阅读说明：本技术 一种脑内部刺激与检测系统及方法 (Brain internal stimulation and detection system and method ) 是由 陈逸伦 曲啸 曹鹏 陈新蕾 于 2021-09-22 设计创作，主要内容包括：本发明涉及一种脑内部刺激与检测系统及方法,属于脑电数据处理技术领域,解决了现有技术中采集的脑电信号准确性差、实时性差的问题。该系统包括处理器以及电极；电极,用于采集目标脑部位置的脑电信号,并传输至处理器；处理器,用于基于脑电信号获取其中的β频带信号,并对β频带信号依次进行希尔伯特变换、滑窗积分获得脑部观测特征值,以及基于脑部观测特征值判断脑部状态是否正常；若断定脑部状态不正常,处理器,还用于控制电极产生相应的刺激信号作用于目标脑部位置。该系统能够准确采集脑电信号,并基于信号频域特征进行判别,准确度高,且数据处理速度快,实时性优异。(The invention relates to a brain internal stimulation and detection system and method, belongs to the technical field of electroencephalogram data processing, and solves the problems of poor accuracy and poor real-time performance of electroencephalogram signals acquired in the prior art. The system includes a processor and an electrode; the electrode is used for collecting electroencephalogram signals of the target brain position and transmitting the electroencephalogram signals to the processor; the processor is used for acquiring a beta frequency band signal based on the electroencephalogram signal, sequentially carrying out Hilbert transform and sliding window integration on the beta frequency band signal to obtain a brain observation characteristic value, and judging whether the brain state is normal or not based on the brain observation characteristic value; if the brain state is judged to be abnormal, the processor is also used for controlling the electrodes to generate corresponding stimulation signals to act on the target brain position. The system can accurately acquire the electroencephalogram signals and judge the electroencephalogram signals based on the signal frequency domain characteristics, and is high in accuracy, high in data processing speed and excellent in real-time performance.)

1. An intracerebral stimulation and detection system, which is characterized by comprising a processor and electrodes;

the electrode is used for collecting electroencephalogram signals of the target brain position and transmitting the electroencephalogram signals to the processor;

the processor is used for acquiring a beta frequency band signal based on the electroencephalogram signal, sequentially carrying out Hilbert transform and sliding window integration on the beta frequency band signal to obtain a brain observation characteristic value, and judging whether the brain state is normal or not based on the brain observation characteristic value;

and if the brain state is judged to be abnormal, the processor is also used for controlling the electrodes to generate corresponding stimulation signals to act on the target brain position.

2. The intracerebral stimulation and detection system according to claim 1, wherein the processor is connected with the electrodes through leads, and the processor end is provided with an analog switch for controlling the working mode of the electrodes by selecting the output mode of the processor;

when the analog switch is switched on, the output mode of the processor is an analog-to-digital conversion mode, and the working mode of the electrode is a reading mode; when the analog switch is switched off, the output mode of the processor is a digital-to-analog conversion mode, and the working mode of the electrode is a stimulation mode.

3. The intracerebral stimulation and detection system according to claim 1 or 2, wherein the processor is configured to acquire the β -band signal based on the electroencephalogram signal by:

periodically sampling an electroencephalogram signal to obtain a first time domain local field potential sequence;

performing fast Fourier transform on the first time domain local field potential sequence by using a time extraction method at preset time intervals to obtain a frequency domain local field potential sequence;

and filtering the frequency domain local field potential sequence to obtain a beta frequency domain local field potential sequence, namely the beta frequency band signal.

