CN113967023B - Closed-loop optogenetic intervention system and intervention method - Google Patents
Closed-loop optogenetic intervention system and intervention method Download PDFInfo
- Publication number
- CN113967023B CN113967023B CN202111254993.1A CN202111254993A CN113967023B CN 113967023 B CN113967023 B CN 113967023B CN 202111254993 A CN202111254993 A CN 202111254993A CN 113967023 B CN113967023 B CN 113967023B
- Authority
- CN
- China
- Prior art keywords
- module
- signals
- electrodes
- brain
- optogenetic
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/388—Nerve conduction study, e.g. detecting action potential of peripheral nerves
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/25—Bioelectric electrodes therefor
- A61B5/279—Bioelectric electrodes therefor specially adapted for particular uses
- A61B5/291—Bioelectric electrodes therefor specially adapted for particular uses for electroencephalography [EEG]
- A61B5/293—Invasive
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/25—Bioelectric electrodes therefor
- A61B5/279—Bioelectric electrodes therefor specially adapted for particular uses
- A61B5/294—Bioelectric electrodes therefor specially adapted for particular uses for nerve conduction study [NCS]
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/369—Electroencephalography [EEG]
- A61B5/377—Electroencephalography [EEG] using evoked responses
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/369—Electroencephalography [EEG]
- A61B5/386—Accessories or supplementary instruments therefor
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/48—Other medical applications
- A61B5/4836—Diagnosis combined with treatment in closed-loop systems or methods
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6846—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be brought in contact with an internal body part, i.e. invasive
- A61B5/6867—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be brought in contact with an internal body part, i.e. invasive specially adapted to be attached or implanted in a specific body part
- A61B5/6868—Brain
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
- A61N5/00—Radiation therapy
- A61N5/06—Radiation therapy using light
- A61N5/0601—Apparatus for use inside the body
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
- A61N5/00—Radiation therapy
- A61N5/06—Radiation therapy using light
- A61N5/0613—Apparatus adapted for a specific treatment
- A61N5/0622—Optical stimulation for exciting neural tissue
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B2503/00—Evaluating a particular growth phase or type of persons or animals
- A61B2503/40—Animals
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B2503/00—Evaluating a particular growth phase or type of persons or animals
- A61B2503/42—Evaluating a particular growth phase or type of persons or animals for laboratory research
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
- Y02A40/70—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in livestock or poultry
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Biomedical Technology (AREA)
- General Health & Medical Sciences (AREA)
- Animal Behavior & Ethology (AREA)
- Public Health (AREA)
- Veterinary Medicine (AREA)
- Pathology (AREA)
- Biophysics (AREA)
- Heart & Thoracic Surgery (AREA)
- Medical Informatics (AREA)
- Molecular Biology (AREA)
- Surgery (AREA)
- Physics & Mathematics (AREA)
- Psychiatry (AREA)
- Neurology (AREA)
- Neurosurgery (AREA)
- Psychology (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physiology (AREA)
- Signal Processing (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Radiology & Medical Imaging (AREA)
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
The application discloses a closed-loop optogenetic intervention system and an intervention method, wherein the system comprises the following steps: a plurality of electrodes for implantation within the brain of an animal; the biological data recording device is in signal communication with the electrodes and collects signals of a plurality of electrodes; the pulse stimulator is connected with the animal brain through an optical fiber and is used for carrying out optical stimulation on the animal brain; and a controller in signal connection with the biological data recording device and the pulse stimulator and configured to: and reading and processing the signals of the biological data recording device, and outputting corresponding signals to the pulse stimulator after the optogenetic triggering condition is reached so as to perform optical stimulation on the brain of the animal. The system can analyze the LFP activities of a plurality of recording electrodes in real time, and realize closed-loop stimulation taking the functional connection between brains as a trigger condition.
Description
Technical Field
The application relates to the field of optogenetics, in particular to a closed-loop optogenetics intervention system and an intervention method.
Background
The closed-loop control triggered by the nerve electric signal is a strategy for adjusting external intervention in real time according to the electric signal of the organism, and is an effective method for researching nerve loop dynamics. The open loop control is to perform corresponding processing according to preset control input, while the closed loop control is to adjust the control input in the system according to the difference between the output and the expected target, and the control input needs to be adjusted in real time according to the state of the system.
In neuroscience research, this process may be summarized as comparing signals recorded in the living body, such as behavioral parameters, electrical signals, or imaging signals, with target signals, the difference results being input to a specific controller for processing, and the controller performing intervention of neural activity on the living body input stimulation signals. In practice, the connection of the neural loop often has directionality, for example, under a certain behavioral paradigm, there is a functional connection between the a brain region and the B brain region, but when the brain functions, the a brain region can only send instructions to the B brain region. Such directional connections are often not available from direct measurements. Intervention of neural activity in individual brain regions by closed loop control is an effective way to examine causal relationships between functional connections and behaviours between brain regions.
Deep brain stimulation (deep brain stimulation, DBS) has been used clinically for decades to treat dyskinesias in the closed-loop electrical stimulation direction. The study on closed-loop DBS can be traced to 2001 at the earliest, osorio et al designed a closed-loop control system for stimulating by detecting epileptic seizures through electroencephalogram signals, and the effectiveness of closed-loop was largely studied and confirmed in the period of 2002-2005. These treatments produced positive effects on 11 out of 27 epileptic patients with fewer side effects on the brain than the open-loop approach. Rosin et al clinically performed closed-loop DBS treatment of patients with Parkinson's Disease (PD) in the hypothalamus (subthalamic nucleus, STN) and triggered electrical stimulation by real-time detection of the envelope of beta rhythms (12-35 Hz). In 2013, little et al showed that the motor scores of eight PD patients were 27% (p=0.005) to 29% (p=0.03) higher, respectively, during closed-loop DBS than in open-loop. In addition to these therapeutic benefits, the closed-loop stimulation time is reduced by 56% compared to open-loop DBS, reducing energy requirements.
