CN115227263A - Neural stimulation system based on EEG signal theta oscillation regulation and control - Google Patents

Neural stimulation system based on EEG signal theta oscillation regulation and control Download PDF

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CN115227263A
CN115227263A CN202210835912.5A CN202210835912A CN115227263A CN 115227263 A CN115227263 A CN 115227263A CN 202210835912 A CN202210835912 A CN 202210835912A CN 115227263 A CN115227263 A CN 115227263A
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詹克君
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Wuxi Zhifeng Technology Co ltd
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Abstract

The invention provides a neural stimulation system for real-time regulation and control based on EEG signal theta oscillation, which analyzes the phase of the theta oscillation by real-time signal processing through recording the theta oscillation so as to perform neural stimulation at a specific theta phase. The frequency and the mode of nerve stimulation are controlled based on the characteristics of the electroencephalogram signals of the subjects, the determination that the stimulation scheme cannot be provided in a customized manner by the existing nerve stimulation technology can be eliminated, and the influence of inherent factors and non-inherent factors of the subjects can be overcome; is beneficial to inducing the long-term enhancement of synaptic plasticity related to memory, and has further enhancement effect on nervous system diseases and mental diseases.

Description

Neural stimulation system based on EEG signal theta oscillation regulation and control
Technical Field
The invention relates to the field of nerve regulation, in particular to a nerve stimulation system for real-time regulation and control based on theta frequency band signals in EEG signals.
Background
Neural stimulation in its broadest sense includes neural stimulation in a variety of stimulation modes, including, for example, visual signals or visual and audio signals or auditory and peripheral neural signals or peripheral neural stimulation (e.g., electrical impulses). The brain may sense multiple patterns of stimulation, and in response to observing stimulation such as visual, auditory, electrical impulses, etc., the brain may adjust, manage, or control the frequency of neural oscillations, which may result in repeated activation of portions of the brain known to process input, and the frequency of neural oscillations such as brain waves may be influenced by or correspond to the frequency of light or audio pulses. Rhythmic or repetitive neural activity of the central nervous system, nervous tissue, may produce oscillatory activity through mechanisms within individual neurons or interactions between neurons, whereas neural-oscillation-based neural stimulation is not even a modern phenomenon, and mankind knows for hundreds of years that rhythmic drumming may help bring a person into a wandering state, a process also known as brain wave entrainment (or nerve entrainment), to synchronize the neural oscillatory activity between groups of neurons based on the frequency of the neurons. Thus, neural stimulation may modulate, control, or otherwise affect the frequency of neural oscillations to provide a beneficial effect on one or more cognitive states, cognitive functions, immune systems, or inflammation, while mitigating or preventing adversely affecting functions on cognitive states or cognition.
The nerve stimulation medical instrument based on the principle has wide application in the aspect of disease treatment, particularly the nerve stimulation method plays an important role in nervous system diseases and mental diseases, and a plurality of places of a human body can apply nerve regulation and control technology, for example, a deep brain electrical stimulation system implanted in the head can be used for relieving tremor symptoms of Parkinson's disease; the spinal cord stimulation system implanted in the back can inhibit intractable back pain; there are also electrical sacral nerve stimulation systems implanted near the sacral spine, which are used to treat refractory urinary urgency; the spinal nerve stimulation system also plays a role in the rehabilitation process of paralyzed patients.
However, the existing nerve stimulation technology mostly adopts fixed frequency and fixed mode for stimulation. The expected therapeutic effect is achieved by selecting and adjusting the stimulation parameters, including the current magnitude, the frequency and the like. However, there is a clear problem that the frequency of the neural oscillations is influenced by various factors specific to the subject, and subjects with certain characteristics (e.g. age, sex, familiar hands, cognitive function, mental illness, etc.) may respond differently to the stimulation signal based on these or other characteristics, traits or habits, and furthermore, there are other extrinsic factors affecting, for example, the level of attention of the subject, the time of day of treatment and various factors related to the subject's diet (e.g. blood glucose, caffeine intake, nicotine intake, etc.), mental state, physical and/or mental condition, etc., which may result in the neural oscillations of the stimulating subject not matching perfectly with the fixed frequency or fixed pattern stimulation.
