an acoustic stimulation treatment system 4, a transcranial electric pulse stimulation treatment system 3 and a cognitive behavior psychotherapy system 5, and forms a closed loop of treatment-detection analysis-treatment control-treatment loop iteration. The acoustic stimulation nerve regulation and control signal of the acoustic stimulation treatment system 4 stimulates the cochlea through the auditory canal to generate an electrical stimulation signal, and then enters the brain center and each connected brain functional area through an auditory nerve passage to carry out deep brain nerve regulation and control treatment. The transcranial electric pulse stimulation treatment system 3 releases electric pulses through an electrode array which is placed on the scalp of a relevant brain functional area, and the electric pulses pass through a skull stimulation cortex and a nervous system to implement shallow cranial nerve regulation treatment. The cognitive behavioral psychotherapy system 5 performs cognitive behavioral psychotherapy by visual reading or video, or by listening to counseling or audio through the ear. The electroencephalogram detection and signal processing and analyzing system 2 collects electroencephalogram signals through an electroencephalogram electrode array placed on the scalp, and performs signal analysis and feedback. The intelligent diagnosis and treatment control system 1 is connected with the electroencephalogram detection and signal processing and analysis system 2 to obtain electroencephalogram analysis result feedback, the control and command of the acoustic stimulation treatment system 4, the transcranial electric pulse stimulation treatment system 3 and the cognitive behavior psychotherapy system 5 connected with the intelligent diagnosis and treatment control system are respectively or uniformly realized according to embedded algorithm software, the personalized acoustic stimulation, transcranial electric pulse stimulation or cognitive behavior psychotherapy of a patient is guided to be selectively or forecasted, the diagnosis and treatment effect is objectively evaluated along with electroencephalogram detection and signal acquisition and analysis, and a doctor or the patient is fed back and guided to perform the next treatment.

In this embodiment, the sound-electricity stimulation neural regulation diagnosis and treatment device combining with the electroencephalogram detection analysis feedback control can adopt an integrated device or a separate device or a wearable device, and each system module is independent or shares a display screen and/or a power supply, so that the size can be reduced, and the cost can be reduced.

Along with the popularity of intelligent digital products and home medical treatment, in order to further reduce the volume and improve the portability, the wearable device can be simplified into. The electroencephalogram analysis feedback and evaluation are written into an APP to be implanted into a smart phone or a handheld tablet, so that the device can be used for neurological dysfunction physical examination and/or screening and/or monitoring and/or prediction, early warning is made, medical inquiry is sought and positive preventive measures are taken, and the device can enjoy mobile and/or remote medical services at home or anytime and anywhere.

In this embodiment, cognitive action psychotherapy system 5 implants the smart mobile phone or handheld flat board through the APP, through interface guide, guide the user to select and implement the cognitive action psychotherapy and/or education and/or consultation and/or treatment of presetting in the APP, simultaneously, the APP can also be connected wearable user and remote expert through wireless network, provide remote control and/or professional consultation service, carry out two-way communication, realize online medical treatment, be favorable to overcoming the inconvenience of patient on-the-spot medical treatment, fear, the mimicry scheduling problem, and need not worry the cross infection problem, along with the development of network technology, this kind of remote medical treatment technique will obtain further popularization.

The constitution and the specific condition of symptoms of different patients are different, so that diagnosis and treatment by certain stimulation cannot be limited, and trial, feedback and optimization are required. Therefore, the electroencephalogram detection and signal processing and analyzing system 2, the acoustic stimulation treatment system 4, the transcranial electric pulse stimulation treatment system 3 and the cognitive behavioral psychotherapy system 5 can not work without the coordination control of the intelligent diagnosis and treatment control system 1.

Therefore, the intelligent diagnosis and treatment control system 1 shown in fig. 3 is constructed, and comprises an acoustic signal following electroencephalogram signal change control module 11, an electric pulse signal following electroencephalogram signal change control module 12 and a cognitive psychotherapy scheme following electroencephalogram signal change control module 13. The intelligent diagnosis and treatment control system 1 utilizes all modules to communicate with an electroencephalogram detection and signal processing and analyzing system 2, an acoustic stimulation treatment system 4, a transcranial electric pulse stimulation treatment system 3 and a cognitive behavior psychology treatment system 5, feeds back and guides the implementation of an optimal treatment scheme and the selection of scheme parameters, implements predictable stimulation regulation and control treatment on a patient, obtains an electroencephalogram signal after treatment, obtains a new stimulation treatment scheme after analysis and treatment, and repeats the steps in such a way, so that the curative effect is continuously improved, and the treatment is accelerated.

In fact, the patient's condition, both physiological and psychological, varies, and therefore, the required treatment parameters or protocols are also randomly varied. In this embodiment, the intelligent electroencephalogram algorithm can be embedded with software through the shared or independent treatment feedback qualitative and quantitative analysis of each control module, the adjustment and optimization of each corresponding treatment scheme parameter and/or treatment scheme using method can be manually or automatically realized, the response to acoustic stimulation, transcranial electric pulse stimulation or cognitive behavior psychotherapy is different due to different physical constitutions and symptoms of different patients, the most suitable stimulation mode or stimulation modes can be guided and determined by trying the acoustic stimulation, transcranial electric pulse stimulation or cognitive behavior psychotherapy according to electroencephalogram signal analysis feedback, the parameters of the stimulation mode or stimulation modes can be corrected in real time according to the curative effect, and the treatment scheme can be continuously optimized until the treatment scheme is cured.

In order for the intelligent diagnosis and treatment control system 1 to accurately make objective diagnosis and treatment effect, the electroencephalogram detection and signal processing and analysis system 2 shown in fig. 2 needs to be constructed, and comprises an electroencephalogram electrode array module 23, an electroencephalogram amplifier module 25, an electroencephalogram signal analysis module 26, an electroencephalogram device control module 22, a display module 24 and a power supply module 21, so as to perform electroencephalogram detection, signal processing and analysis on a patient. Obviously, different from subjective curative effect evaluation, electroencephalogram detection is helpful for quantifying effects and objectively evaluating curative effects, so that subsequent treatment parameters can be more accurately adjusted, and the recovery process can be expressed by percentage or a curve graph instead of fuzzy subjective evaluation or diagnosis and treatment such as 'better' and 'less or more dosage'.