Although several years have passed since the beginning of the use of electrical signals or behaviours to trigger optogenetic stimulation, only a few papers have achieved feedback control in this way. This is because the combination of closed loop control theory and neuroscience is highly interdisciplinary. All closed loop systems require a real-time processing component that can detect the occurrence of a target transient event in time and respond within a defined time. Solutions based on integrated circuits, programmable gate arrays (FPGAs), digital Signal Processors (DSPs) or other custom hardware are well suited for real-time processing, meeting stringent real-time requirements. However, such custom hardware systems are difficult to verify in a cross-laboratory environment, and suffer from the following drawbacks:
1. there is currently no method to implement closed loop intervention based on functional connections. Neuronal activity in the individual brain regions of the hippocampus and cortex is an effective way to examine causal relationships between functional connections and behaviours between the brain regions by closed-loop optogenetic intervention.
2. The detection methods are determined when the program is burnt on the main board, and cannot be adjusted in real time. Some laboratories have developed hardware customization systems for simultaneously achieving electrical signal recording and closed loop optogenetic stimulation in a closed loop system. Although many of these settings are quite elaborate, their implementation in other laboratories is not as easy as the control system typically requires experienced technicians to assemble the required electronics and debug other necessary hardware and software.
Disclosure of Invention
The application aims to provide a closed-loop optogenetic intervention system and an intervention method, which are used for solving the problems in the prior art.
To solve the above problems, according to one aspect of the present application, there is provided a closed loop optogenetic intervention system, the system comprising:
a plurality of electrodes for implantation within the brain of an animal;
a biological data recording device in signal communication with the electrodes and acquiring signals of the plurality of electrodes;
the pulse stimulator is connected with the animal brain through an optical fiber and is used for carrying out optical stimulation on the animal brain;
a controller in signal connection with the biological data recording device and the pulse stimulator and configured to: and reading the signal of the biological data recording device, processing the signal, and outputting a corresponding signal to the pulse stimulator after the optogenetic triggering condition is reached so as to perform optical stimulation on the brain of the animal.
In one embodiment, the controller includes:
the command receiving and analyzing unit is used for receiving and analyzing external commands and allowing the trigger threshold to be changed according to the requirements of users;
the storage unit is used for storing signals in real time; and
and the processing unit is used for processing the data in real time and continuously detecting whether the optogenetic triggering condition is met currently.
In one embodiment, the controller further includes a display unit, and the processing result of the processing unit is displayed in real time through the display unit.
In one embodiment, the processing unit comprises:
a data reading module that reads signals of a plurality of electrodes from the biological data recording device;
the filtering module is used for carrying out filtering processing on the read-in signals;
the calculation module is used for calculating the signals subjected to the filtering treatment;
the judging module is configured to judge whether the optogenetic triggering condition is met according to the data calculated by the calculating module; and
and the output module sends laser pulses to the pulse stimulator according to the output state determined by the judging module.
In one embodiment:
the data reading-in module is configured to: acquiring an actual signal y of the ith electrode from the read-in signal of the ith electrode read in by the following formula 1 i Equation 1: y is i＝ di×scale i X 1000, wherein scale i Analog signal amplification factor for the ith electrode;
the filtering module is configured to: actual signal y to the ith electrode i Filtering to obtain a filtered signal R i ；
The computing module is configured to: the root mean square RMS1 of the filtered signal within the first time length window is calculated by the following equation 2 i And root mean square RMS2 of the filtered signal within the second time length window i Equation 2:wherein W is the number of data points in the corresponding window;
the judging module is configured to: comparing root mean square RMS1 of the filtered signal over a first time length window i Whether or not it is greater than the root mean square RMS2 of the filtered signal within the second time length window i Three times (1).
In one embodiment, the processing unit comprises:
a data reading module that reads signals of a plurality of electrodes from the biological data recording device;
the calculation module: the calculation module is configured to calculate a correlation between any two read-in signals,
the filtering module is used for calculating and smoothing the correlation obtained by the calculating module;
the judging module is used for judging whether the functional connection strength of channel pairing which is higher than a certain proportion is higher than a given threshold value; and
and the output module is used for sending laser pulses to the pulse stimulator according to the output state determined by the judging module.
In one embodiment of the present application, in one embodiment,
the filtering module is configured to: determining a trigger condition according to a baseline, and correcting and smoothing the correlation obtained by the calculation module by using a 200-500 millisecond moving average filter so as to generate real-time functional connection strength;
the judging module is configured to: when more than 25% of the channel pairing function connection strength is higher than a given threshold value, starting to trigger the pulse stimulator to emit laser pulses; and
the output module is configured to: the delay from reaching the threshold to the beginning of the pulse stimulation is set to 20-40 milliseconds; and after the laser pulse stimulus is triggered, continuing until the connection strength drops below a threshold again.
In one embodiment, the biological data recording device is an OmniPlex multichannel recording system, and the pulse stimulator is a Master-9 pulse stimulator, and the controller is implemented using Matlab software. In one embodiment, the controller is configured to increase the response speed through parallel computation, and call the Nvidia GPU to perform parallel operation by means of the CUDA toolkit, so as to accelerate the online detection speed.
In one embodiment, the closed-loop optogenetic intervention system includes a plurality of electrodes and a plurality of optical fibers and uses multiple-input multiple-output closed-loop control.
According to another aspect of the present application there is also provided a closed loop optogenetic intervention method comprising the steps of:
implanting a plurality of electrodes into the brain of the animal;
signal communication with the electrodes using a biological data recording device and collecting signals of the plurality of electrodes;
and reading the signal of the biological data recording device, processing the signal in real time, and sending a laser pulse to perform optical stimulation on the brain of the animal after the optogenetic triggering condition is reached.