In the field of neurostimulation medical treatment, there is a lack of neurostimulation technology and related systems based on the real-time state of a nerve signal, and on the other hand, when neurostimulation is used for treating neurological diseases and mental diseases, it is desired to improve the effect thereof in a targeted and customized manner.
Disclosure of Invention
The neural oscillation of the brain central nervous system is mainly represented by five types in the frequency bands of EEG signals, namely delta, theta, alpha, beta and gamma, and the significance, the function, the generation mechanism and the like of the neural oscillation are different in different frequency bands. For example, waves are related to alertness and work, delta-wave sleep and dreams. Theta waves (Theta oscillations), which generally refer to signals having a frequency in the range of 4-8Hz, are usually generated in a relaxed state and, with different psychological tasks, can induce Theta oscillations at the midline position in the front of the head.
the strength of theta oscillation activity is found to be related to the same character, including anxiety state score, nervonic quality score, extroversion score and the like; in working memory related tasks, theta oscillations are related to the encoding and maintenance of working memory. It has been found that during the performance of a task, the amplitude of the high frequency signal (e.g., gamma band) may be coupled to the phase of the theta oscillation, indicating that the amplitude of the gamma band is synchronized with a particular phase.
The recording of neuronal firing indicates that long-term enhancement of memory-related synaptic plasticity can be induced when stimulated in the theta band, and thus modulation of the pattern of neural stimulation based on theta oscillations of EEG signals will have a further enhancing effect on neurological and psychiatric disorders.
Based on the above situation, the invention provides a neural stimulation system for real-time regulation and control based on the theta oscillation of the EEG signal, and the system analyzes the phase of the theta oscillation by recording the theta oscillation and performing real-time signal processing, thereby performing neural stimulation at a specific theta phase. Specifically, the invention provides a nerve stimulation system for real-time regulation and control based on EEG signal theta oscillation, which comprises:
the system comprises an EEG signal acquisition module, an EEG signal processing module, a control module, a communication module, a feedback stimulation module and a P1 value determination module;
the EEG signal acquisition module comprises an EEG electrode and an amplifier and transmits the acquired EEG signal to the EEG signal processing module through the communication module;
the EEG signal processing module is used for preprocessing the EEG signals acquired by the EEG signal acquisition module, extracting Theta waves with the phase frequency range of 4-8Hz from the preprocessed EEG signals through a filtering algorithm, taking out the Theta phases through Hilbert Transform, and transmitting the Theta phases of the EEG of the testee to the control module through the communication module,
the P1 value determining module is used for obtaining a P1 value; the P1 value acquisition module acquires the acquired EEG signals from the EEG signal processing module before starting the neural regulation, or the acquired EEG signals can be previous EEG data in a database, P1 values are obtained after the EEG signals are processed, and then feedback type neural regulation is carried out according to phase values to be regulated. The P1 value is obtained through the regulation relation between the theta wave and the gamma wave of the EEG signal; the value of P1 is obtained in the following manner:
obtaining a theta wave phase signal phi through Hilbert conversion U (t) and gamma amplitude signal A U (T) the sampling time point is U = { T = k Δ T, k ∈ 1,2, … T }. Δ T is the sampling interval and T is the number of samples. Due to the phase phi U (t) is a periodic signal, ranging from-pi to pi, the phase range S = { m Δ N, m =1,2, … N }, Δ N is the phase interval, N is the number of fractions evenly divided from-pi to pi. Therefore, we can calculate the amplitude signal A U (t) distribution A over different phase ranges S S ＝{A U (phi), phi belongs to s, and As is the amplitude signal A U (t) distribution over the phase range S. To obtain at which phase A S Has a phase-locked relationship, we calculate the entropy between phase and amplitude,
Figure BDA0003748150060000031
wherein p is m Is calculated as follows
Figure BDA0003748150060000032
Wherein, the first and the second end of the pipe are connected with each other,<A S >phi (m) represents the average value of the amplitude of the corresponding gamma amplitude when the current theta phase value is m delta n;
by calculating the H value at different phases S = { m Δ N, m =1,2, … N }, there is a phase-locked relationship between the phase of the theta wave and the gamma wave when H takes the maximum value. We take the corresponding phase to get phi max = argmax (H), i.e. at the phase phi max The upper theta wave and the gamma wave have a coupling relation, so that the obtained P1= phi max . IntoStep one, the coupling relation C is calculated by the formula:
Figure BDA0003748150060000033
H max the maximum value of H indicates that there is no coupling relationship between C and H when C is 0, and indicates that the coupling relationship between C and H is increased when C is increased.