4. The brain internal stimulation and detection system according to claim 3, wherein the step of obtaining a β -band signal based on the electroencephalogram signal, and sequentially performing hilbert transform and sliding window integration on the β -band signal to obtain a brain observation characteristic value comprises:

performing fast Fourier inverse transformation on the beta frequency domain local field potential sequence to obtain a second time domain local field potential sequence;

performing Hilbert transform on the second time domain local field potential sequence to obtain a third time domain local field potential sequence which is shifted by 90 degrees;

constructing an analytic signal based on the second time domain local field electric potential sequence and the third time domain local field electric potential sequence, and performing modulus extraction on the analytic signal;

performing sliding window internal integration on the module value of the analytic signal by adopting a sliding window with a preset size and a preset step length to obtain a brain observation value sequence; the width of the sliding window is the same as the preset step size;

and carrying out exponential decay weighted average on the brain observation value sequence to obtain a brain observation characteristic value.

5. The intracerebral stimulation and detection system according to claim 3, wherein the obtaining of the frequency-domain local field potential sequence by performing a fast Fourier transform on the first time-domain local field potential sequence by using a time extraction method comprises:

representing a frequency domain local field potential sequence by the first time domain local field potential sequence by using a fast Fourier transform through the following formula:

wherein k represents a frequency domain component, dt (t) represents a local field potential value with sequence number t in the first time domain local field potential sequence,representing a frequency domain local field potential sequence, N representing the number of local field potential values in the first time domain local field potential sequence;

and solving the frequency domain local field potential sequence by using a time extraction method through the following formula:

wherein, Dt (omega)_2lDenotes the value of the local field potential with the sequence number 2l in the sequence of the frequency domain local field potentials, Dt (omega)_2l+1And the sequence number of the local field potential value in the frequency domain local field potential sequence is 2l + 1.

6. The intracerebral stimulation and detection system according to claim 4, wherein the beta frequency domain local field potential sequence is inverse fast Fourier transformed to obtain a second time domain local field potential sequence by the following formula:

where k denotes a frequency domain component, dt (t)' denotes a second time-domain local field potential sequence,representing a frequency domain local field potential sequence, N representing the number of local field potential values in the first time domain local field potential sequence;

performing Hilbert transform on the second time domain local field potential sequence to obtain a third time domain local field potential sequence which is shifted by 90 degrees and is:

wherein the content of the first and second substances,and representing a third time domain local field electric potential sequence, and Dt (tau)' representing a local field electric potential value with a sequence number tau in the second time domain local field electric potential sequence.

7. The intracerebral stimulation and detection system of claim 6,

constructing an analytic signal based on the second time domain local field electric potential sequence and the third time domain local field electric potential sequence as follows:

the modulus of the analytic signal is:

here, Ht represents an analysis signal.

8. The intracerebral stimulation and detection system according to claim 7, wherein the sequence of brain observation values is obtained by integrating the mode values of the analytic signals with a preset step length by using a sliding window with a preset size through the following formula:

wherein the content of the first and second substances,represents the integral value in the mth sliding window at the time T, P is a preset step length,the integral values in the sliding windows form an observed value sequence of the T time;

and the observation value sequences corresponding to all the moments in the preset time period are sequentially connected to form a brain observation value sequence.

9. The system according to claim 7, wherein if the observed brain characteristic value calculated by the processor is greater than or equal to a preset threshold, the processor controls the electrodes to generate corresponding stimulation signals to act on the target brain position;

and if the calculated brain observation characteristic value is continuously smaller than the preset threshold value in the time period [2h,3h ], controlling the alarm equipment to send out a danger alarm by the processor.

10. A brain internal stimulation and detection method is characterized by comprising the following steps;

acquiring an electroencephalogram signal of a target brain position by using an electrode;

acquiring a beta frequency band signal based on the electroencephalogram signal, sequentially carrying out Hilbert transform and sliding window integration on the beta frequency band signal to obtain a brain observation characteristic value, and judging whether the brain state is normal or not based on the brain observation characteristic value;

and if the brain state is judged to be abnormal, controlling the electrodes to generate corresponding stimulation signals to act on the target brain position.

Technical Field

The invention relates to the technical field of electroencephalogram data processing, in particular to a brain internal stimulation and detection system and method.