In one embodiment, the step of "reading the signal of the biological data recording device and processing the signal in real time and transmitting a laser pulse to photostimulate the brain of the animal after the optogenetic trigger condition is reached" comprises the sub-steps of:
reading signals of a plurality of electrodes from the biological data recording device;
filtering the read-in signal;
calculating the filtered signals;
judging whether the optogenetic triggering condition is met; and
and sending laser pulses for optical stimulation.
In one embodiment:
in the step of "reading signals of a plurality of electrodes from the biological data recording apparatus", the actual signal y of the i-th electrode is obtained from the read-in signal of the i-th electrode read in by the following formula 1 i Equation 1: y is i＝ di×scale i X 1000, wherein scale i Analog signal amplification factor for the ith electrode;
in the step "filter-processing the read-in signal", the actual signal y of the i-th electrode is subjected to i Filtering to obtain a filtered signal R i ；
In the step "calculate filtered signal", the root mean square RMS1 of the filtered signal in the first time length window is calculated by the following equation 2 i And root mean square RMS2 of the filtered signal within the second time length window i Equation 2:wherein W is the number of data points in the corresponding window; and
in the step of judging whether the optogenetic trigger condition is satisfied, the root mean square RMS1 of the filtered signal in the first time length window is compared i Whether or not it is greater than the root mean square RMS2 of the filtered signal within the second time length window i Three times (1).
In one embodiment, the step of "reading the signal of the biological data recording device and processing the signal in real time and transmitting a laser pulse to photostimulate the brain of the animal after the optogenetic trigger condition is reached" comprises the sub-steps of:
reading signals of a plurality of electrodes from the biological data recording device;
calculating the correlation between any two read-in signals;
calculating and smoothing the obtained correlation;
judging whether the functional connection strength of channel pairing higher than a certain proportion is higher than a given threshold; and
and sending laser pulses for optical stimulation.
In one embodiment, in the step of calculating and smoothing the obtained correlation, determining a trigger condition according to a "baseline", correcting and smoothing the correlation obtained by the calculation module using a 200-500ms moving average filter, thereby generating a real-time functional connection strength;
in the step of calculating the correlation between any two read-in signals, when more than 25% of the channel pairing function connection strength is higher than a given threshold value, triggering the pulse stimulator to emit laser pulses; and
in the step of "sending a laser pulse for optical stimulation", the delay from reaching the threshold value to the start of pulse stimulation is set to 20-40ms, and after the laser pulse stimulation is triggered, it is continued until the connection strength falls below the threshold value again.
The closed-loop optogenetic intervention system and the method can process data flow in experiments in real time, and can analyze LFP activities of a plurality of recording electrodes in real time through the system or the method, realize closed-loop stimulation taking brain function connection as a trigger condition, and modify experimental parameters according to personal requirements. In addition, the parallel computation of the GPU can be invoked, so that the requirements of multiple-input multiple-output of the multi-channel photoelectric combined electrode can be met.
Drawings
FIG. 1 is a block diagram of the connection of a closed loop optogenetic intervention system of one embodiment of the application.
Fig. 2 is a flow chart of preprocessing of LFP according to one embodiment of the present application.
FIG. 3 shows a block diagram of the connections of the units of the controller in one embodiment of the application.
FIG. 4 is a block diagram of the connection of the various modules of a processing unit in accordance with one embodiment of the application.
FIG. 5 is a flow chart of the operation of the processing unit of one embodiment of the present application.
Fig. 6 is a block diagram of the connection of the individual modules of a processing unit according to another embodiment of the application.
FIG. 7 shows a schematic of a process for parallel computation using Matlab.
Fig. 8 is a graph of actual waveforms versus behavioral experimental results for one example of the application of the closed loop optogenetic intervention system of the present application.
Detailed Description
The preferred embodiments of the present application will be described in detail below with reference to the attached drawings, so that the objects, features and advantages of the present application will be more clearly understood. It should be understood that the embodiments shown in the drawings are not intended to limit the scope of the application, but rather are merely illustrative of the true spirit of the application.
In the following description, for the purposes of explanation of various disclosed embodiments, certain specific details are set forth in order to provide a thorough understanding of the various disclosed embodiments. One skilled in the relevant art will recognize, however, that an embodiment may be practiced without one or more of the specific details. In other instances, well-known devices, structures, and techniques associated with the present application may not be shown or described in detail to avoid unnecessarily obscuring the description of the embodiments.
Reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
In the following description, for the purposes of clarity of presentation of the structure and manner of operation of the present application, the description will be made with the aid of directional terms, but such terms as "forward," "rearward," "left," "right," "outward," "inner," "outward," "inward," "upper," "lower," etc. are to be construed as convenience, and are not to be limiting.
For closed-loop optogenetic control, the main aspects of research and difficulties are as follows:
(1) Closed loop control for realizing multiple input multiple output
Even for only a few tens of recording sites the number of possible combinations of connectivity patterns is quite large. Worse, functional connections are difficult to predict. With motor activity, sensory input, and changes in the state of the brain interior, the functional connection may change significantly. With the increase of the number of recorded neurons, to run a model of a large complex network in real time, the original model needs to be decomposed into smaller subsystems or simpler low-order models so that they can run in parallel on a time scale of millisecond.
(2) Ensuring stability of detection method
The neural dynamics are very unstable and often exhibit random transient phenomena such as short-time synchronization of action potentials in cells or extracellular field potentials. The design of the closed-loop control therefore requires that the stability of the detection method be fully considered and the choice of recording and stimulation parameters be critical. Furthermore, the relationship between optogenetic regulation and recorded neural activity is difficult to quantify, which prevents the control algorithm from effectively adjusting the stimulation parameters, resulting in either under-or over-stimulation of the target cells. Therefore, it is important to realize simultaneous electrical signal recording and closed-loop optogenetic stimulation.
The application provides a closed-loop optogenetic intervention system and a closed-loop optogenetic intervention method, which can process data flow in an experiment in real time. The method can analyze the Local Field Potential (LFP) activities of a plurality of recording electrodes in real time, realize closed-loop stimulation taking the functional connection between brains as a trigger condition, and modify parameters according to personal requirements. In addition, the requirements of multi-channel photoelectric combined electrode multi-input multi-output can be met by calling GPU parallel calculation.