And after the control module obtains the value of P1, judging whether to send a stimulation instruction to the feedback stimulation module according to the real-time theta phase data, and when the theta phase is positioned near the value of P1, sending the stimulation instruction to the feedback stimulation module. Usually, when the theta phase is set to be within the range of [ P1- Δ n, P1+ Δ n ], a stimulation instruction is sent to the feedback stimulation module, and the specific range can be set according to actual needs. Here [ P1- Δ n, P1+ Δ n ] is located between- π and π cyclically.
The communication module is used for transmitting instructions among the EEG signal acquisition module, the EEG signal processing module, the control module, the P1 value determination module and the feedback stimulation module,
and the feedback stimulation module receives the stimulation instruction transmitted from the control module through the communication module, starts the nerve stimulation means based on the stimulation instruction, and stops the nerve stimulation means when the stimulation instruction transmitted from the control module through the communication module stops.
In a preferred embodiment of the present invention, the nerve stimulation means includes stimulation by light pulses, stimulation by audio pulses, stimulation by ultrasonic pulses, or stimulation by electric pulses through electrodes.
In a preferred embodiment of the present invention, the stimulation of the light pulse is to play a visual flicker to the subject through VR glasses.
In a preferred embodiment of the present invention, the stimulation of the audio pulse is to play audio flash to the subject through a speaker.
In a preferred embodiment of the present invention, the preprocessing includes denoising, and processing for filtering electromyographic, electrocardiographic, and electrooculographic signals.
In a preferred embodiment of the invention, the EEG signal acquisition module and the feedback stimulation module are integrated into the design and brain cap.
In a preferred embodiment of the present invention, the nerve stimulation means is transcranial direct current stimulation tDCS with a direct current of 0.8 to 2.5 mA.
In a preferred embodiment of the invention, the present neurostimulation system for real-time modulation based on the theta oscillations of the EEG signal is used for neuromodulation in patients with neurodegenerative diseases.
In a preferred embodiment of the invention, the neurodegenerative disease is selected from multiple sclerosis, including Alzheimer's disease AD, parkinson's disease PD, huntington's disease HD, amyotrophic lateral sclerosis ALS, different types of spinocerebellar ataxia SCA, or pick's dementia.
Compared with the prior art, the invention has the following obvious and prominent substantive characteristics and remarkable advantages:
1. the invention controls the frequency and the mode of the neural stimulation based on the characteristics of the individual electroencephalogram signals of the testee, can eliminate the determination that the existing neural stimulation technology can not customize and provide a stimulation scheme, and overcomes the influence of intrinsic factors and extrinsic factors of the testee;
2. the nerve stimulation based on the phase of Theta wave is beneficial to inducing the long-term enhancement of synaptic plasticity related to memory, and has further enhancement effect on nervous system diseases and mental diseases.
3. The neural oscillation change excited by the neural stimulation of the subject and the coupling of brain waves with different frequencies occur in the central nervous system, the operation mode of the system is not influenced, and the system also eliminates the influence of the coupling between waveforms on the neural electrical stimulation.
4. The system can be simply transformed and implemented on the basis of the existing nerve stimulation system, and is low in cost and easy to realize.
Description of the drawings:
FIG. 1 is a schematic diagram of an EEG signal processing method of the system of the present invention;
FIG. 2 is a schematic diagram of the connection relationship between modules of an embodiment of the system of the present invention;
fig. 3 is an electroencephalogram guidance chart of the electroencephalogram signal acquisition module.