Background

Parkinson's disease is a common neurodegenerative disease, and according to the investigation of the world health organization, about 400 million Parkinson's disease patients exist in the world, wherein the number of Chinese patients exceeds 200 million. Parkinson's disease is characterized by pathological motor symptoms including tremor, rigidity, bradykinesia, and postural instability. Among them, some pathological symptoms of parkinson's disease are caused by dysfunction of the brain region of the brain colliculus. With the development of sensor technology, the performance of medical instruments is gradually intelligentized, and the medical instruments are applied to clinical operations and treatments of neurological diseases. The deep brain stimulation technology is a technology that an electrode is implanted into the brain of a patient through a surgical operation, disease prediction and abnormal signal identification are carried out through acquiring intracranial electroencephalogram data, and a corresponding electric pulse sequence is released to a deep brain structure so as to achieve the purpose of treatment.

The local field potential technology is a reliable electroencephalogram signal detection method and is used for displaying integrated analog form synaptic signals input to cortex. Oscillations are classified by the range of the dominant frequency band: delta- (1-4Hz), theta- (4-10Hz), alpha- (10-14Hz), beta- (14-36Hz), gamma- (36-100Hz), etc. Different frequency bands play different roles in brain memory and synchronization of neural activity. Coupling effect exists between local field potentials of different frequency bands, which influences pathological states of Parkinson's disease patients. As local field potential data accumulates, a number of biomarkers for closed-loop neuromodulation have been proposed. Recent studies have shown that beta concussion of the basal nucleus is closely related to the severity of parkinson's disease symptoms. When dopamine is depleted, energy increases; and when the patient exercises or uses deep brain stimulation, the energy decreases. Deep brain stimulation technology has been proven to be an effective method for treating dyskinesia diseases such as Parkinson's disease, and deep brain stimulation aiming at subthalamic nucleus and globus pallidus inner part is an effective intervention measure for improving the functional performance of patients with advanced Parkinson's disease. The stimulation device modulates the activity of certain nuclei via implanted electrodes, thereby destroying the pathological oscillating ganglion rings of the corresponding wave bands within the cortical basal lamina.

In the prior art, on one hand, one electrode is usually adopted for acquiring electroencephalogram signals, and the other electrode is adopted for generating corresponding stimulation signals to act on a target brain position; in addition, in the prior art, whether the brain state is normal is judged only based on the time domain characteristics of the acquired electroencephalogram signals; on the other hand, the brain state is usually determined by analyzing the energy spectrum of the beta band of the electroencephalogram signal.

In the prior art, at least the following defects exist, firstly, the stimulating electrode is at the target point, the signal collecting electrode is under the cortex, and the two electrodes cannot be simultaneously positioned at the target point, so that the signals collected by the signal collecting electrode have larger noise and errors, and even if the two electrodes are simultaneously positioned at the target point, the stimulating signal generated by one electrode can greatly interfere the electroencephalogram signal collected by the other electrode, and the accuracy of the collected electroencephalogram signal is difficult to ensure; secondly, whether the brain state is normal or not is judged only based on the time domain characteristics of the acquired electroencephalogram signals, the frequency domain characteristics in the electroencephalogram signals are ignored, and the accuracy of brain state judgment is reduced; thirdly, the brain state can be accurately judged by analyzing the energy spectrum of the beta frequency band of the electroencephalogram signal, but the requirement of timeliness cannot be met in practical application.

Disclosure of Invention

In view of the above analysis, embodiments of the present invention provide a system and a method for stimulating and detecting brain interior, so as to solve the problem of inaccurate brain interior detection result due to poor accuracy and poor real-time of the electroencephalogram signal acquired in the prior art.