Fig. 1 is a block diagram of the connection of a closed loop optogenetic intervention system of one embodiment of the application, as shown in fig. 1, the closed loop optogenetic intervention system 100 comprising a plurality of electrodes 10, a biological data recording device 20, a pulse stimulator 30, and a controller 40. The electrodes 10 are used for being implanted into the brain of an animal and collecting brain electrical signals of a specific brain region, the biological data recording device 20 is connected with the electrodes 10 in a signal manner and receives the brain electrical signals collected by the electrodes, and the pulse stimulator 30 is connected with the brain of the animal through an optical fiber 50 and is used for carrying out optical stimulation on a designated region of the brain of the animal. The controller 40 is in signal connection with the biological data recording device 20 and the pulse stimulator 30 and is configured to: the brain electrical signals of the biological data recording device 20 are read and processed, and after the optogenetic triggering condition is reached, the corresponding output signals are output to the pulse stimulator 30 to perform optical stimulation on the brain of the animal.
The plurality of electrodes 10 are microwire electrodes or silicon electrodes which can detect the electrical activity of neurons in deep brain by being implanted in the brain, the high frequency part of the signals recorded by the electrodes reflects the information of the discharge (spike) of neurons around the site, and the low frequency part, namely the Local Field Potential (LFP), reflects the change of the functional activity of the cell population, and the amplitude is jointly influenced by the number, type, distribution and geometric form of cells around the recording site. LFP reflects the combined effect of cell membrane potential at this site in the range of hundreds of microns around the electrode, which is very sensitive to the behavior of the synchronized firing of the neuron clusters, which are closely related to the functional state and behavior tasks of the brain.
In one embodiment, the biometric data recording device 20 is selected from the OmniPlex multichannel recording system of Plexon corporation. Before recording, whether the whole set of instrument can work normally or not needs to be tested, headset test board HTB-10 can be used for simulating neuron discharge signals, waveform parameters are compared, and the whole set of system can be confirmed to record normally. Thereafter, omniPlex (version 1.11.3, plexon, usa) is turned on for signal acquisition, and PlexControl (version 1.11.3, plexon, usa) is turned on for display and saving of signals. The head stage is then inserted into the bus bar on the electrode cap to collect signals, and then the electrical signals (with the collection frequency of 20 kHz) can be stored on a computer in a pl2 format and imported into a NeuroExplorer (version 5.115,Nex Technologies,USA) for data preprocessing.
Alternatively, the nerve electrical signals collected by OmniPlex may be stored on a computer in the form of pl2, where the nerve electrical signals include a wide band signal, a spike signal, a sequence of events, and so on. Alternatively, the preprocessing of the LFP may be done by means of NeuroExplorer software. A specific flow of this preprocessing is described below with reference to fig. 2.
As shown in fig. 2, firstly, the state of an animal is determined according to the video record of the animal, for example, in the case of taking the brain of a rat as an acquisition object, a signal of the rat in a awake and stationary state needs to be selected to obtain raw data: windeband. Next, LFP is extracted from all signals by low-pass filtering in NeuroExplorer and saved as a format that Matlab (version 2014b,MathWorks Inc,USA) can handle. And removing the power frequency interference. Then, the average power of the signal for each channel is calculated over time, removing the bad contacted problem channels. Finally, detecting head movement interference, the absolute value of the normally recorded local field potential should be less than 20mV, and if the signal fluctuates greatly, the data in the time period is indicated to be subject to head movement interference, and the data segment should be discarded.
It should be noted that the OmniPlex-64 multichannel recording system is a biological data recording system that assembles together the complex electronic components of Plexon, including the synchronizing clocks between the system components and the amplifier digital interface card. The OmniPlex host employs a powerful digital interface supporting up to 64 inputs and one auxiliary analog input, supporting 64 or more non-neural experimental signal inputs.
The controller 40 implements real-time soft processing of the multichannel brain electrical signals, operates on the input data stream, and produces an output that can be used for closed-loop control.
Fig. 3 shows a block diagram of the connection of the modules of the controller 40 in one embodiment of the application, as shown in fig. 3, the controller 40 comprises a command reception parsing unit 41, a storage unit 42 and a processing unit 43 in one embodiment. The command receiving and parsing unit 41 is used for receiving and parsing external commands, and optionally, the command receiving and parsing unit 41 allows the trigger threshold to be changed according to the needs of the user. The storage unit 42 is used for real-time storage of the recorded signals. The processing unit 43 is configured to process the data stream in real time, and continuously detect whether the current state meets the optogenetic triggering condition, and may also use parallel processing to increase the calculation speed when needed.
In one embodiment, the controller 40 described above may be written in Matlab language and may run on an OmniPlex-based signal recording system. For example, real-time threshold selection may be accomplished using Matlab handles and Timer timers. The user can know the waveform of the recording electrode in real time, set a threshold value according to individual differences of animals, and switch between the threshold value detection and the optogenetic stimulation.
An embodiment of the processing unit 43 with a trigger condition of rilpple is described below in connection with fig. 4. As shown in fig. 4, the processing unit 43 includes a data reading module 431, a filtering module 432, a calculating module 433, a judging module 434, and an output module 435. The data reading module 431 is configured to read signals of the plurality of electrodes from the biological data recording device 30, and the filtering module 432 is configured to perform filtering processing on the read signals. The calculation module 433 calculates a filtered signal. The judgment module 434 is configured to judge whether the optogenetic triggering condition is satisfied based on the data calculated by the calculation module 433, and the output module 435 is configured to send the laser pulse to the pulse stimulator 30 based on the output status determined by the judgment module 434.