The specific implementation mode is as follows:
the following describes the elements of the present invention. In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 illustrates a pattern diagram of how the system of the present invention initiates a neural stimulation time point based on EEG signal acquisition. The corresponding EEG signal is first recorded by the electroencephalogram recording electrode, as shown in the bottom image of fig. 1. The images in the middle row are displayed, the EEG signal is preprocessed, and the theta phase extraction is carried out on the EEG signal. And extracting the theta frequency band through a filtering algorithm. The theta phase is extracted by performing Hilbert Transform (Hilbert Transform) on the filtered theta band signal. When the hilbert transform is performed, the waveform becomes a saw-tooth-like shape in the top row, and it is very easy to extract the phase. The corresponding electrical stimulation is then performed at a particular theta phase (P1). Each t in fig. 1 1 The points are all time points when the theta wave is positioned on a specific phase, and the time points are the time points when the nerve stimulation is sent. In the invention, P1 is obtained based on a specific calculation formula of the invention content part, and the method can improve the specific effect of the nerve stimulation system by utilizing theta-gamma coupling to the maximum extent. When the real-time signal processing module detects the P1 phase value, the electrical stimulation device is controlled to trigger the electrical nerve stimulation at the corresponding time point, and the optical and sound generation device can be controlled to perform theta phase synchronization visual flicker and sound flicker by using the detected time point in addition to the electrical nerve stimulation performed by using the detection time point. The P1 value determining module is used for obtaining a P1 value; the P1 value acquisition module acquires the collected EEG signal from the EEG signal processing module before starting the neural regulation, or the EEG data in the database is processed to obtain the P1 value, and then the P1 value is regulated according to the intentionAnd carrying out feedback type neural regulation and control on the controlled phase value.
The method of acquiring EEG brain electrical signal data is not different, and a conventional method can be used, and known methods include, for example, the following: the EEG signal data is composed of multi-channel continuous sampling data, the EEG signal data acquired each time is sliced to form a sample, each sample selects 1024 sampling points as the length of the sample, the step length of the sample is 512 sampling points, and each sample contains the sampling data of a plurality of channels. When the electroencephalogram signal is obtained from the electrode, the electroencephalogram signal data can be obtained by utilizing the electrode in the electroencephalogram signal tester by combining the existing equipment, the standard of '10-20 international standard lead system' is used for the position of the electrode point of the electroencephalogram signal, and the specific lead position is shown in figure 3.
The EEG electroencephalogram signal data need to be preprocessed, which are all processed in the EEG signal processing module of the invention, and the specific method is not different from the prior art. Because the EEG signal is essentially the potential difference of each electrode point on the surface of the scalp, the acquired EEG signal data is inaccurate due to external noise, head or eyeball motion. Therefore, artifact removal is required for the electroencephalogram signal data. The preprocessing generally includes denoising, and filtering electromyographic, electrocardio and electrooculogram signals. In particular, the method comprises the steps of,
the method comprises the steps of firstly carrying out band-pass filtering on electroencephalogram signal data, selecting the band-pass filtering frequency to be 1-30Hz, wherein the frequency range is proved to have strong correlation with brain activities in previous researches, and the method of band-pass filtering can remove artifact signals belonging to a high frequency band.
Blinking and eye movement artifacts in the brain electrical signal data are then removed using existing Independent Component Analysis (ICA) methods. The analysis of independent components is subject to blind source signal separation, and is based on the assumption that data collected by a single signal input end is obtained by mixing n independent components, and is expressed by using a mathematical formula as follows:
y j ＝w j1 x 1 +w j2 x 2 +…+w jn x n ,j＝1,2,3…,m (1)
formula (1) can be converted to the following form:
Y COL ＝W MIX X INDEP (2)
fusing multiple signal sources into signal y by linear transformation j Into a linear combination of a plurality of independent source component signals. The formula (2) is a mathematical expression of the independent component analysis hypothesis, wherein X INDEP ＝[x 1 ,x 2 ,…,x n ] T Vector expressions of n-dimensional independent sources representing statistical meaning, and Y COL ＝[y 1 ,y 2 ,…，y m ] T A vector expression representing the collected m-dimensional signal data. W MIX Is a mixing matrix. The independent source signals pass through a mixing matrix W of m x n MIX Mixing is carried out. Independent signal source X in the analysis process INDEP And a mixing matrix W MIX Are unknown, independent component analysis uses random vectors collected to m-dimensional signals as data input, and estimates n-dimensional independent source signals X through an optimization iterative algorithm INDEP 。
An electroencephalogram data sample is decomposed into independent components with the number consistent with the number of electroencephalogram data channels by using an independent component analysis method, and the number of the channels is 30 in the embodiment, so that the electroencephalogram data sample is decomposed into 30 independent components. Firstly, the dimension of the independent component output by the independent component analysis cannot exceed the input dimension, namely the number of the acquired electroencephalogram signal data channels. Secondly, correlation studies show that the higher the dimensionality of the independent component output, the higher the accuracy of the final classification.