In one aspect, the present invention provides an intracerebral stimulation and detection system, including a processor and electrodes;

the electrode is used for collecting electroencephalogram signals of the target brain position and transmitting the electroencephalogram signals to the processor;

the processor is used for acquiring a beta frequency band signal based on the electroencephalogram signal, sequentially carrying out Hilbert transform and sliding window integration on the beta frequency band signal to obtain a brain observation characteristic value, and judging whether the brain state is normal or not based on the brain observation characteristic value;

and if the brain state is judged to be abnormal, the processor is also used for controlling the electrodes to generate corresponding stimulation signals to act on the target brain position.

Furthermore, the processor is connected with the electrode through a wire, and an analog switch is arranged at the processor end and used for controlling the working mode of the electrode by selecting the output mode of the processor;

when the analog switch is switched on, the output mode of the processor is an analog-to-digital conversion mode, and the working mode of the electrode is a reading mode; when the analog switch is switched off, the output mode of the processor is a digital-to-analog conversion mode, and the working mode of the electrode is a stimulation mode.

Further, the processor acquires a β -band signal based on the electroencephalogram signal by:

periodically sampling an electroencephalogram signal to obtain a first time domain local field potential sequence;

performing fast Fourier transform on the first time domain local field potential sequence by using a time extraction method at preset time intervals to obtain a frequency domain local field potential sequence;

and filtering the frequency domain local field potential sequence to obtain a beta frequency domain local field potential sequence, namely the beta frequency band signal.

Further, the step of obtaining a beta band signal based on the electroencephalogram signal, and sequentially performing hilbert transform and sliding window integration on the beta band signal to obtain a brain observation characteristic value comprises:

performing fast Fourier inverse transformation on the beta frequency domain local field potential sequence to obtain a second time domain local field potential sequence;

performing Hilbert transform on the second time domain local field potential sequence to obtain a third time domain local field potential sequence which is shifted by 90 degrees;

constructing an analytic signal based on the second time domain local field electric potential sequence and a third time domain local field electric potential sequence, and performing modulus operation on the analytic signal;

performing sliding window internal integration on the module value of the analytic signal by adopting a sliding window with a preset size and a preset step length to obtain a brain observation value sequence; the width of the sliding window is the same as the preset step size;

and carrying out exponential decay weighted average on the brain observation value sequence to obtain a brain observation characteristic value.

Further, the obtaining of the frequency domain local field potential sequence by performing fast fourier transform on the first time domain local field potential sequence by using a time extraction method includes:

representing a frequency domain local field potential sequence by the first time domain local field potential sequence by using a fast Fourier transform through the following formula:

wherein k represents a frequency domain component, dt (t) represents a local field potential value with sequence number t in the first time domain local field potential sequence,representing a frequency domain local field potential sequence, N representing the number of local field potential values in the first time domain local field potential sequence;

and solving the frequency domain local field potential sequence by using a time extraction method through the following formula:

wherein, Dt (omega)_2lDenotes the value of the local field potential with the sequence number 2l in the sequence of the frequency domain local field potentials, Dt (omega)_2l+1And the sequence number of the local field potential value in the frequency domain local field potential sequence is 2l + 1.

Further, performing inverse fast fourier transform on the beta frequency domain local field potential sequence to obtain a second time domain local field potential sequence by the following formula:

wherein k represents a frequency domain component, and dt (t)' represents a second timeA sequence of field local field potentials,representing a frequency domain local field potential sequence, N representing the number of local field potential values in the first time domain local field potential sequence;

performing Hilbert transform on the second time domain local field potential sequence to obtain a third time domain local field potential sequence which is shifted by 90 degrees and is:

wherein the content of the first and second substances,and representing a third time domain local field electric potential sequence, and Dt (tau)' representing a local field electric potential value with a sequence number tau in the second time domain local field electric potential sequence.

Further, constructing an analytic signal based on the second time-domain local field electric potential sequence and the third time-domain local field electric potential sequence as follows:

the modulus of the analytic signal is:

here, Ht represents an analysis signal.

Further, a sliding window with a preset size is adopted to perform sliding window internal integration on the module value of the analytic signal by a preset step length through the following formula to obtain a brain observation value sequence, which specifically comprises the following steps:

wherein the content of the first and second substances,represents the integral value in the mth sliding window at the time T, P is a preset step length,the integral values in the sliding windows form an observed value sequence of the T time;

and the observation value sequences corresponding to all the moments in the preset time period are sequentially connected to form a brain observation value sequence.

Further, if the observed brain characteristic value calculated by the processor is greater than or equal to a preset threshold value, the processor controls the electrodes to generate corresponding stimulation signals to act on the target brain position;

and if the calculated brain observation characteristic value is continuously smaller than the preset threshold value in the time period [2h,3h ], controlling the alarm equipment to send out a danger alarm by the processor.

In another aspect, the present invention provides a method for stimulating and detecting intracerebral regions, comprising;

acquiring an electroencephalogram signal of a target brain position by using an electrode;

acquiring a beta frequency band signal based on the electroencephalogram signal, sequentially carrying out Hilbert transform and sliding window integration on the beta frequency band signal to obtain a brain observation characteristic value, and judging whether the brain state is normal or not based on the brain observation characteristic value;

and if the brain state is judged to be abnormal, controlling the electrodes to generate corresponding stimulation signals to act on the target brain position.

Compared with the prior art, the invention can realize at least one of the following beneficial effects:

1. according to the system and the method for stimulating and detecting the brain interior, firstly, one electrode is arranged at a target brain position, namely a target point, the working mode of the electrode is controlled through the analog switch, so that the same electrode is subjected to signal acquisition and stimulation in a time-sharing multiplexing mode, the accuracy of signal acquisition and the accuracy of target point stimulation can be improved, and the defects of large signal acquisition noise, large error and inaccurate stimulation position caused by the fact that two electrodes are adopted for signal acquisition and target point stimulation respectively in the prior art are overcome.

2. According to the brain internal stimulation and detection system and method provided by the invention, the time domain and frequency domain conversion is carried out on the collected electroencephalogram signals, so that the brain observation characteristic value for judging the brain state contains the time domain characteristic and the frequency domain characteristic of the electroencephalogram signals, and further, whether the brain state is normal can be judged more accurately, and the problem that in the prior art, the accuracy of judging the brain state based on the time domain characteristic of the electroencephalogram signals is poor is solved.

3. Compared with a discrete Fourier transform processing method in the prior art, the brain internal stimulation and detection system and method provided by the invention have the advantages that the electroencephalogram signals are processed by adopting the fast Fourier transform, the finer time resolution is provided, and the accuracy and the real-time performance of signal processing are improved. In addition, data overfitting can be prevented through Hilbert transformation, so that a more accurate phase-shifted local field potential sequence is provided, and the accuracy of a brain state judgment result is improved.

In the invention, the technical schemes can be combined with each other to realize more preferable combination schemes. Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.

Drawings

The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, wherein like reference numerals are used to designate like parts throughout.

FIG. 1 is a schematic diagram of an intracerebral stimulation and detection system according to an embodiment of the present invention;

FIG. 2 is a flowchart of a method for stimulating and detecting brain tissue according to an embodiment of the present invention.

Reference numerals:

1-a processor; 2-a wire; 3-electrodes.

Detailed Description

The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not to limit the scope of the invention.

The invention discloses a brain internal stimulation and detection system. As shown in fig. 1, the system includes a processor 1, electrodes 3, and leads 2. Preferably, the processor is a microchip, and the processor is connected to the electrodes through wires, in practical applications, the processor, the wires and the electrodes are disposed inside the brain, and the electrodes are disposed at the target brain position, for example, the electrodes may be disposed at the subthalamic nucleus position or the inner nuclei of the globus pallidus.

Preferably, the electrodes are used for collecting brain electrical signals of the target brain position and transmitting the brain electrical signals to the processor through a lead.