Alternatively, the data read-in module 431 may read the data format from the OmniPlex server in real time using the matlabcalentdevelopment kit. The obtained data format can be read using the print_parameters () function. They include three types: spike_type, event_type, and content_type. Where spike_type is a SPIKE waveform separated from the server's own SPIKE-picking program, event_type records an event time sequence, here we focus mainly on content_type. The CONTINUOUS_TYPE includes a wideband signal (sampling rate 40 kHz) and a local field potential signal (downsampling to 1 kHz). Simultaneously reading in analog signal amplification scale of the ith recording electrode in the preset AD conversion process i If the times areThe actual signal size of the ith recording electrode and the read-in data d if the number is not 1 i The relationship of (2) is:
y i ＝d i ×scale i ×1000
wherein y is i Is in mV.
Reading in input signals from N recording electrodesAfter this, the detection of the closed-loop control trigger condition can be started, but before this, it is often necessary to confirm the magnitude of the trigger threshold. Because of the variability of individual animals, experienced users are required to debug according to actual conditions. The auxiliary debugging of the threshold value of the curence based on Matlab can display waveforms and the threshold value to be measured in real time according to an electrode channel selected by a user, the refreshing time is 50 milliseconds/frame, and the threshold value to be set is selected by moving a cursor. Without additional modification, the default is set to one-fold mean and three-fold standard deviation.
In one embodiment, filtering module 432 selects a bandpass filter with a cutoff frequency of 80-250 Hz. The filter is provided with two sliding windows, the lengths of the sliding windows are 8ms and 2s respectively, the sliding windows are respectively marked as a first time length window and a second time length window, and the sampling frequency is set to be 1000Hz, so that the number of data points of the first time length window is 8, and the number of data points of the second time length window is 2000. For each channel i of the multi-channel electrode, the acquired LFP signal y is first i Obtaining a signal R of a rip frequency band through a filter i 。
The calculation module 433 calculates the root mean square of the signal within two time length windows, i.e. the root mean square RMS1 of the signal of the first time length window i And root mean square RMS2 of the signal of the second time length window i . Root mean square RMS of window with data point data W i The calculation method of (1) is as follows:
the determination module 434 is configured to compare RMS1 i And 3×RMS2 i If RMS1 i Exceeding 3×RMS2 i If the state of the channel continues for 8s or more, defining that a ripple event has occurred, and defining the state (state) of the channel as state=1, otherwise defining the state of the channel as state=0. At one detection instant, one state is described for each channel. How to integrate this information depends on how closed-loop optogenetic logic is used. When multiple-input multiple-output (multiple-input) closed-loop control is used, these states can be assigned to the corresponding fibers, and if multiple-input single-output (multiple-inputs and single-output) closed-loop control is used, integration of these results from different channels is required.
The flow of processing unit 43 involving a rule as a trigger condition according to one embodiment of the present application is described below in connection with fig. 5. Fig. 5 is a flow chart of a processing unit 43 involving a rule as a trigger condition according to one embodiment of the application. As shown in fig. 5, each module of the processing unit 43 performs the following steps:
step 100, starting the system;
step 200, initializing parameters;
step 300, the data reading module 431 reads data from OmniPlex;
step 400, the filtering module 432 passes the data through an 80-250Hz band pass filter;
step 500, the computing module 433 computes root mean square RMS1 and RMS2;
step 600, the judging module 434 judges whether the stimulation duration is reached, and when the stimulation duration is not reached, it goes to step 700 described below, and when the stimulation duration is reached, it goes to step 800;
step 700, comparing the sizes of RMS1 and 3×RMS2, and entering step 900 when RMS1>3×RMS2, and returning to step 300 when RMS 1+.3× RMS2;
step 800, inputting a 60ms pulse to an output module, such as plexDo;
step 900, disconnect OminiPlex and go to step 1000.
Step 1000 ends.
An implementation of the processing unit 43A with a functional connection as a trigger condition according to another embodiment of the present application is described below with reference to fig. 6. As shown in fig. 6, the processing unit 43A includes:
a data reading module 431A for reading signals of the plurality of electrodes from the biological data recording device 20;
a calculating module 432A, configured to calculate a correlation between any two read signals;
the filtering module 433A is used for calculating and smoothing the correlation obtained by the calculating module;
a judging module 434A, configured to judge whether or not the functional connection strength of the channel pairing is higher than 25% and determine an output state when the functional connection strength of the channel pairing exceeding 25% is higher than a given threshold; and
an output module 435A for transmitting laser pulses to the pulse stimulator 30 according to the output status determined by the decision module 434A.
Wherein, the functional connection of the brain refers to the connection between brain regions sharing functional attributes. More specifically, it may be defined as the relatedness of different spatial occurrences. A main indicator describing the functional connection state of a neural network in neuroscience is the correlation (coherence) of signals between brain regions.
The brain interval is simultaneously enhanced and phase synchronized by certain nerve oscillation, provides rhythmic time window for information communication between the brain intervals and reflects the change of brain function connection. Coherence is often used in neuroscience to quantify the functional connection between two brain regions. To calculate the coherence between the two LFP signals x (t) and y (t), the power spectrum of the signal may be obtained first using a Multi-window spectrum estimation (Multi-tap method). Fourier transforms of x (t), (t=1, 2, …, N) for each orthogonal window (tap)The method comprises the following steps:
the definition of the cross-power spectrum between two time sequences x (t) and y (t) is:
the definition of Coherence is:
wherein S is xx (f) And S is yy (f) The self-power spectral densities, x (t) and y (t), respectively, are derived in the same way as the cross-spectral densities. The Coherence is 0-Cxy (f) 1. For LFP collected from different days after training, differences between corherence are compared by adopting a mode of independent sample t test, and when p<At 0.05, a significant difference between these functional linkages was considered.
Optionally, the filtering module 433A uses a 200-500ms moving average filter to correct and smooth the computed coherence, thereby generating a real-time functional connection strength, and controls stimulation triggering through a custom threshold.