In this embodiment, the electroencephalogram electrode array module 23 includes a plurality of electroencephalogram electrodes, the plurality of electroencephalogram electrodes are disposed on the scalp corresponding to the main brain functional area of the patient, electroencephalogram signals are collected, the electroencephalogram electrode array module 23 is connected with the electroencephalogram amplifier module 25 for signal input and amplification, the power supply module 21 is connected with the electroencephalogram device control module 22 for power supply, the electroencephalogram device control module 22 is respectively connected with the electroencephalogram amplifier module 25, the electroencephalogram signal analysis module 26 and the display module 24 for electroencephalogram time-space modeling and signal analysis and display, the electroencephalogram signal analysis module 26 communicates with the intelligent diagnosis and treatment control system 1 for information transmission, the analyzed result is fed back to the intelligent diagnosis and treatment control system 1, and the optimal adjustment of the intelligent diagnosis and treatment control system 1 on subsequent diagnosis and treatment parameters is facilitated.

The application of the acoustic stimulation therapy is wide, the physiological acoustic detection of the single-side or double-side ear can be carried out through the acoustic stimulation therapy system 4, the physiological acoustic detection comprises tests or questionnaires of hearing, sleep, anxiety, depression, concentration and the like, tinnitus sound matching (including compound sound, frequency, loudness, tone, timbre and melody) and background natural sound are included, a compound acoustic regulation and control scheme of wave amplitude or phase angle or wave amplitude and phase angle mixing is formulated, the operation is simple and convenient, the stimulation therapy can be carried out when a patient wears the earphone, and the operation is very convenient.

Therefore, the acoustic stimulation treatment system 4 shown in fig. 4 is constructed, and includes a physiological acoustic detection module 41, an acoustic stimulation treatment scheme making module 42, an acoustic stimulation treatment module 43 and an acoustic stimulation treatment display screen module 44, the physiological acoustic detection module 41 is connected with the acoustic stimulation treatment scheme making module 42 and provides various parameter information required to be input for scheme making, the acoustic stimulation treatment scheme making module 42 respectively inputs the made acoustic stimulation treatment scheme into the acoustic stimulation treatment module 43 for acoustic stimulation treatment, and the acoustic stimulation treatment display screen module 44 displays the process and result of the physiological acoustic detection, the acoustic treatment scheme and the parameters in real time, qualitatively, quantitatively and visually, and under the guidance of the acoustic signal following the electroencephalogram signal change control module 11, controls the modules of the acoustic stimulation treatment system 4 to work.

For non-otogenic nerve dysfunction, an acoustic stimulation nerve regulation and control treatment scheme and parameters thereof are preselected mainly according to electroencephalogram detection and analysis feedback, and a composite acoustic regulation and control scheme and multi-course treatment are fed back and guided to be optimized and formulated subsequently through treatment-electroencephalogram detection and analysis-treatment control-treatment loop iteration, so that the curative effect is continuously improved. Specifically, the electrical signals corresponding to the acoustic regulation and control scheme in the acoustic stimulation treatment scheme manufacturing module 42 are input into the ear canal through the earphone in the acoustic stimulation treatment module 43, the tympanic membrane and the cochlea vibrate to drive the hair cells in the cochlea to fluctuate and generate electrical stimulation signals, and the electrical stimulation signals enter the brain central system and the brain functional areas such as the thalamus, the hippocampus and the like connected with the brain central system through the auditory nerve channel to drive or activate the electrical excitability of neurons and further amplify the electrical signals, desynchronize disordered signals of the nerve function, and reshape the nerve network, so that the normal working state of nerves is recovered.

Compared with treatment means such as medicines and transcranial electric pulse stimulation, acoustic stimulation is more natural, deep brain stimulation can be more easily controlled and implemented, and in most cases, the deep brain stimulation is pleasant and not unpleasant, and through parameter adjustment and combination with electroencephalogram objective detection and evaluation, a diagnosis and treatment scheme which is easily accepted is more easily found. The adjustable parameters of the acoustic stimulation treatment system 4 include, but are not limited to, hearing threshold, frequency, amplitude, phase angle, peak sharpening, trough filling, wave delay, gap knocking, background noise and/or natural sound, etc., according to the brain electrical objective detection and evaluation treatment effect, the qualitative and quantitative algorithm is embedded into software through the intelligent diagnosis and treatment control system 1, the treatment scheme and the treatment scheme parameter selection are manually or automatically adjusted and optimized, the treatment effect can be enhanced and the rehabilitation is accelerated, the required treatment times or treatment period number can be predicted according to the expected treatment effect, and the patient can conveniently carry out self objective evaluation and scientifically arrange life, work or study outside the treatment.

The electric pulse therapy has the functions of exciting neuromuscular tissues, promoting local blood circulation and the like, and plays an important role in modern medical technology. Therefore, the transcranial electrical pulse stimulation treatment system 3 shown in fig. 5 is constructed, and comprises an electrical pulse signal generator module 31 and an electrical pulse treatment electrode array module 32, wherein the electrical pulse treatment electrode array module 32 comprises a plurality of electrical pulse treatment electrodes arranged on the cortex of the relevant brain functional area, the electrical pulse signal generator module 31 is connected with the electrical pulse treatment electrode array module 32, and under the guidance of the electrical pulse signal following the electroencephalogram signal change control module 12, the electrical pulse signal generator module 31 of the transcranial electrical pulse stimulation treatment system 3 sends out electrical pulse signals, and the electrical pulse is released by the electrical pulse treatment electrode array module 32 to stimulate the intracranial cortex nervous system, so that the purpose of relieving or treating the symptom of the neurological dysfunction is achieved.

Obviously, the parameters of the electrical stimulation treatment scheme are not constant and vary from person to person, and need to be adjusted continuously as the treatment progresses or continues, the parameters of the electrical stimulation treatment scheme include, but are not limited to, pulse frequency, pulse waveform, pulse amplitude (current and/or voltage), pulse width, pulse delay, starting sequence (used alone or in combination with acoustic stimulation treatment), duration, rest interval, repetition number, etc. during the treatment process, according to the electroencephalogram objective detection and evaluation of treatment effect feedback, the qualitative and quantitative algorithm embedded software in the intelligent diagnosis and treatment control system 1 is utilized to make the electrical pulse stimulation scheme including, but not limited to, amplitude modulation and/or frequency modulation, and manually or automatically adjust and optimize the parameters of the electrical stimulation treatment scheme, enhance the treatment effect and accelerate rehabilitation, according to the desired treatment effect and the actual treatment effect, the number of times or the number of courses of treatment needed subsequently is objectively predicted, and even according to the receiving degree of the patient on the electric stimulation treatment, multiple combinations and options of parameters of the electric stimulation treatment scheme and the number of courses of treatment are generated, so that the patient can freely select according to the feeling and the life rule.