After the independent component analysis is completed, according to the correlation levels of each component after calculation and decomposition and the horizontal and vertical ocular electrical signals, the independent component highly correlated with the ocular electrical signals is determined and is taken as ocular artifact to be removed. Mixing X INDEP Changing the value in the vector of the independent source of the medium-sized artifact to 0, and inputting the modified X into a formula (2) to obtain the reconstructed m-dimensional electroencephalogram signal.
The result obtained in the step is electroencephalogram signal data which is provided after band-pass filtering and typical artifact signal removal, and is called the preprocessed electroencephalogram signal data.
Preprocessing of the brain electrical signals can be processed by matlab software.
The preprocessed EEG signal data can be used for extracting theta waves, and the specific method for extracting the theta waves is not different from the prior art. Generally, electroencephalogram data is composed of multi-channel continuous sampling data, for example, in the present invention, EEG electroencephalogram data acquired each time can be sliced to form one sample, each sample selects 1024 sampling points as the length of the sample, the step length of the sample is 512 sampling points, and each sample contains sampling data of multiple channels. The power spectral density of a single channel of a single sample is obtained by using the formula (3) for the single channel of the single sample, and the following four types of frequency indexes are extracted according to the frequency distribution condition of the electroencephalogram signal, wherein the delta is 1-4Hz, the theta is 4-8Hz, the alpha is 8-12Hz, and the beta is 13-30Hz. The set of power spectral density eigenvectors at different frequency bins can be expressed as:
P delta ＝{P req },freq∈delta (4)
P theta ＝{P freq }，freq∈theta (5)
P alpha ＝{P freq },freq∈alpha (6)
P beta ＝{P freq }，freq∈beta (7)
in the above formula: p freq For the calculated discrete power spectral density, P delta 、P theta 、P alpha 、P beta For the collection of power spectral density vectors under different frequency bands, the characteristic dimensions of four frequency bands under a single channel are respectively M fr The value is the number of discrete frequency points k in the corresponding frequency segment fr; fr represents a frequency segment, and takes the value of one of the set { delta, theta, alpha, beta }; the feature dimension values of a certain frequency segment fr in all channels are the same, so that the theta waveform in the EEG signal can be extracted (see FIG. 1), or the phase in the commercial matlab software can be usedAnd (5) processing by the closing module.
The process is completed by the EEG signal processing module in the invention, and the EEG signal processing module can be a general computer with a communication interface so as to be connected with the communication module or an embedded computing device.
The EEG signal processing module further extracts the theta waveform and obtains the phase of the signal through Hilbert Transform (Hilbert Transform). In communication theory and signal processing method, hilbert transform is a tool for analyzing signals, can be used for signal transform and filtering, and is used for obtaining instantaneous amplitude and instantaneous phase of signals. Converting the original theta-band EEG signal obtained by band-pass filtering into an analytic signal in complex form by Hilbert transform y = H (y), wherein z (t) = x (t) + iy (t) = A (t) e iφ(t) Phi (t) is the phase waveform signal of the frequency band. Obtaining a phase time sequence and an amplitude time sequence, namely, obtaining the phase of each time point of the theta waveform
Figure BDA0003748150060000083
The value is obtained.
In the P1 numerical value determining module, the following method is adopted to obtain the adjustment relation between theta waves and gamma waves of the EEG signals:
obtaining a theta wave phase signal phi through Hilbert conversion U (t) and gamma amplitude signal A U (T) the sampling time point is U = { T = k Δ T, k ∈ 1,2, … T }. Δ T is the sampling interval and T is the number of samples. Due to the phase phi U (t) is a periodic signal, ranging from-pi to pi, the phase range S = { m Δ N, m =1,2, … N }, Δ N is the phase interval, N is the number of fractions evenly divided from-pi to pi. Therefore, we can calculate the amplitude signal A U (t) distribution A over different phase ranges S S ＝{A U (phi), phi belongs to s, and As is the amplitude signal A U (t) distribution over the phase range S. To obtain at which phase A S Has a phase-locked relationship, calculates the entropy between phase and amplitude,
Figure BDA0003748150060000081
wherein p is m Is calculated as follows
Figure BDA0003748150060000082
Wherein the content of the first and second substances,<A S >phi (m) represents the average value of the amplitude of the corresponding gamma amplitude when the current theta phase value is m delta n;
by calculating the H value at different phases S = { m Δ N, m =1,2, … N }, there is a phase-locked relationship between the phase of the theta wave and the gamma wave when H takes the maximum value. We take the corresponding phase to get phi max = argmax (H), i.e. at the phase phi max The upper theta wave and the gamma wave have a coupling relation, so that the obtained P1= phi max . Further, the coupling relationship C is calculated as:
Figure BDA0003748150060000091
H max is the maximum value of H, when C takes 0, the coupling relation between the two is not existed, when the value of C is increased, the coupling relation between the two is increased,
and after the control module obtains the value of P1, judging whether to send a stimulation instruction to the feedback stimulation module according to the real-time theta phase data, and when the theta phase is positioned near the value of P1, sending the stimulation instruction to the feedback stimulation module. Usually, when the theta phase is set to be within the range of [ P1- Δ n, P1+ Δ n ], a stimulation instruction is sent to the feedback stimulation module, and the specific range can be set according to actual needs. Here [ P1- Δ n, P1+ Δ n ] is located between- π and π cyclically. The EEG signal processing module sends data to the control module through the communication module, the control module feeds back a stimulation starting signal through the communication module, the signal duration is less than 1ms, and at the moment, the feedback stimulation module stimulates nerves after receiving the starting signal.
The control module is connected with each part to control the operation and cooperation of each module, which can be a general computer with a communication interface, can be combined with the signal processing module, and can also exist independently.
Fig. 2 shows a schematic diagram of the system of the invention when applying transcranial direct current stimulation tDCS, which includes an EEG signal acquisition module, an EEG signal processing module, a control module, a communication module, and a feedback stimulation module. The EEG signal acquisition module and the feedback stimulation module can be combined into a helmet form, and simultaneously, signal acquisition and path direct current stimulation are completed.
The EEG signal acquisition module transmits EEG signals acquired by the EEG signal acquisition module to the EEG signal processing module through the communication module, the EEG signals after pretreatment are subjected to filtering algorithm, theta waves with the phase frequency range of 4-8Hz are extracted, hilbert Transform is performed, the phase of the Theta is taken out, the phase of the Theta of the EEG of a subject is transmitted to the control module through the communication module, the control module judges whether a stimulation instruction is transmitted to the feedback stimulation module or not according to real-time Theta phase data, when the phase of the Theta is equal to a P1 value, the stimulation instruction is transmitted to the feedback stimulation module, the feedback stimulation module receives the stimulation instruction transmitted from the control module through the communication module, cranial direct current stimulation aiming at the subject is started based on the stimulation instruction, and electrical stimulation is implemented through a helmet.
The above P1 is calculated by the above formula, and in one embodiment, a single point value between 130 ° and 145 °, for example, t shown in fig. 1 n The (n =1,2,3,4,5 … …) time point is the time point at which brain stimulation is initiated.
In the principle of tDCS, anode stimulation is taken as a functional representation for enhancing local brain activity, and cathode stimulation is taken as a functional representation for weakening the local brain activity; the inflow point and the outflow point of electricity generation in the helmet can be guided in a net shape and arranged comprehensively.
When the feedback stimulation module is transcranial direct current stimulation tDCS, stimulation is started and painstakingly transmitted to a circuit front-end chip of the feedback stimulation module to generate specific analog signals, the voltage value of the analog signals is preferably in mA level, and the number is 0.5-50mA; in addition, the analog signal requires a regulated current regulation, which is performed on the scalp of the user by matching its input voltage through an impedance recognition system in the circuitry. The feedback stimulation module may also contain an embedded module and perform a steady flow adjustment based on scalp impedance. The conductor is preferably a contact copper wire.
Neuromodulation refers to the use of implantable or non-implantable techniques to modify the activity of the central, peripheral or autonomic nervous system by electrical stimulation or pharmacological means to improve the symptoms of the affected population. In a preferred embodiment of the present invention, the nerve stimulation means is transcranial direct current stimulation tDCS with 0.8 to 2.5mA direct current.
When the stimulation of the invention adopts sound wave and light signals, the feedback stimulation module can be replaced by other devices. In a preferred embodiment of the present invention, the neural stimulation means is further selected from the group consisting of stimulation by light pulses to the subject, stimulation by audio pulses to the subject, and stimulation by ultrasound pulses to the subject. At this point, the tDCS device in fig. 2 may be replaced by playing a visual flash to the subject, for example, through VR glasses, acoustically stimulating the subject through headphones, or stimulating the subject through other acoustic devices, such as an ultrasound device.
The invention controls the frequency and the mode of the neural stimulation based on the characteristics of the individual electroencephalogram signals of the testee, can eliminate the determination that the existing neural stimulation technology can not provide a stimulation scheme in a customized manner, and overcomes the influence of inherent factors and extrinsic factors of the testee; the nerve stimulation based on the phase of Theta wave is beneficial to inducing the long-term enhancement of synaptic plasticity related to memory, and has further enhancement effect on nervous system diseases and mental diseases.
In addition, the neural oscillation change excited by the neural stimulation of the subject and the coupling of brain waves with different frequencies occur in the central nervous system, the operation mode of the system is not influenced, and the system also eliminates the influence of the coupling between waveforms on the neural electrical stimulation. The system can be simply transformed and implemented on the basis of the existing nerve stimulation system, and is low in cost and easy to realize.
In a preferred embodiment of the invention, the present real-time EEG signal theta oscillation based neurostimulation system is used for neuromodulation in patients with neurodegenerative diseases. In a preferred embodiment of the invention, the neurodegenerative disease is selected from the group consisting of multiple sclerosis, including Alzheimer's disease AD, parkinson's disease PD, huntington's disease HD, amyotrophic lateral sclerosis ALS, different types of spinocerebellar ataxia SCA, pick's dementia.
The embodiments of the present invention are not described herein. The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The present invention provides a neurostimulation system for regulating and controlling based on theta oscillations of an EEG signal as described in detail above. The principles and embodiments of the present invention are explained herein using specific examples, which are presented only to assist in understanding the method and its core concepts. It should be noted that, for those skilled in the art, without departing from the principle of the present invention, it is possible to make various improvements and modifications to the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.

Claims (9)

1. A neurostimulation system for real-time modulation based on EEG signal theta oscillations, comprising:
an EEG signal acquisition module, an EEG signal processing module, a control module, a communication module, a feedback stimulation module and a P1 value determination module,
the EEG signal acquisition module comprises an EEG electrode and an amplifier and transmits an EEG signal of a collected subject to the EEG signal processing module through the communication module;
the EEG signal processing module is used for preprocessing the EEG signals acquired by the EEG signal acquisition module, extracting Theta waves with the phase frequency range of 4-8Hz by the preprocessed EEG signals through a filtering algorithm, extracting Theta phases by Hilbert Transform, and transmitting the Theta phases of the electroencephalograms of the testees to the control module through the communication module,
the P1 value determining module is used for obtaining a P1 value; the P1 value acquisition module acquires an acquired or stored EEG signal before starting nerve regulation, a P1 value is obtained after processing, the P1 value is obtained through the regulation relation between the theta wave and the gamma wave of the EEG signal, and the following method is specifically adopted:
obtaining theta wave phase signal phi through Hilbert transform U (t) and gamma wave amplitude signal A U (T) sampling time point is U = { T = k Δ T, k ∈ 1,2, … T }; Δ T is the sampling interval and T is the number of samples due to the phase signal φ U (t) is a periodic signal, ranging from-pi to pi, with a phase range S = { m Δ N, m =1,2, … N }, Δ N being the phase interval, N being from-pi to piThe number of parts to be evenly divided is calculated by Hilbert transform U (t) distribution A over different phase ranges S S ＝{A U (phi), phi belongs to s, and As is the amplitude signal A U (t) distribution over a phase range S, computing the entropy H between phase and amplitude, determining the sum A S Has a phase-locked relationship with:
Figure FDA0003748150050000011
wherein p is m Is calculated as follows
Figure FDA0003748150050000012
Wherein the content of the first and second substances,<A S > φ (m) represents the average value of the amplitude of the corresponding gamma amplitude when the current theta phase value is m delta n;
by calculating the H value at different phases S = { m Δ N, m =1,2, … N }, there is a phase-locked relationship between the phase of the theta wave and the gamma wave when H takes the maximum value. Extracting the corresponding phase phi according to the value of m max ，φ max E.g. S, and phi max = argmax (H), i.e. at the phase phi max The upper theta wave and the gamma wave have a coupling relation, and P1= phi is obtained max ；
The control module obtains a P1 value from the P1 value determining module, judges whether to send a stimulation instruction to the feedback stimulation module according to the real-time theta phase data, sends the stimulation instruction to the feedback stimulation module when the theta phase is within the range of [ P1-delta n, P1+ delta n ],
the communication module is used for transmitting instructions among the EEG signal acquisition module, the EEG signal processing module, the control module, the P1 value determination module and the feedback stimulation module,
a feedback stimulation module which receives the stimulation instruction transmitted from the control module via the communication module, starts the nerve stimulation means for the subject based on the stimulation instruction, and stops the nerve stimulation means when the stimulation instruction transmitted from the control module via the communication module stops;
wherein, P1 is a single-point numerical value selected from 130-145 degrees, and the sending time of the stimulation instruction is less than 1 ms.
2. The EEG signal theta oscillation based real time modulated neurostimulation system of claim 1, said neurostimulation means selected from the group consisting of stimulation of the subject with light pulses, stimulation of the subject with audio pulses, stimulation of the subject with ultrasound pulses, or stimulation of the subject with electrical pulses through the skin or implanted electrodes.
3. The system of claim 1 wherein the neurostimulation means is a means for stimulating the subject with pulses of light to cause visual flicker to be played back to the subject through VR glasses.
4. The EEG signal theta oscillation based real-time modulated neurostimulation system of claim 1, wherein the neurostimulation means is audio pulse stimulation of the subject and audio blinking is played to the subject through a speaker.
5. The EEG signal theta oscillation based real time modulated neurostimulation system of claim 1, wherein said pre-processing includes de-noising, electromyographic, electrocardiographic, and ocular signal filtering processing in an EEG signal processing module.
6. The EEG signal theta oscillation based real-time neuromodulation system as claimed in claim 1, wherein the EEG signal acquisition module and the feedback stimulation module are integrated with the brain cap.
7. The EEG signal theta oscillation based real-time modulated neurostimulation system of claim 1, wherein said neurostimulation means is transcranial DC stimulation tDCS with 0.8-2.5 mA DC.
8. The EEG signal theta oscillation based real-time modulation neurostimulation system according to claim 1, the EEG signal theta oscillation based real-time modulation neurostimulation system of the present invention is used for neuromodulation of patients with neurodegenerative diseases.
9. The neurostimulation system for real-time modulation based on oscillations of the EEG signals theta according to claim 1, the neurodegenerative disease being selected from the group consisting of multiple sclerosis, including alzheimer's disease AD, parkinson's disease PD, huntington's disease HD, amyotrophic lateral sclerosis ALS, different types of spinocerebellar ataxia SCA, pick's dementia.
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* Cited by examiner, † Cited by third party
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CN116492597A (en) * 2023-06-28 2023-07-28 南昌大学第一附属医院 Peripheral-central nerve regulation and control device and storage medium
CN116492597B (en) * 2023-06-28 2023-11-24 南昌大学第一附属医院 Peripheral-central nerve regulation and control device and storage medium

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