Optionally, the output module 435A is configured to have a delay of 20-40ms from reaching the threshold to the beginning of the stimulation, and to continue the optical stimulation once triggered until the connection strength drops below the threshold again.
In one embodiment, the output modules 435 and 435A are configured to: once the output status is calculated, plexDO can be used to control the output of the device up and column. Each bit may be independently set to a low level (0V) or a high level (+5v). For example, bit 0 (DIO 0) may be set to 1 to indicate the start of the optical stimulus, which is set to 0 at the end of the optical stimulus. Throughout the process, the potential remains in the set state (0V or 5V) until the user changes them. The PlexDO line number starts at 1 and the first line corresponds to the name GPCTR0 on the NIDAQ card.
Alternatively, the waveform output may have two modes: setting a fixed pulse length and adjusting a laser switch according to actual detection conditions. The former will output a length-determining pulse square wave, which in the happle trigger condition is typically 60ms, and in the sphere trigger condition is typically set to 200ms. The latter will remain in the set state until the next output changes state value.
Optionally, the controller is configured to increase the response speed by parallel computing. The CPU is responsible for the transaction processing and serial computation with strong logic, and the CUDA toolkit is used for calling the Nvidia GPU to carry out parallel operation, so that the online detection speed can be accelerated. In Matlab's GPU computing, data between Matlab work areas and Graphics Processors (GPUs) are transferred to each other, and each of them has a storage address space that is independent of each other, that is, the memory of the host and the video memory of the device. The main calling method is to use the GPUs array () function to put data on the GPU for operation, and then use the other () function to fetch the result. Matlab defines rich library functions on the GPU, and the method mainly uses fast Fourier transform and matrix multiplication. Some complex functions cannot be accelerated by using Matlab internal codes, and can be compiled into mex files which can be called by Matlab through calling a.cu file and mixed programming of Matlab and C.
FIG. 7 shows the above process, as shown in FIG. 7, for input signals from N recording electrodes in order to split the final operation into a fast Fourier transform and a matrix multiplicationWe integrate it into Data channel_pair ＝[y i ；y j ]Wherein y is i 、y j Represents the time series from the ith and jth electrode respectively, for N recording electrodes together +.>A combination. Similarly, parameters from K orthogonal windows are sorted in the process to +.>Thus, each column of Data contains information about a certain set of pairs of recording electrodes, which can be separately passed to each core of the GPU for processing during correlation detection.
An example of rat conditional fear memory intervention using the closed loop optogenetic intervention system of the present application is described below.
The experimental object: clean-grade male SD rats 28 days after birth were selected for this study and purchased from the university laboratory animal center of Nanjing medical science. The photo-sensitive channel protein expression virus AAV9-CamkII alpha-NpHR-mCherry of the experiment was packaged by the biological company, and the control virus AAV9-CamkII alpha-mCherry was purchased. After virus injection, SD male rats were kept on feeding to week 8, and virus transfection efficiency and expression level of sensitive channel protein were confirmed by observing fluorescence. 570nm yellow light was used to activate the NpHR receptor, inhibiting cellular activity. The animal feeding, surgery and other related operations for experimental animals are in strict compliance with the regulations of the international animal ethics act and the relevant regulations of the animal feeding and management committee of university of southeast.
The experimental method comprises the following steps: the application relates to a closed loop optogenetic intervention system, which is used after training of memory of conditional fear (Fear conditioning, FC) on rats, and performs inhibition of hippocampal ripple on days 0 to 7 after experiments, and performs synchronous recording on intervention signals, so that the inventor can perform statistics on the results of five groups of mice, the statistics show that the detection delay time in the experiments is lower than 10ms, the detection delay time is set to be three times standard deviation, the average stimulus time is 12.86 percent of the total stimulus time, and the amplitude of the signals is not considered to be more than 20 percent of the channel of the rats when the detection delay time is set to be three times standard deviation, and the channel amplitude of the signals is set to be more than 16 percent of the channel of the rats.
Fig. 8 is an actual waveform and behavioral experimental results of one example of application of the closed loop optogenetic intervention system of the present application. The left-hand view of fig. 8 shows the waveform of optogenetic suppression, and the amplitude of the rilple band (80-250 Hz) is effectively suppressed after receiving the optical stimulus. The rat distant memory evoked experiments were performed and the proportion of stiff time to total test time was calculated using FreezeFrame and found to be significantly reduced in the proportion of stiff state in rats in the closed-loop intervention group (n=4) compared to the control group (n=6) (p <0.01, t-test). This shows that inhibition of hippocampal hippocampus formation in the memory formation phase by the closed-loop control system of the present application effectively interferes with memory formation, thereby directly verifying the effectiveness of the closed-loop optogenetic intervention of the present application from experimental results.
While the preferred embodiments of the present application have been described in detail, it will be appreciated that those skilled in the art, upon reading the above teachings, may make various changes and modifications to the application. Such equivalents are also intended to fall within the scope of the application as defined by the following claims.