The cognitive behavioral psychotherapy has better curative effect on partial nervous dysfunction diseases, such as anxiety, depression, obsessive compulsive disorder and other emotional and stress problems, and is even better than the drug therapy. Therefore, the cognitive behavioral psychotherapy system 5 shown in fig. 6 is constructed, and includes an inquiry scale module 52, a cognitive behavioral psychotherapy module 53 and a cognitive behavioral psychotherapy display screen module 51, the inquiry scale module 52 is connected to the cognitive behavioral psychotherapy module 53 for information input, and the inquiry scale module 52 and the cognitive behavioral psychotherapy module 53 are respectively connected to the cognitive behavioral psychotherapy display screen module 51 for information and scheme display. The cognitive behavioral psychological counseling and/or training and/or education is performed by reading data and/or audio data and/or video data compiled by experts and stored in the cognitive behavioral psychotherapy module 52. Under the guidance of the cognitive psychotherapy scheme following the electroencephalogram signal change control module 13, the cognitive behavioral psychotherapy of objective detection analysis feedback control is carried out. The existing common cognitive behavior psychotherapy usually carries out subjective evaluation on curative effect and adjustment on subsequent treatment schemes by observing the color of the words, doctors subjectively judge the treatment to occupy a leading position, and pay attention to experience to easily ignore objective change of brain functional areas of patients and individualized cognitive behavior psychology performance. In the embodiment, the objective evaluation of the treatment effect can be performed through electroencephalogram signal analysis and feedback, the adjustment of the subsequent treatment scheme is more accurate, and the improvement of the diagnosis and treatment effect is facilitated.

And (3) carrying out cognitive behavioral psychotherapy:

firstly, the cognitive behavioral psychotherapy system 5 makes a preliminary cognitive behavioral psychology consultation and/or education and/or training plan through a cognitive behavioral psychology inquiry scale, can independently perform or cooperate with sound and electricity stimulation therapy, and adjusts or integrates a therapy scheme through trial and error;

secondly, according to the brain electricity objective detection and the treatment effect evaluation, the intelligent diagnosis and treatment control system 1 is used for embedding software by a qualitative and quantitative algorithm, and the cognitive behavior psychotherapy scheme is manually or automatically adjusted and optimized, for example, expert consultations compiled and stored in the cognitive behavior psychotherapy module 53 by selecting experts, training and/or education are performed by reading image-text data and/or audio data and/or video data, and the treatment effect is enhanced and the rehabilitation is accelerated;

finally, the required treatment times or the treatment course number can be predicted according to the expected treatment effect through objective evaluation of the actual treatment effect, and the method is clear at a glance.

In an embodiment of the present invention, a noninvasive neuromodulation and/or neurological disorder screening and predicting method combining electroencephalogram EEG objective detection, analysis, feedback control is provided, which may be applied to diagnosis, treatment, and/or physical examination and/or screening and/or monitoring and/or predicting neurological disorders, including but not limited to tinnitus, deafness, sleep disorders, anxiety, depression, vertigo, ear blockage, headache, mental fatigue, epilepsy, alzheimer's disease (dementia), and parkinson's disease, and specifically includes the following:

the scalp electroencephalogram detection of a plurality of brain functional areas is carried out before, during and after the noninvasive acoustic stimulation and/or the transcranial electric pulse stimulation and/or the cognitive psychotherapy of a human body, the electroencephalogram signal acquisition is carried out, and according to individual differences, the acoustic stimulation, the transcranial electric pulse stimulation and the cognitive psychotherapy can be subjected to single selection or combined treatment schemes and parameter selection thereof through qualitative and quantitative judgment or try.

In this embodiment, the electroencephalogram signals can be obtained by arranging a dry or wet electroencephalogram electrode array on the scalp of the main brain functional area (including but not limited to frontal lobe area, parietal area, temporal lobe area) (as shown in fig. 7), and the electrode signal transmission and amplifier signal acquisition parameters are as follows: the sampling bit number is not less than 10bits, the input impedance should reach G omega level, the equivalent minimum input noise should not be more than 10 muV, the data transmission rate is not less than 1Mbps, and the signal amplitude range is not more than 200 muV.

The brain network can be used to describe the connections between multiple brain regions, in this embodiment, 21 brain electrical electrodes are selected to avoid the volume effect, and network coherence is one of the most common methods for measuring the interaction strength between network nodes, and is used to represent the linear relationship between two different signals in a specific frequency domain.

C _xy (f) Is the network coherence (coherence) of the X and Y signals at frequency point f,P _xy (f) is the cross spectrum (cross spectra) of the X and Y signals,P _xx (f) andP _yy (f) is the self spectrum (self spectrum) of the corresponding signal, coherence in the mth treatment stateC _xy (f)_mCoherence with the nth treatment stateC _xy (f)_nThe differences in (a) and (b) emphasize changes in neural activity or differences in cyberspace topology due to therapeutic effects.

Network attributes are used to make quantitative measurements of brain networks:

C _ij is the network topology coherence (coherence) value between the i electrode and the j electrode; t is the total number of electrodes;d _ij is the path length from i node to j node, Clu-clustering coefficient, L-path length, Ge-global efficiency, L e-local efficiency.

Actually, the onset of disease of patients with neurological dysfunction relates to interaction among a plurality of brain functional areas and the plurality of brain functional areas, after intervention such as acoustic stimulation, transcranial electric pulse stimulation or cognitive psychotherapy, related electroencephalogram signals can change, a brain neural network is remodeled and changed, electroencephalogram signals corresponding to the cortex of the plurality of brain functional areas are collected, real-time electroencephalogram space-time modeling is carried out, corresponding electroencephalogram space-time and network characteristics are extracted, decoding analysis is carried out, subjective judgment can be abandoned, and diseases of patients with neurological dysfunction are objectively, qualitatively and quantitatively evaluated and predicted.

Through the electroencephalogram signal decoding analysis feedback, as shown in fig. 8, electroencephalogram characteristic parameters of treatment effect and treatment times and acoustoelectric stimulation treatment parameters and/or cognitive behavioral psychotherapy methods can be obtained, which is beneficial to establishing a correlation model with curative effect evaluation and realizing closed loop of detection, analysis, feedback, treatment and regulation.

Specifically, the electroencephalogram characteristic parameters comprise an electroencephalogram power spectrum, an electroencephalogram neural network space topology, related network statistical attribute parameters and evaluation indexes, the electroencephalogram characteristic parameters are mastered, and the prediction of the required treatment times according to the expected treatment effect is facilitated.

Actually, the original record of the electroencephalogram signal contains information related and unrelated to the neural function regulation, firstly, component information related to the neural function regulation needs to be obtained from the electroencephalogram signal of the original record, task-related components can be described through feature vectors, the feature vectors related to different tasks are classified by adopting a machine learning algorithm, different brain region activity states are decoded from the electroencephalogram signal, electroencephalogram components closely related to clinical indexes (such as tinnitus induced disability scale, sleep scale, anxiety scale, depression scale and the like) of the neural dysfunction of a patient are obtained, and the unrelated electroencephalogram components are abandoned;

the decoding precision depends on how much the features extracted by the feature algorithm represent related tasks, and how accurately the classification algorithm can distinguish the categories of different tasks, and the higher the decoding precision is, the more objective the diagnosis and treatment effect evaluation of the patient diseases is.

In this embodiment, the decoding and analyzing of the electroencephalogram signals mainly includes the following steps:

preprocessing an electroencephalogram signal: as shown in fig. 9, high-quality electroencephalogram signals, especially signals with wave bands including delta (0-4 Hz, deep sleep), theta (4-8 Hz, shallow sleep), alpha (8-13Hz, closed eye is completely relaxed), beta (14-30Hz, closed eye is entirely concerned with thinking) and gamma (31 Hz or above, high) are obtained by filtering, screening, denoising, discarding, removing artifacts such as electro-oculogram and myoelectricity, analyzing principal components, reconstructing signals and the like, as shown in fig. 14, when the delta wave band occurs in the deep sleep condition, the wave belonging to the 'unconsciousness level' is required for restoring physical sleep; a brain wave component which occurs in the theta band during superficial sleep and lasts for 0.125S to 0.25S; the alpha band, which occurs in relaxed or closed eye conditions, sometimes appears in the brain and sometimes disappears, and is not always present; when the beta wave band appears in human logic thinking, calculation and high concentration degree or is subjected to reasoning to solve problems, time domain, frequency domain and other computational analysis are carried out on EEG signals obtained by subtracting reference electrode signals from electrode signals in different areas, useful and root-source information can be analyzed, partial useless information is removed, and errors and processing workload are reduced;

extraction of electroencephalogram signal features: by carrying out analysis such as electroencephalogram network reconstruction and multi-dimensional discrete wavelet transformation, various characteristic information of electroencephalogram signals is extracted to the maximum extent, and characteristic analysis is carried out on the electroencephalogram signals, so that more, more accurate, more comprehensive and more comprehensive information is provided for diagnosis and treatment effect evaluation, and the condition that part of characteristic information is omitted and deviates from the objectivity and accuracy of evaluation is avoided;

brain electrical signal characteristic identification and characteristic parameter classification, as shown in figure 10, can utilize brain neural network topology, brain electrical space-time information (including amplitude, energy, etc.) as the characteristic, adopt deep neuron network, long-time network, support vector machine, neural network, etc. to carry on characteristic identification and classification, obtain the characteristic parameter of brain electrical, include converting the brain wave of the signal amplitude of brain area where the target brain electrical electrode locates with time change into the power spectrum PSD of the brain electrical power with frequency change, and obtain the network topological structure of brain electrical and relevant including Coh-network coherence, Clu-clustering coefficient, L-characteristic path length, Ge-global efficiency and L e-network statistics such as local efficiency through the network analysis, through the brain electrical detection and signal decoding analysis of the same state and brain area after longitudinal contrast many treatments, obtain the brain electrical characteristic parameter with better sensitivity and associativity to the regulation and control of the neurological disorder, thus observe and understand the change of brain area neural activity and change of different brain areas associativity change more intuitively and quantificationally, set up the associative model of curative effect and curative effect of the neurological disorder of many brain areas;

by the objective analysis of the electroencephalogram characteristic parameters, a tracking evaluation and prediction method of multi-course or multi-time treatment effects can be fed back and guided to be formulated, a method for qualitatively and quantitatively regulating and optimizing implementation and scheme parameters of a subsequent treatment scheme according to an expected treatment effect can be provided, an association model of the electroencephalogram characteristic parameters and the nerve function state can be established according to objective analysis of physical examination and/or screening and/or monitoring of the electroencephalogram characteristic parameters, the nerve function state can be qualitatively and quantitatively evaluated and predicted, and early warning can be performed.

Specifically, the neural dysfunction patient, a healthy person and the same patient are obtained through electroencephalogram signal decoding analysis feedback, the decoding analysis results of electroencephalogram signals of delta wave bands and/or theta wave bands and/or alpha wave bands and/or beta wave bands and/or gamma wave bands obtained before and/or during and/or after each treatment are obtained for the same patient, and the average PSD and/or network statistical attributes (Clu-clustering coefficient, L-characteristic path length, Ge-global efficiency, L e-local efficiency) and/or the change values of neural network topological attributes or network correlation Coh of each brain area before and after multiple treatments are obtained for the same brain area and are used as treatment effect evaluation indexes.

In this embodiment, the power spectrum PSD values of the patient and the healthy person and the comparison between the characteristic values thereof in the relevant frequency range or the whole frequency range are shown in fig. 11, as shown in the correlation evaluation of the change amount of the power spectrum PSD of the same patient and the increase of the treatment times or the improvement of the treatment effect, and the abnormal activity of the nerves in the brain region represented by one or more frequencies corresponding to the peak or the trough of the power spectrum are larger, the change of the abnormal activity of the nerves in the corresponding brain region is more obvious, that is, the treatment effect of the neurological dysfunction is more obvious.

Experiments show that the change of the network attributes Clu, Ge and L e of the same patient has a correlation with the increase of the number of treatments or the treatment effect, as shown in fig. 13, the more significant the difference of the network attributes is, the more the symptom of the neural dysfunction is improved, the difference of the network attributes has short connection between the auditory region (temporal lobe) and the sensory region (top lobe) and long connection between the auditory region (temporal lobe) and the emotional region (frontal lobe), the difference mode of the abnormal response is probably related to tinnitus and neural fatigue caused by the neural dysfunction, attention weakness, sensory disorder, anxiety and depression, the neural network organically connects the acoustic stimulation treatment (such as the temporal lobe region) with the electric pulse treatment (such as the top lobe region) and the cognitive behavior psychotherapy (such as the frontal lobe region) to evaluate the treatment effect, and the network-related Coh difference reflects the network space topological difference between the brain regions, as shown in fig. 12, that the strength difference of the related activity degree is related to help to evaluate the effect of different treatment schemes and the effect after a plurality of treatments.

Actually, relative to the analysis of the power spectrum, the network coherence and the network attribute reflect the overall difference between the same brain area and/or different brain areas, and can reflect the abnormal activity change quantity of the brain area, namely the connection strength between various physiological and/or auditory and/or psychological and/or emotional and/or memory and/or attention and consciousness and/or sensory-motor brain functional areas, further reveals that the generation of the neurological dysfunction such as tinnitus is a comprehensive pathogenesis, provides relatively reliable standard parameters for treatment, and further guides the compiling of qualitative and quantitative algorithms for tracking, evaluating and predicting the treatment effect.

In order to ensure the objectivity of diagnosis and treatment evaluation, it is obviously a comprehensive engineering, therefore, a comprehensive system is required to evaluate three treatment schemes of acoustic stimulation, transcranial electric pulse stimulation and cognitive behavior psychology according to the PSD change amount of a power spectrum, the relevance of PSD corresponding to a plurality of peaks or troughs of power spectrum and frequency, the network coherence Coh difference and the network attributes Clu, Ge, L e and L change amount, and a method of singly or jointly implementing the treatment effect difference, the multi-course treatment effect and the progress, or setting a treatment target and an evaluation standard, predicting the number of required treatment courses or the treatment times, regulating and optimizing subsequent treatment parameters according to the expected treatment effect and using one or a mixture of a plurality of treatment plans is slightly simplified by combining an electroencephalogram objective detection analysis feedback method, so that the method can be used for physical examination, screening, modeling and prediction of neurological dysfunction, and making early warning of neurological dysfunction according to the above-diagnosis state difference or change relative to a healthy state, is beneficial to ask for medical treatment in time and taking positive preventive measures.

The method for the objective assessment of the effect of treatment by electroencephalographic detection and analysis is further described below in accordance with the examples:

one, test control group, variable W

1. Healthy persons versus tinnitus patients;

2. the patient is the patient himself.

Second, test state, variable X

1. pre, testing electroencephalogram before treatment;

2. in, testing brain electricity in treatment;

3. post, test brain electrical after treatment.

Thirdly, extracting and analyzing an electroencephalogram characteristic parameter S for setting a brain area (electrode node) variable Y (energy difference change and nerve network remodeling change between brain areas):

1. the average power spectrum PSD, fig. 11;

2. neural network coherence Coh and average neural network topology attribute metrics, as shown in FIGS. 12 and 13, (e.g., Clu-cluster coefficient; L-eigenpath length; Ge-global efficiency; L e-local efficiency);

fourthly, the electroencephalogram band and the variable Z comprise:

1. delta band (0-4 Hz, deep sleep);

2. the theta band (4-8 Hz, light sleep);

3. alpha (8-13Hz) band (closed eye fully relaxed);

4. beta (14-30Hz) band (eye closure full attention thinking);

5. gamma band (31 Hz and above, high).

Fifthly, aiming at the same patient, under the conditions of nerve regulation and control sound-electricity stimulation treatment times n, setting a contrast group W, a test state X, a test brain area Y and an electroencephalogram wave band Z, decoding (preprocessing, feature extraction, feature identification and classification) EEG signals, and calculating an electroencephalogram feature parameter S_n(W, X, Y, Z) and pre-treatment (n = 0) parameters S₀Difference △ S of (W, X, Y, Z)_n(W,X,Y,Z) = S_n(W,X,Y,Z) – S₀(W,X,Y,Z)；

Sixthly, the difference value △ S of the electroencephalogram characteristic parameters_n(W, X, Y, Z) is the ordinate, and the number of treatments n is the abscissa, to prepare a two-dimensional treatment effect curve.

Example of obtaining brain electrical judgment parameters and criteria for adjusting stimulation therapy parameters:

first, presetting nerve regulation and control sound and electricity stimulation treatment parameters and evaluating effect (unilateral or bilateral ear)

1. Creating a complex acoustic regulation scheme (complex acoustic regulation therapy of amplitude or phase angle or mixture of amplitude and phase angle) according to Psychoacoustic Testing (including but not limited to hearing, sleep, anxiety, depression, energy concentration) and tinnitus acoustic matching (including but not limited to complex sound, frequency, loudness, tone, timbre, melody), and for non-otogenic neurological dysfunction, selecting parameters for creating the complex acoustic regulation scheme mainly guided by the results of electroencephalographic Testing and analysis, the parameters including but not limited to hearing threshold, frequency, amplitude, phase angle, peak sharpening, valley filling, wave delay, knock gap, background noise and/or natural sound;

2. neuromodulation transcranial electrical pulse stimulation treatment protocols, parameters including, but not limited to, pulse amplitude and/or frequency modulation associated amplitude, frequency, phase angle, etc., pulse frequency, pulse waveform, pulse amplitude (current or voltage), pulse width, pulse delay;

3. acoustoelectric stimulation treatment protocol implementation parameters: start sequence (single or simultaneous start), duration, rest interval; the number of repetitions;

4. and (3) effect evaluation: number of treatments n =0 based on the pre-treatment status as a comparison

Difference △ S of electroencephalogram characteristic parameters₁(W, X, Y, Z), increasing or decreasing (in relation to specific characteristic parameters) in accordance with rules given in prior research efforts or clinical findings;

changes in tinnitus conditions are subjective and sensed by the patient, and comprise physiological and cognitive psychology, for example, changes in score assessed by the tinnitus disability table THI represent changes in treatment effect; and performing auxiliary verification by means of numerical simulation modeling and analysis of a neural network computer, functional nuclear magnetic imaging (fMRI), animal experiments and the like.

The two evaluation methods are used independently or in combination;

adjusting subsequent nerve regulation and control acoustoelectric stimulation treatment parameters and evaluating effects (unilateral or bilateral ears), wherein before n +1 times of treatment:

1. by comparing current △ S_n(W, X, Y, Z) and previously △ S_n-1(W, X, Y, Z) trend, intelligent identification, preferably acoustic stimulationThe desired value △ S is reached by the treatment parameters (including but not limited to hearing threshold, frequency, amplitude, phase angle) and the electrical pulse stimulation treatment parameters (including but not limited to waveform, amplitude, wave width, pulse delay)_n+1(W,X,Y,Z)；

2. The acoustic-electric stimulation treatment scheme implementation parameters are preferably as follows: including but not limited to treatment duration, rest interval, number of repetitions;

3. the two parameter adjustment steps are manually or automatically implemented by a programming algorithm;

4. by the difference value △ S of the electroencephalogram characteristic parameters_nThe absolute values of (W, X, Y and Z) are vertical coordinates, the treatment times n are horizontal coordinates, a two-dimensional treatment effect curve is manufactured and displayed on a display screen, and the visualization that the tinnitus curing degree is represented by the tinnitus treatment electroencephalogram detection characteristic parameter change is realized, as shown in figure 13.

Example of acoustic electrical stimulation treatment assessment using big data and artificial intelligence analysis:

method and standard for evaluating therapeutic effect

1. Establishing a large database through clinical treatment data of a large sample amount of patients, performing data mining, performing artificial intelligent AI analysis, and obtaining a difference value △ S of the brain-electrical characteristic parameters_n(W, X, Y, Z) expresses evaluation criteria of the treatment effect of the acoustic-electric stimulation, and comprises electroencephalogram characteristic parameters expressed by mean power spectrum PSD, neural network coherence Coh, mean neural network topological attribute indexes including but not limited to Clu-clustering coefficient, L-characteristic path length, Ge-global efficiency, L e-local efficiency and the like under the conditions of setting contrast group W, test state X, test brain area Y and electroencephalogram wave band Z respectively or in combination

S_n(W, X, Y, Z), and evaluation criteria parameters A (W, X, Y, Z), B (W, X, Y, Z) and C (W, X, Y, Z)

a. Recovery, -, △ S_n(W,X,Y,Z) │≧A(W,X,Y,Z)

b. Remarkably effective, and B (W, X, Y, Z) is less than or equal to | △ S_n(W,X,Y,Z) │<A(W,X,Y,Z)

c. Basically effective, C (W, X, Y, Z) ≦ △ S_n(W,X,Y,Z) │<B(W,X,Y,Z)

d. Basic invalidation, | △ S_n(W,X,Y,Z) │<C(W,X,Y,Z)

2. Evaluation criteria parameters a (W, X, Y, Z), B (W, X, Y, Z) and C (W, X, Y, Z) were selected:

a. given S, W, X, Y, and Z

b. A, B and C evaluation criteria data for each patient based on the effects of multiple clinical service patients

c. And (3) establishing a large database and carrying out AI deep mining analysis to obtain A, B and C evaluation standard data relative to each neurological dysfunction disease characteristic classification under the common meaning of large sample size.

In summary, the present invention provides a method and an apparatus for controlling sound-electricity stimulation nerves by combining with electroencephalogram detection analysis feedback control, which analyze feedback according to an electroencephalogram objective detection result, qualitatively and quantitatively guide the selection of treatment schemes and scheme parameters in order to obtain a desired diagnosis and treatment effect, realize the selection of treatment schemes such as acoustic stimulation, transcranial electrical pulse stimulation, cognitive behavior psychology, etc., or optimize and control the parameters of a combination treatment scheme, design and/or predict the number of treatment procedures or treatment times according to treatment target settings, perform physical examination and/or screening and/or monitoring and/or prediction of neurological dysfunction, and achieve early warning of neurological dysfunction for a doctor or timely take preventive measures according to the difference or change of the neurological function state relative to a healthy state.

The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, or direct or indirect applications in other related fields, which are made by the contents of the present specification, are included in the scope of the present invention.

Claims (10)

1. A noninvasive neuromodulation and/or neurological dysfunction screening and/or predicting method combining electroencephalogram (EEG) objective detection and analysis feedback control, is characterized by comprising the following steps:

carrying out electroencephalogram detection on a plurality of physiological and/or auditory and/or psychological and/or emotional and/or memory and/or attention and consciousness and/or sensorimotor brain functional areas of a human body before, during and after noninvasive acoustic stimulation and/or transcranial electric pulse stimulation and/or cognitive psychotherapy, and carrying out electroencephalogram signal acquisition;

through electroencephalogram signal decoding analysis feedback, acquiring electroencephalogram characteristic parameters which are related to treatment effects, treatment times, acoustoelectric stimulation treatment parameters and/or cognitive behavioral psychotherapy methods, wherein the electroencephalogram characteristic parameters comprise an electroencephalogram power spectrum, an electroencephalogram neural network space topology, related network statistical attribute parameters and evaluation indexes;

the method comprises the steps of feeding back and guiding to make a tracking evaluation and prediction method of multi-course or multi-time neural regulation treatment effect through objective analysis of electroencephalogram characteristic parameters, qualitatively and quantitatively regulating and optimizing a subsequent treatment scheme implementation and scheme parameter selection method according to an expected treatment effect, establishing an associated model of electroencephalogram characteristic parameters and neural function states according to objective analysis feedback of physical examination or screening or monitoring of electroencephalogram characteristic parameters, qualitatively and quantitatively evaluating and predicting the neural function states, and carrying out early warning.

2. The method of claim 1, wherein the onset of the patient with neurological dysfunction involves the interaction between multiple brain functional area disorders and multiple brain functional areas, and the associated EEG signals will change and remodel the corresponding brain neural network after acoustic stimulation and/or transcranial electrical pulse stimulation and/or cognitive psychotherapy, so that the disease of the patient with neurological dysfunction can be objectively, qualitatively and quantitatively evaluated and predicted by collecting EEG signals corresponding to multiple brain functional area cortex, extracting corresponding EEG space-time and network characteristics, performing decoding analysis, and the EEG signals can be obtained by placing dry or wet EEG arrays with a total number of not less than 2 leads in the main brain functional areas, the electrode signal transmission and amplifier signal acquisition parameters are as follows: the sampling bit number is not less than 10bits, the input impedance should reach G omega level, the equivalent minimum input noise should not be more than 10 muV, the data transmission rate is not less than 1Mbps, and the signal amplitude range is not more than 200 muV.

3. The method for non-invasive neuromodulation and/or neurological dysfunction screening and/or prediction in conjunction with objective electroencephalography (EEG) detection and analysis feedback control according to claim 2,

acquiring component information related to nerve function regulation from an original recorded electroencephalogram signal, describing task related components through feature vectors, classifying the feature vectors related to different tasks by adopting a machine learning algorithm, decoding different brain region activity states from the electroencephalogram signal, obtaining electroencephalogram components closely related to clinical indexes of nerve dysfunction of a patient, wherein the decoding precision depends on how much degree the features extracted by the feature algorithm represent the related tasks, and the classification algorithm can accurately distinguish the categories of different tasks, and the electroencephalogram signal decoding analysis comprises but is not limited to the following steps:

A. preprocessing an electroencephalogram signal: obtaining high-quality electroencephalogram signals, particularly signals comprising but not limited to delta, theta, alpha, beta and gamma wave bands, by means of signal filtering screening, denoising and discarding, electro-oculogram and electromyogram artifact removing, principal component analysis and signal reconstruction;

B. extraction of electroencephalogram signal features: by means of brain electrical network reconstruction and multi-dimensional discrete wavelet transformation, various feature information of brain electrical signals is extracted to the maximum extent, and feature analysis is carried out on the brain electrical signals, so that more, more accurate, more comprehensive and more comprehensive information is provided for diagnosis and treatment evaluation;

C. the electroencephalogram signal characteristic identification and characteristic parameter classification comprises the steps of adopting brain neural network topology and electroencephalogram space-time information which are not limited to serve as characteristics, adopting a deep neuron network, a long-time network, a short-time network, a support vector machine and a neural network to carry out characteristic identification and classification, and obtaining electroencephalogram characteristic parameters which include but are not limited to converting electroencephalogram waves of a brain area where a target electroencephalogram electrode is located, wherein the signal amplitude of the brain area changes along with time, into a power spectrum PSD of electroencephalogram power changing along with frequency, obtaining network topology of the electroencephalogram and related network statistical values which include but are not limited to Coh-network coherence, Clu-clustering coefficient, L-characteristic path length, Ge-global efficiency and L e-local efficiency through network analysis, obtaining electroencephalogram characteristic parameters with good sensitivity and relevance to regulation and control of neurological dysfunction through longitudinally comparing electroencephalogram detection and signal decoding analysis of the brain areas after multiple treatments, and observing and understanding brain area neural activity change and different brain area relevance change more intuitively and quantificationally, and evaluating treatment effects and neural abnormal activity relevance among multiple brain areas.

4. The method for noninvasive neuromodulation and/or neurological dysfunction screening and/or prediction in conjunction with EEG objective detection analysis feedback control of claim 3,

D. obtaining the neural dysfunction patient and the healthy person through electroencephalogram signal decoding analysis feedback, and obtaining the decoding analysis result of electroencephalogram signals of delta wave band and/or theta wave band and/or alpha wave band and/or beta wave band and/or gamma wave band obtained before and/or during and/or after each treatment of the same patient, obtaining the power spectrum average PSD and/or the network statistical attribute (Clu-clustering coefficient, L-characteristic path length, Ge-global efficiency, L e-local efficiency) and/or the change value of neural network topological attribute or network correlation Coh of each brain area before and after a plurality of treatments in the same brain area, and using the change value as the treatment effect evaluation index;

E. comparing the PSD values of the power spectrums of the patients and healthy people and the characteristic values thereof in the relevant frequency or all frequency ranges, evaluating the relevance between the change amount of the PSD of the power spectrum of the same patient and the increase of treatment times or the improvement of treatment effect, and reflecting the abnormal activity of the nerves in the brain area by one or more frequencies corresponding to the peaks or the troughs of the power spectrum; the larger the power spectrum change amount is, the more obvious the corresponding abnormal activity change of the nerves in the brain area is, namely, the more obvious the treatment effect of the neurological dysfunction is;

F. the change amount of the network attributes Clu, Ge, L e and L of the same patient is related to the increase of treatment times or treatment effect, the more significant the network attribute difference is, the larger the improvement of the symptom of the nerve dysfunction is, the short connection between an auditory area and a sensory area and the long connection between the auditory area and an emotional area exist in the network attribute difference, which shows that the difference mode of the abnormal response may be related to tinnitus and nerve fatigue, attention weakness, sensory disturbance, anxiety and depression caused by the nerve dysfunction, the treatment effect can be systematically evaluated by organically linking the acoustic stimulation treatment with electric pulse treatment and cognitive behavior psychotherapy through the analysis of the nerve network attribute, and the network correlation Coh difference reflects the network space topological difference between brain areas, namely the strength difference of the correlation activity degree, which can help to evaluate the effect of different treatment schemes and the effect after a plurality of treatments;

G. relative to the analysis of power spectrum, the network coherence and network attributes reflect the overall difference between the same brain area and/or different brain areas, and can reflect the abnormal activity change of the brain area, which is the connection strength between the physiological and/or auditory and/or psychological and/or emotional and/or memory and/or attention and consciousness and/or sensory-motor brain functional areas, further reveals that the generation of tinnitus neurological dysfunction is a comprehensive pathogenesis of physiological and cognitive behavioral-psychological interaction, provides relatively reliable evaluation standard parameters for treatment, and further guides the writing of qualitative and quantitative algorithms for tracking, evaluation and prediction of treatment effect, and makes a comprehensive system estimate the curative effect difference, multiple therapeutic effects and progress of the respective or combined implementation of three treatment schemes of acoustic stimulation, cranial electrical pulse stimulation and cognitive behavioral psychological stimulation, or sets therapeutic targets and evaluation standards, predicts the number of required treatment or the number of treatment times, optimizes the treatment effect according to the expected treatment and/or the subsequent treatment state, and/or optimizes the early warning and/or early warning method for taking a simple and/or early warning method for health state detection and/or early warning of neurological dysfunction, and/or early warning of brain dysfunction;

H. through clinical treatment of a large sample amount of patients, a large database is established, deep data mining is carried out, artificial intelligent AI analysis is carried out, and a more general curative effect evaluation method and indexes related to the neural dysfunction characteristic classification are obtained;

I. the method is characterized in that the neural activity of the brain functional region before and after the sound and electricity stimulation neural regulation and control treatment, the change of the neural activity and the evaluation of the curative effect are verified in an auxiliary way by means of neural network computer numerical simulation modeling and analysis and/or functional nuclear magnetic imaging (fMRI) and/or animal experiment.

5. The method for noninvasive neuromodulation and/or neuromodulation in combination with EEG objective detection analysis feedback control according to claim 1, which is applied for qualitatively and quantitatively guiding and optimizing diagnosis and/or physical examination and/or screening and/or monitoring and/or predicting of neurological disorders, including but not limited to tinnitus, deafness, sleep disorders, anxiety, depression, vertigo, ear congestion, neuropathic headache, mental fatigue, epilepsy, Alzheimer's disease and Parkinson's disease.

6. An acoustoelectric stimulation neural regulation diagnosis and treatment device combined with electroencephalogram detection and analysis feedback control is characterized by comprising but not limited to: the brain electricity detection and signal processing and analyzing system, the intelligent diagnosis and treatment control system, the acoustic stimulation treatment system, the transcranial electric pulse stimulation treatment system and the cognitive behavior psychotherapy system, the brain electricity detection and signal processing and analyzing system comprises but is not limited to a brain electricity electrode array module, a brain electricity amplifier module, a brain electricity signal analyzing module, a brain electricity equipment control module, a display module and a power supply module, the intelligent diagnosis and treatment control system comprises but is not limited to an acoustic signal following brain electricity signal change control module, an electric pulse signal following brain electricity signal change control module and a cognitive psychotherapy scheme following brain electricity signal change control module, the intelligent diagnosis and treatment control system shares or independently carries out qualitative and quantitative analysis on intelligent brain electricity algorithm embedded software through treatment feedback of each control module, and manually or automatically realizes adjustment and optimization on parameters of each corresponding therapy scheme and/or a therapy scheme using method, the acoustic stimulation treatment system comprises but is not limited to a physiological acoustic detection module, an acoustic stimulation treatment scheme manufacturing module, an acoustic stimulation treatment module and an acoustic stimulation treatment display screen module, the transcranial electric pulse stimulation treatment system comprises but is not limited to an electric pulse signal generator module and an electric pulse treatment electrode array module, the cognitive behavior psychotherapy system comprises but is not limited to an inquiry table module, a cognitive behavior psychotherapy module and a cognitive behavior psychotherapy display screen module, the acoustic-electric stimulation neural regulation and treatment device combined with electroencephalogram detection analysis feedback control adopts an integrated device or a separated device or a wearable device, and each system module is independent or shares a display screen and/or a power supply.

7. The EEG-based EEG detection and analysis feedback controlled SOG neural modulation diagnosis and treatment device as claimed in claim 6, wherein the device is simplified into a wearable device, including but not limited to a wearable EEG detection and analysis system, a cognitive behavioral psychotherapy system, a telemedicine system based on EEG analysis feedback, and a smart phone or a tablet computer connected with the system in a wired or wireless manner, wherein the EEG analysis feedback and evaluation is written as an APP to be implanted into the smart phone or the tablet computer, so as to be used for the physical examination and/or screening and/or monitoring and/or prediction of neurological dysfunction, to make early warning, to ask medical advice and/or take positive preventive measures, and the cognitive behavioral psychotherapy system is implanted into the smart phone or the handheld tablet computer through the APP for guiding a wearable device user to select and implement a preset cognitive behavioral psychotraining and/or education and/or counseling and/or treatment scheme, simultaneously, APP still with wearable equipment user with remote expert pass through wireless network and be connected, provide remote control and/or professional consultation service, carry out two-way communication.

8. The acoustoelectric stimulation neuromodulation diagnosis and treatment device combined with electroencephalogram detection and analysis feedback control as claimed in claim 6, wherein the acoustics stimulation treatment system is used for performing physiological acoustics detection of one side or two sides of ears, including but not limited to hearing, sleep, anxiety, depression, concentration test or questionnaire, tinnitus sound matching, background natural sound, and making a composite acoustics regulation and control scheme with amplitude or phase angle or amplitude and phase angle mixing, for non-otogenic nerve dysfunction, the composite acoustics regulation and control scheme is guided and made through electroencephalogram detection and analysis result feedback, corresponding electric signals in the acoustics stimulation treatment scheme making module are converted into sound wave signals through an earphone in the acoustics stimulation treatment module and input into an ear canal, a tympanic membrane and a cochlea are vibrated, and hair cells in the cochlea are driven to fluctuate to generate electric stimulation signals, entering physiological and/or auditory and/or psychological and/or emotional and/or memory and/or attention and consciousness and/or sensorimotor functional areas of a central system of the brain through an auditory pathway, driving or activating electrical excitability of neurons and generating amplified electrical stimulation signals, desynchronizing disordered signals of brain functional areas, reshaping a brain neural network, and restoring the nerves of all brain functional areas to a normal working state, wherein adjustable parameters of the acoustic stimulation treatment system comprise but are not limited to hearing threshold, frequency, amplitude, phase angle, peak sharpening, trough filling, wave delay, notch knocking, background noise and/or natural sound, manually or automatically adjusting and optimizing acoustic stimulation treatment scheme selection and treatment scheme parameters through qualitative and quantitative algorithm embedded software of an intelligent diagnosis and treatment control system according to brain electrical objective detection and evaluation treatment effects, enhance the treatment effect and accelerate the rehabilitation, and predict the required treatment times or treatment courses according to the expected treatment effect.

9. The sound-electricity stimulation nerve regulation and treatment device combined with electroencephalogram detection and analysis feedback control as claimed in claim 6, wherein the electrical pulse signal generator module of the transcranial electrical pulse stimulation treatment system sends electrical pulse signals, the electrical pulse signals are released through the electrical pulse treatment electrode array module arranged on the cortex of the relevant brain functional area to stimulate the intracranial cortex nervous system, so as to achieve the purpose of relieving symptoms of neurological dysfunction, parameters of the electrical stimulation treatment scheme include, but are not limited to, pulse frequency, pulse waveform, pulse amplitude, pulse width, pulse delay, start sequence, duration, rest interval and repetition number, treatment effect is detected and evaluated objectively according to electroencephalogram, manual or automatic adjustment and optimization of treatment scheme parameters are fed back and guided, software is embedded through qualitative and quantitative algorithms of the intelligent treatment control system, so as to formulate an electrical pulse stimulation scheme including, but not limited to, amplitude modulation and/or frequency modulation, enhance the treatment effect and accelerate the rehabilitation, and predict the required treatment times or treatment courses according to the expected treatment effect.

10. The sound-electricity stimulation neural regulation diagnosis and treatment device combined with electroencephalogram detection and analysis feedback control as claimed in claim 6, wherein the cognitive behavioral psychotherapy system makes a preliminary cognitive behavioral psychology consultation and/or education and/or training plan through a cognitive behavioral psychology inquiry table, can independently perform or cooperate with the sound-electricity stimulation therapy, performs treatment effect feedback according to electroencephalogram objective detection and evaluation, manually or automatically adjusts and optimizes a cognitive behavioral psychotherapy scheme through an intelligent diagnosis and treatment control system qualitative and quantitative algorithm embedded software, including but not limited to expert consultation, reading data and/or audio data and/or video data compiled by experts and stored in a cognitive behavioral psychotherapy module for consultation and/or training and/or education, enhances treatment effect and accelerates rehabilitation, the number of treatments or courses required is predicted based on the desired therapeutic effect.

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