And the processor is used for acquiring the beta frequency band signal based on the electroencephalogram signal, sequentially carrying out Hilbert transform and sliding window integration on the beta frequency band signal to obtain a brain observation characteristic value, and judging whether the brain state is normal or not based on the brain observation characteristic value.

Preferably, the system further comprises an external display device and an alarm device, the external display device and the alarm device are in communication connection with the processor in a wireless communication mode, and the external display device can display the brain signals collected by the electrodes and the brain observation characteristic values obtained by processing the brain signals by the processor. In addition, if the brain observation characteristic value is greater than or equal to the preset threshold value, the brain state is judged to be abnormal, and at the moment, the processor is also used for controlling the electrodes to generate corresponding stimulation signals to act on the target brain position; and if the brain observation characteristic value is continuously smaller than the preset threshold value in the time period [2h,3h ], the processor controls the alarm equipment to send out a danger alarm so as to avoid that the patient is in a false negative state and cannot find out that the danger is caused in time. Because the attention point of the medical staff or the patient on the electroencephalogram signal is mainly whether the signal is abnormal or not, the condition of false negative is easily ignored after a long time, and although the expression of the intracranial electroencephalogram signal is normal under the condition, the false judgment may exist actually, so that the medical staff or the patient can be reminded in the warning mode to carry out comprehensive analysis and judgment by combining with other sensor information at the same time at the current time so as to reduce the condition of false negative.

Preferably, the processor side is provided with an analog switch for controlling the operation mode of the electrode by selecting the output mode of the processor. Specifically, when the brain observation characteristic value is smaller than a preset threshold value, the analog switch is switched to an on state, at this time, the output mode of the processor is an analog-to-digital conversion mode, the working mode of the electrode is a reading mode, and therefore electroencephalogram signals of the target brain position are acquired; when the observed brain characteristic value is greater than or equal to the preset threshold value, the analog switch is switched to an off state, at the moment, the output mode of the processor is a digital-to-analog conversion mode, the working mode of the electrodes is a stimulation mode, and corresponding stimulation signals are generated to act on the target brain position.

Preferably, the system further comprises a memory, and when the mode of the electrode is changed from the stimulation mode to the reading mode, the processor sets the local field potential sequence stored in the memory in a near-term mode to zero, and does not perform secondary stimulation within a certain time period so as to avoid ineffective stimulation.

Preferably, the processor acquires the beta band signal based on the electroencephalogram signal by:

step 1, periodically sampling an electroencephalogram signal to obtain a first time domain local field potential sequence. Preferably, the sampling period is in a range of [4ms,6ms]Preferably, the sampling period is 5 ms. Considering that the electroencephalogram abnormity can be judged only based on the electroencephalogram abnormity in a certain time period in the detection of the electroencephalogram abnormity, the first time domain local field potential sequence at the current time is updated to obtain a plurality of first time domain local field potential sequences corresponding to a plurality of times in a preset time period, and the brain state is judged based on the plurality of first time domain local field potential sequences. Illustratively, the first time-domain local field potential sequence corresponding to the T-1 moment is { d0, d 1., d (N-1) }, and after updating, the first time-domain local field potential sequence corresponding to the T moment is { d1, d 2., dN }, wherein N represents the first time-domain local field potential sequence, and N represents the second time-domain local field potential sequence corresponding to the T momentThe number of local field potential values in the time-domain local field potential sequence, N being 2ⁿAnd n represents a positive integer.

And 2, considering that the electroencephalogram signal cannot be effectively represented by the single time domain characteristic, so that the accuracy of judging the brain state is low, the method also considers the frequency domain characteristic of the electroencephalogram signal, so as to better represent the characteristic of the electroencephalogram signal and improve the accuracy of the brain state judgment result. Specifically, the first time domain local field potential sequence is subjected to fast Fourier transform by using a time extraction method at preset time intervals to obtain a frequency domain local field potential sequence. Preferably, the preset time interval is a times of the sampling period, preferably, a has a value range of [4,6], preferably, a is 6. The method specifically comprises the following steps:

and step 21, representing the frequency domain local field potential sequence by using the first time domain local field potential sequence through the following formula by using fast Fourier transform:

wherein k represents a frequency domain component, dt (t) represents a local field potential value with sequence number t in the first time domain local field potential sequence,representing a frequency domain local field potential sequence and N representing the number of local field potential values in the first time domain local field potential sequence.

Step 22, solving the frequency domain local field potential sequence by a time extraction method through the following formula by using a butterfly algorithm:

wherein, Dt (omega)_2lDenotes the value of the local field potential with the sequence number 2l in the sequence of the frequency domain local field potentials, Dt (omega)_2l+1And the sequence number of the local field potential value in the frequency domain local field potential sequence is 2l + 1.

Specifically, N is 2ⁿThe local field potential sequence of the filter needs n-level butterfly operation, and in order to ensure the precision of subsequent filtering frequency and prevent the complexity of diffusion from being too high, the value range of n is preferably [6,9 ]]. Preferably, in the butterfly operation, the operation result of each stage is stored in the same group of memories, and the ordinal number of the operation is represented by binary, and the sequence at this time is just the sequence of inversion of the code bit of the frequency domain sequence, so that the decoding can be carried out to obtain the frequency domain local field electric potential sequence.

Step 23, filtering the frequency domain local field potential sequence to obtain a β frequency domain local field potential sequence, that is, the β frequency band signal, which is specifically represented as:

wherein, ω is_β-、ω_β+Respectively representing the lower and upper limits of the beta band.

Preferably, the step of obtaining the beta band signal based on the electroencephalogram signal, and sequentially performing hilbert transform and sliding window integration on the beta band signal to obtain the brain observation characteristic value comprises:

s1, performing inverse fast Fourier transform on the beta frequency domain local field potential sequence to obtain a second time domain local field potential sequence, which specifically comprises the following steps:

and performing fast Fourier inverse transformation on the beta frequency domain local field potential sequence to obtain a second time domain local field potential sequence by the following formula:

where k denotes a frequency domain component, dt (t)' denotes a second time-domain local field potential sequence,representing a frequency domain local field potential sequence and N representing the number of local field potential values in the first time domain local field potential sequence.

S2, performing hilbert transform on the second time domain local field potential sequence to obtain a third time domain local field potential sequence which is phase-shifted by 90 °, and specifically includes:

wherein the content of the first and second substances,and representing a third time domain local field electric potential sequence, and Dt (tau)' representing a local field electric potential value with a sequence number tau in the second time domain local field electric potential sequence.

S3, constructing an analytic signal based on the second time domain local field electric potential sequence and the third time domain local field electric potential sequence, and performing modulus selection on the analytic signal, wherein the method specifically comprises the following steps of taking the second time domain local field electric potential sequence as a real part of the analytic signal, taking the phase-shifted third time domain local field electric potential sequence as an imaginary part of the analytic signal, and constructing the analytic signal as follows:

the analytic signal is modulo:

here, Ht represents an analysis signal.

And S4, performing sliding window internal integration on the module value of the analytic signal by adopting a sliding window with a preset size and a preset step length to obtain a brain observation value sequence. Preferably, the width of the sliding window is the same as the preset step size. Preferably, the width of the sliding window and the preset step length areThe method specifically comprises the following steps: in order to reduce the computational complexity, the module value of the analytic signal is divided into a plurality of windows with the length of a through a sliding window, and the envelope of each window is takenModulo summation is carried out to obtain the integral value of each window, and the specific formula is as follows:

wherein the content of the first and second substances,represents the integral value in the mth sliding window at the time T, P is a preset step length,and the integral values in the sliding windows form an observed value sequence of the T time.

And the observation value sequences corresponding to all the moments in the preset time period are sequentially connected to form a brain observation value sequence So (t).

Preferably, since Hilbert transform (Hilbert transform) causes distortion of signals at both ends of a time series due to discontinuity, the present invention constructs a window-by-window brain observation value sequence So (t) by discarding windows at both ends and using a window of a history signal corresponding to time as a reference value. Time sequence S formed by windows at current T moment^T＝{S₁ ^T,S₂ ^T,...,S_N/P ^TAnd the time series S formed by the windows at the previous moment (T-1)^T-1＝{S₁ ^T-1,S₂ ^T-1,...,S_N/P ^T-1In which S is_(N-a)/P ^T(i.e., S)₂ ^T) And S_N/P ^T-1The time of characterization is the same, S₁ ^TAnd S_N/P-1 ^T-1The time of characterization is the same, but S is due to the marginal effect of the Hilbert transform (Hilbert transform) at T-1 and T_N/P ^T-1And S₁ ^TThe distortion of the signal (S) needs to be eliminated, So the sequence connection corresponding to the continuous time within So (t) is expressed as {_N/P-2 ^T-1,S_N/P-1 ^T-1,S₂ ^T-1,...,S_N/P ^T-1}。

And S5, carrying out exponential decay weighted average on the brain observation value sequence to obtain a brain observation characteristic value. Specifically, the brain observation characteristic value is calculated and obtained through an optimized neural network model according to the following formula:

wherein So represents a brain observation characteristic value, L represents the length of a brain observation value sequence So (t), namely the number of observation values, xi represents an attenuation coefficient, and xi is equal to (0, 1).

In another embodiment of the invention, a method for stimulating and detecting the interior of a brain is disclosed. Since the method embodiment and the system embodiment are based on the same working principle, the system embodiment may be referred to for repeated points, and will not be described herein again.

Specifically, as shown in fig. 2, the method includes;

and S110, acquiring an electroencephalogram signal of the target brain position by using the electrodes.

S120, acquiring a beta frequency band signal based on the electroencephalogram signal, sequentially carrying out Hilbert transform and sliding window integration on the beta frequency band signal to obtain a brain observation characteristic value, and judging whether the brain state is normal or not based on the brain observation characteristic value.

And S130, if the brain state is judged to be abnormal, the control electrode generates a corresponding stimulation signal to act on the target brain position.

Compared with the prior art, the system and the method for stimulating and detecting the brain interior disclosed by the embodiment of the invention have the advantages that firstly, one electrode is arranged at the position of a target brain, namely a target point, the working mode of the electrode is controlled through the analog switch, so that the same electrode is subjected to signal acquisition and stimulation in a time-sharing multiplexing mode, the accuracy of signal acquisition and the accuracy of target point stimulation can be improved, and the defects of large signal acquisition noise, large error and inaccurate stimulation position caused by the fact that two electrodes are adopted for signal acquisition and target point stimulation respectively in the prior art are overcome. Secondly, the brain internal stimulation and detection system and method provided by the invention convert the acquired electroencephalogram signals in time domain and frequency domain, so that the brain observation characteristic value for judging the brain state contains the time domain characteristics and frequency domain characteristics of the electroencephalogram signals, and further, whether the brain state is normal can be judged more accurately, and the problem of poor accuracy of judging the brain state only based on the time domain characteristics of the electroencephalogram signals in the prior art is avoided. Finally, compared with the discrete Fourier transform processing method in the prior art, the brain internal stimulation and detection system and method provided by the invention have the advantages that the electroencephalogram signals are processed by adopting the fast Fourier transform, the finer time resolution is provided, and the accuracy and the real-time performance of signal processing are improved. In addition, data overfitting can be prevented through Hilbert transformation, so that a more accurate phase-shifted local field potential sequence is provided, and the accuracy of a brain state judgment result is improved.

Those skilled in the art will appreciate that all or part of the flow of the method implementing the above embodiments may be implemented by a computer program, which is stored in a computer readable storage medium, to instruct related hardware. The computer readable storage medium is a magnetic disk, an optical disk, a read-only memory or a random access memory.

The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.