Claims (9)
1. A closed loop optogenetic intervention system, the system comprising:
a plurality of electrodes for implantation within the brain of an animal;
a biological data recording device in communication with the electrodes and collecting signals from the plurality of electrodes;
the pulse stimulator is connected with the animal brain through an optical fiber and is used for carrying out optical stimulation on the animal brain; and
a controller in signal connection with the biological data recording device and the pulse stimulator and configured to: reading and processing the signals of the biological data recording device, and outputting corresponding signals to the pulse stimulator after the optogenetic triggering condition is reached so as to perform optical stimulation on the animal brain; wherein the method comprises the steps of
The controller includes:
the command receiving and analyzing unit is used for receiving and analyzing external commands and allowing the trigger threshold to be changed according to the requirements of users;
the storage unit is used for storing signals in real time; and
the processing unit is used for processing the data in real time and continuously detecting whether the optogenetic triggering condition is met currently;
the processing unit includes:
a data reading module that reads signals of a plurality of electrodes from the biological data recording device;
the filtering module is used for carrying out filtering processing on the read-in signals;
the calculation module is used for calculating the signals subjected to the filtering treatment;
the judging module is configured to judge whether the optogenetic triggering condition is met according to the data calculated by the calculating module; and
the output module sends laser pulses to the pulse stimulator according to the output state determined by the judging module; and
the data reading-in module is configured to: acquiring an actual signal y of the ith electrode from the read-in signal of the ith electrode read in by the following formula 1 i Equation 1: y is i＝ di×scale i X 1000, wherein scale i Analog signal amplification factor for the ith electrode;
the filtering module is configured to: actual signal y to the ith electrode i Filtering to obtain a filtered signal R i ；
The computing module is configured to: the root mean square RMS1 of the filtered signal within the first time length window is calculated by the following equation 2 i And root mean square RMS2 of the filtered signal within the second time length window i Equation 2:wherein W is the number of data points in the corresponding window;
the judging module is configured to: comparing root mean square RMS1 of the filtered signal over a first time length window i Whether or not it is greater than the root mean square RMS2 of the filtered signal within the second time length window i Three times (1).
2. The closed loop optogenetic intervention system of claim 1 wherein: the controller also comprises a display unit, and the processing result of the processing unit is displayed in real time through the display unit.
3. The closed loop optogenetic intervention system of claim 1 wherein the biological data recording device is an OmniPlex multichannel recording system, the pulse stimulator is a Master-9 pulse stimulator, and the controller is implemented using Matlab software.
4. The closed loop optogenetic intervention system of claim 1 wherein the controller is configured to increase response speed by parallel computing and invoke Nvidia GPU with CUDA toolkit for parallel operation to accelerate online detection speed.
5. A closed loop optogenetic intervention system, the system comprising:
a plurality of electrodes for implantation within the brain of an animal;
a biological data recording device in communication with the electrodes and collecting signals from the plurality of electrodes;
the pulse stimulator is connected with the animal brain through an optical fiber and is used for carrying out optical stimulation on the animal brain; and
a controller in signal connection with the biological data recording device and the pulse stimulator and configured to: reading and processing the signals of the biological data recording device, and outputting corresponding signals to the pulse stimulator to perform optical stimulation on the brain of the animal after the optogenetic trigger condition is reached, wherein the controller comprises a processing unit, and the processing unit comprises:
a data reading module that reads signals of a plurality of electrodes from the biological data recording device;
the calculation module: the calculation module is configured to calculate a correlation between any two read-in signals,
the filtering module is used for calculating and smoothing the correlation obtained by the calculating module;
the judging module is used for judging whether the functional connection strength of channel pairing which is higher than a certain proportion is higher than a given threshold value or not and is configured to start triggering the pulse stimulator to emit laser pulses when more than 25% of the functional connection strength of channel pairing is higher than the given threshold value; and
and the output module is used for sending laser pulses to the pulse stimulator according to the output state determined by the judging module.
6. The closed loop optogenetic intervention system of claim 5 wherein,
the filtering module is configured to: determining a trigger condition according to a baseline, and correcting and smoothing the correlation obtained by the calculation module by using a 200-500 millisecond moving average filter so as to generate real-time functional connection strength; and
the output module is configured to: the delay from reaching the threshold to the beginning of the pulse stimulation is set to 20-40 milliseconds; and after the laser pulse stimulus is triggered, continuing until the connection strength drops below a threshold again.
7. The closed loop optogenetic intervention system of claim 5 wherein the biological data recording device is an OmniPlex multichannel recording system, the pulse stimulator is a Master-9 pulse stimulator, and the controller is implemented using Matlab software.
8. The closed loop optogenetic intervention system of claim 5 wherein the controller is configured to increase response speed by parallel computing and invoke Nvidia GPU with CUDA toolkit for parallel operation to accelerate online detection speed.
9. The closed loop optogenetic intervention system of claim 5 wherein the controller further comprises a display unit, the processing results of the processing unit being displayed in real time by the display unit.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111254993.1A CN113967023B (en) | 2021-10-27 | 2021-10-27 | Closed-loop optogenetic intervention system and intervention method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111254993.1A CN113967023B (en) | 2021-10-27 | 2021-10-27 | Closed-loop optogenetic intervention system and intervention method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113967023A CN113967023A (en) | 2022-01-25 |
| CN113967023B true CN113967023B (en) | 2023-08-29 |
Family
ID=79588673
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111254993.1A Active CN113967023B (en) | 2021-10-27 | 2021-10-27 | Closed-loop optogenetic intervention system and intervention method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113967023B (en) |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104225790A (en) * | 2014-09-19 | 2014-12-24 | 清华大学 | Closed loop nerve stimulation system |
| CN104436447A (en) * | 2014-10-11 | 2015-03-25 | 中国科学院苏州生物医学工程技术研究所 | Closed loop photostimulation system used for regulating and controlling functions of spinal cord |
| CN104874102A (en) * | 2015-04-02 | 2015-09-02 | 中国科学院苏州生物医学工程技术研究所 | Multi-channel wireless closed loop deep brain neural sensing and control system |
| CN105361880A (en) * | 2015-11-30 | 2016-03-02 | 上海乃欣电子科技有限公司 | Muscle movement event recognition system and method |
| CN105688333A (en) * | 2016-01-12 | 2016-06-22 | 中国人民解放军军事医学科学院基础医学研究所 | Nerve effect evaluation apparatus of external transcranial magnetic stimulation |
| CN109011148A (en) * | 2018-08-29 | 2018-12-18 | 复旦大学 | Portable closed loop brain depth stimulator system with intelligent adaptive function |
| CN110464298A (en) * | 2019-07-25 | 2019-11-19 | 深圳大学 | A kind of EEG Processing device and method |
| CN111298304A (en) * | 2020-03-20 | 2020-06-19 | 北京航空航天大学 | Diaphragm nerve light stimulation system with variable light intensity |
| CN210963587U (en) * | 2019-07-25 | 2020-07-10 | 武汉大学 | Closed-loop regulating and controlling device for treating epilepsy based on optogenetics |
| CN112451854A (en) * | 2020-12-01 | 2021-03-09 | 中国康复研究中心 | Implanted diaphragm pacemaker and control method thereof |
| CN113181569A (en) * | 2021-04-27 | 2021-07-30 | 燕山大学 | Closed-loop transcranial brain stimulation system and method |
| CN113476060A (en) * | 2021-06-16 | 2021-10-08 | 南京曦光信息科技研究院有限公司 | Closed-loop feedback type photoacoustic and electromagnetic integrated brain working rhythm adjusting device |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8280516B2 (en) * | 2008-10-09 | 2012-10-02 | Daniel Graupe | Method and apparatus for closed-loop deep brain stimulation in treating neurological diseases |
| GB201613033D0 (en) * | 2016-07-28 | 2016-09-14 | Univ Newcastle | Optogenetic system and method |
-
2021
- 2021-10-27 CN CN202111254993.1A patent/CN113967023B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104225790A (en) * | 2014-09-19 | 2014-12-24 | 清华大学 | Closed loop nerve stimulation system |
| CN104436447A (en) * | 2014-10-11 | 2015-03-25 | 中国科学院苏州生物医学工程技术研究所 | Closed loop photostimulation system used for regulating and controlling functions of spinal cord |
| CN104874102A (en) * | 2015-04-02 | 2015-09-02 | 中国科学院苏州生物医学工程技术研究所 | Multi-channel wireless closed loop deep brain neural sensing and control system |
| CN105361880A (en) * | 2015-11-30 | 2016-03-02 | 上海乃欣电子科技有限公司 | Muscle movement event recognition system and method |
| CN105688333A (en) * | 2016-01-12 | 2016-06-22 | 中国人民解放军军事医学科学院基础医学研究所 | Nerve effect evaluation apparatus of external transcranial magnetic stimulation |
| CN109011148A (en) * | 2018-08-29 | 2018-12-18 | 复旦大学 | Portable closed loop brain depth stimulator system with intelligent adaptive function |
| CN110464298A (en) * | 2019-07-25 | 2019-11-19 | 深圳大学 | A kind of EEG Processing device and method |
| CN210963587U (en) * | 2019-07-25 | 2020-07-10 | 武汉大学 | Closed-loop regulating and controlling device for treating epilepsy based on optogenetics |
| CN111298304A (en) * | 2020-03-20 | 2020-06-19 | 北京航空航天大学 | Diaphragm nerve light stimulation system with variable light intensity |
| CN112451854A (en) * | 2020-12-01 | 2021-03-09 | 中国康复研究中心 | Implanted diaphragm pacemaker and control method thereof |
| CN113181569A (en) * | 2021-04-27 | 2021-07-30 | 燕山大学 | Closed-loop transcranial brain stimulation system and method |
| CN113476060A (en) * | 2021-06-16 | 2021-10-08 | 南京曦光信息科技研究院有限公司 | Closed-loop feedback type photoacoustic and electromagnetic integrated brain working rhythm adjusting device |
Non-Patent Citations (1)
| Title |
|---|
| 光遗传学无线光刺激器与闭环调控系统研制;袁明军;硕士电子期刊;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113967023A (en) | 2022-01-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111477299B (en) | Method and device for regulating and controlling sound-electricity stimulation nerves by combining electroencephalogram detection and analysis control | |
| Rohenkohl et al. | Gamma synchronization between V1 and V4 improves behavioral performance | |
| US7460903B2 (en) | Method and system for a real time adaptive system for effecting changes in cognitive-emotive profiles | |
| Newman et al. | Closed-loop, multichannel experimentation using the open-source NeuroRighter electrophysiology platform | |
| US20120116741A1 (en) | Systems and methods of constructing a patient specific neural electrical stimulation model | |
| Farokhniaee et al. | Theoretical principles of deep brain stimulation induced synaptic suppression | |
| CN116570834B (en) | Transcranial direct current stimulation method, device, terminal and medium | |
| Porcaro et al. | Multiple frequency functional connectivity in the hand somatosensory network: an EEG study | |
| CN117064409B (en) | Method, device and terminal for evaluating transcranial direct current intervention stimulation effect in real time | |
| Chen et al. | An IC-PLS framework for group corticomuscular coupling analysis | |
| Chen | A primer on neural signal processing | |
| Zhang et al. | Homology characteristics of Eeg and Emg for lower limb voluntary movement intention | |
| CN113967023B (en) | Closed-loop optogenetic intervention system and intervention method | |
| Fu et al. | Calculation and analysis of microstate related to variation in executed and imagined movement of force of hand clenching | |
| Virdi et al. | Home automation control system implementation using SSVEP based brain computer interface | |
| CN115192907A (en) | Real-time biofeedback percutaneous vagus nerve electronic acupuncture device | |
| Myers et al. | Single-trial classification of disfluent brain states in adults who stutter | |
| Filippov et al. | Sound-induced changes of infraslow brain potential fluctuations in the medial geniculate nucleus and primary auditory cortex in anaesthetized rats | |
| Lütkenhöner | Auditory signal detection appears to depend on temporal integration of subthreshold activity in auditory cortex | |
| Molina | Direct brain-computer communication through scalp recorded EEG signals | |
| Ranganathan et al. | NeuralCLIP: a modular FPGA-based neural interface for closed-loop operation | |
| De Santos et al. | A client–server architecture for remotely controlling a robot using a closed-loop system with a biological neuroprocessor | |
| Erbslöh et al. | Artefact-suppressing analog spike detection circuit for firing-rate measurements in closed-loop retinal neurostimulators | |
| Rudolph et al. | Inferring network activity from synaptic noise | |
| CN113112017B (en) | Electroencephalogram grading and prognosis FPGA decoding system based on neural manifold |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |