KR20140133328A - apparatus and method for recording neural signals - Google Patents

Abstract

Disclosed are a device to measure a neural signal right after electrostimulation using a low-gain amplifier and a high-resolution digital converter and a measurement method using the same. The measurement device given in the present invention includes: an electrostimulation signal generation unit, a sensing unit, and a detection unit. The electrostimulation signal generation unit generates an electrostimulation signal. The sensing unit senses a response signal from a neuron in response to the electrostimulation signal. The detection unit amplifies the response signal sensed by the sensing unit into a neural signal, extracts a transient response by removing the neural signal from the neural signal transient response signal included in the amplified response signal, calculates the difference between the two, and detects the neural signal during the time section when the transient response occurred.

Description

[0001] The present invention relates to an apparatus and a method for measuring a neural signal,

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an apparatus and a method for measuring a neural signal, and more particularly, to a neuron signal measuring apparatus and method for detecting a response signal generated by an electric stimulus and restoring a neural signal in the sensed response signal .

Methods of stimulating neurons in specific areas of the brain using electrical stimulation have been widely used as treatments for degenerative brain diseases such as Parkinson's disease or as pain relief therapies. Micro-stimulation through microelectrodes has been used for local nerve stimulation requiring high selectivity, and the current applied through the electrode induces rearrangement of ions around the cell membrane, cause.

Accurate measurement of neuronal responses to stimuli at the stimulation site almost simultaneously with stimulation is very important in determining the effect on the stimulus when the electrical stimulus is applied. However, due to the transient response of several hundred mV due to the stimulation, it is a reality that the response can not be measured up to 100 ms immediately after stimulation.

The main reason for the difficulty in measuring nerve signals immediately after stimulation is due to the charge remaining on the electrode electrolyte interface due to the applied electric stimulation pulse. Immediately after stimulation, the remaining charge is slowly discharged through the high impedance path at the electrode-electrolyte interface, thus producing a long lasting transient signal to the neural signal measuring device.

Since the most widely used neural signal measuring devices currently use 12-bit or 16-bit analog-to-digital converters (ADCs), high gain (1000 to 10,000) analog amplifiers . These neural signal measuring devices cause an amplifier saturation state during the application of electric stimulus of several volts, so that the signal can not be recorded, and in addition, it is used to eliminate the reverse electrode potential existing in the electrode-electrolyte interface The signal non-writable period further increases due to the filter transient response of the high-pass filter.

Quot; E. < / RTI > to quickly remove the remaining charge at the electrode interface. A. Brown, S. Member, J. D. Ross, S. Member, R. A. Blum, Y. Nam, B. C. Wheeler, and S. P. Deweerth, "Stimulus-Artifact Elimination in a Multi-Electrode System," vol. 2, no. 1, pp. Immediately after stimulation, a low impedance path can be used to reduce the size and duration of the artifacts, as shown in Fig. 10-21, 2008. The desaturation of a faster amplifier (desaturation time), indicating that fast signal recording is possible.

Incidentally, "Y. Jimbo, N. Kasai, K. Torimitsu, T. Tateno, and HPC Robinson, "A system for MEA-based multisite stimulation.," IEEE transactions on bio - medical engineering , vol. 50, no. 2, pp. 241-8, Feb. 2003, "the effect of the filter response of the high-pass filter for removing DC offset was minimized by using a" blanking circuit "which cuts off the input of the amplifier during the stimulation, and the response after 0.5 ms after stimulation was measured. However, a short pulse of 200μs was used for a small intensity of 4μA.

However, these methods have disadvantages such as the necessity of complicated circuit implementation of a blanking circuit, a sample and hold circuit, and a discharge circuit. "A retrofitted neural recording system with a novel stimulation IC to monitor early neural responses from a stimulating electrode." Journal of neuroscience methods, vol. 178, no. 1, pp. 99-102, Mar. 2009. "There is a trade-off in discharge time and recording time. In addition, these methods have drawbacks that it is difficult to manufacture a generalized system because the parameters required by various factors such as the intensity of the stimulus and the impedance of the electrode are changed.

When noise caused by stimulation is within the linear operating range of the amplifier, methods for recovering neural signals using a digital signal processing method have been proposed. A high-pass digital filter can be simply applied, and a high-pass digital filter can be applied, and the " IEEE transactions on bio - medical engineering , vol. 50, no. 10, pp. 1129-35, Oct. 2003. ", an optimized Wiener filter is used to remove the transient response frequency band, or " D. a Wagenaar and SM Potter, "Real-time multi-channel stimulus artifact suppression by local curve fitting.", Journal of neuroscience methods , vol. 120, no. 2, pp. 113-20, Oct. 2002. "Local curve fitting," or "T. Wichmann, " A digital averaging method for removal of stimulus artifacts in neurophysiologic experiments. &Quot; Journal of neuroscience methods , vol. 98, no. 1, pp. 57-62, May 2000. " Hashimoto, CM Elder, and JL Vitek , "A template subtraction method for stimulus artifact removal in high-frequency deep brain stimulation.," Journal of neuroscience methods , vol. 113, no. 2, pp. 181-6, Jan. 2002. " Template subtraction method using the template proposed in the present invention can be applied. However, this digital signal processing method can be applied only to the signals output in the linear range of the amplifier. In the case of the partial curve approximation method, it can be applied only to the transient response which changes slowly, and the method of removing the transient response using the template, When a filter is applied, a prior learning is required for the variable.

SUMMARY OF THE INVENTION The present invention has been made in order to solve the above-described problems, and it is an object of the present invention to provide a method of manufacturing a microelectromechanical system (MEMS), in which a microstimulation of voltage or current is applied to a fine metal electrode, And to provide a well-generalized cranial nerve signal measuring device and a measuring method capable of restoring a signal for a trial.

According to another aspect of the present invention, there is provided an apparatus for measuring a cranial nerve signal, including: an electric stimulation signal generator for generating an electric stimulation signal; a response signal including a nerve signal of the nerve cell responsive to the generated electric stimulation signal; Detecting a transient response signal in the sensed response signal, correcting the detected transient response signal, extracting a neural signal band from the corrected transient response signal, And a detection unit for detecting the nerve signal.

The apparatus for measuring a cerebral nerve signal according to claim 1, further comprising a switch element for selectively applying the electric stimulation signal to the sensing unit according to a change in time, wherein the switching element is a bipolar junction transistor (BJT) A field effect transistor (FET), a solid state relay, and a mechanical relay.

The sensing unit may include at least one of fine active electrodes formed to be arranged in an arrayed structure. The fine active electrode may be one of a transition metal, Si, Ge, and As, including carbon nanotubes (CNTs), Ti, V, Cr, Zn, Y, Zr, Nb and W, Or combinations of one or more of the following metals: Ru, Rd, Pd, Os, Ir, and Pt, including platinum group metals, Ag, Au and Pt, Wherein the metal oxide system has the formula M x O y wherein M is at least one of Ti, V, Cr, Zn, Y, Zr, Nb and W, or a combination of at least one of , A semi-metallic system comprising one or more of Si, Ge and As, a platinum group system comprising one or more of Ru, Rd, Pd, Os, Ir and Pt, And < RTI ID = 0.0 > Pt, < / RTI > (Oxygen), and x and y may be quantitative ratios of metal and oxygen, respectively.

A first signal processing unit for amplifying the response signal, converting the amplified response signal to a digital signal and sampling the processed signal, and a second signal processing unit for correcting the distorted part of the transient response signal in the sampled digital signal, And a second signal processing unit for performing filtering for extracting a neural signal in the transient response signal.

The first signal processing unit may include an amplification unit that outputs the response signal as an amplified signal having a predetermined voltage gain, and an analog-digital conversion unit that converts the amplified signal into a digital signal. The amplifying unit may have a voltage gain value within a range of 1 to 10. The amplification unit may include at least one of an operational amplifier and a transistor.

The analog-to-digital converter may include a 24-bit analog-to-digital converter having an input voltage of +/- 10V.

The second signal processing unit may include a correction unit that uses a method of approximating a transient response signal including the neural signal to extract a transient response signal that does not include the neural signal, And a filter unit for extracting an action potential band of the nerve signal. The corrector may determine a variable used in the method of approximating the partial curve based on at least one of a time to lower to baseline and a rate of change of existing action potential magnitude. The correcting unit may select a variable whose fastest transient response is removed from the set of variables whose neural signal size is 85% or more.

And a total 2.8 ms section including samples for the preceding 0.8 ms time period and samples for the following 2 ms based on the data point with respect to one data point in extracting the transient response signal not including the neural signal Wherein the samples existing in the brain are used.

The partial curve approximation of the partial curve approximation method may be based on a cubic polynomial.

The filter unit may be a low-pass filter that passes a band of 0 to 8 kHz.

According to another aspect of the present invention, there is provided a method for measuring a cranial nerve signal, comprising the steps of generating an electrical stimulation signal, sensing a reaction signal including a neural signal of the neuron in response to the electrical stimulation signal, Amplifying the sensed response signal, converting the amplified response signal to a digital signal, detecting a transient response signal in the transduced response signal, and correcting the detected transient response signal, And extracting a nerve signal band from the transient response signal and detecting a nerve signal present in the corrected transient response signal.

According to the present invention, there is an advantage that it is always possible to record in the linear range operation of the amplifier due to the low gain amplification in the hardware configuration, and since the high-pass filter for eliminating the DC offset is not used, There is an advantage in that it has a fast de-saturation time even for stimulation of a large intensity.

In addition, the discharge time used for the discharge of the remaining charge is unnecessary, and as a result, the faster recording time is shown. Also, the optimization process of the variable used for the partial curve approximation can recover the nerve signal even in the transient response And is robust to various types of current or voltage stimulation waveforms.

1 is a block diagram illustrating a device for measuring a cranial nerve signal measuring a cranial nerve signal immediately after stimulation according to an embodiment of the present invention.
FIG. 2 is a flowchart illustrating a method of measuring a cranial nerve signal using the cranial nerve signal measuring apparatus for measuring a cranial nerve signal immediately after stimulation shown in FIG. 1;
FIG. 3 is a flowchart for explaining step S200 shown in FIG. 2 in more detail.
4 is a flow chart for explaining step S220 shown in FIG. 2 in more detail.
5 illustrates a process of extracting a nerve signal by removing a transient response at a stimulation electrode using a method of measuring a cranial nerve signal according to an embodiment of the present invention.
FIG. 6 is a diagram showing a shape in which the magnitude of the action potential is modified by a partial curve approximation method of various combinations of variables. FIG.
FIG. 7 is a graph showing a graph for comparing the restored neural signals with the restored neural signals by the second-order Butterworth digital filtering method with respect to various combinations of the parameters of the partial curve approximation method.
Each of Figures 8A and 8B shows the size preservation rate (%) of the action potential with respect to the two variable pre-window length and post-window length used and the time point at which it is restored to the baseline FIG. 8C is a diagram showing a formula for a cost obtained by extracting the preservation rate and the restoration time with a weight of 1: 1, and FIG. 2 is a view showing costs in two dimensions.
FIG. 9 is a block diagram illustrating a cranial nerve signal measuring system for measuring a cranial nerve signal immediately after stimulation according to an embodiment of the present invention.
FIG. 10 is a diagram comparing measured electrode potentials when a commercialized neural measurement system is used and when a low-gain high-resolution measurement system is used.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, a detailed description of preferred embodiments of the present invention will be given with reference to the accompanying drawings. In the following description of the present invention, detailed description of known functions and configurations incorporated herein will be omitted when it may make the subject matter of the present invention rather unclear.

Embodiments in accordance with the concepts of the present invention can make various changes and have various forms, so that specific embodiments are illustrated in the drawings and described in detail in this specification or application. It is to be understood, however, that it is not intended to limit the embodiments according to the concepts of the present invention to the particular forms of disclosure, but includes all modifications, equivalents, and alternatives falling within the spirit and scope of the invention.

It is to be understood that when an element is referred to as being "connected" or "connected" to another element, it may be directly connected or connected to the other element, . On the other hand, when an element is referred to as being "directly connected" or "directly connected" to another element, it should be understood that there are no other elements in between. Other expressions that describe the relationship between components, such as "between" and "between" or "neighboring to" and "directly adjacent to" should be interpreted as well.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, the terms "comprises ",or" having ", or the like, specify that there is a stated feature, number, step, operation, , Steps, operations, components, parts, or combinations thereof, as a matter of principle.

Hereinafter, the present invention will be described in detail with reference to the drawings.

FIG. 1 is a block diagram showing a device for measuring a cranial nerve signal measuring a cranial nerve signal immediately after electric stimulation according to an embodiment of the present invention.

1, the apparatus for measuring a cerebral nerve signal 100 according to an embodiment of the present invention includes a stimulus signal generation unit 220, a sensing unit 200, and a detection unit 300. As shown in FIG.

The stimulus signal generation unit 220 generates an electric stimulation signal in the form of a voltage or a current and transmits the electric stimulation signal to the sensing unit 200 at the time of stimulation through the switch 210. The switch 210 may include a switch element for selectively applying the electric stimulation signal to the sensing unit 200 according to a change in time.

The switching device may include at least one of a bipolar junction transistor (BJT), a field effect transistor (FET), a solid state relay, and a mechanical relay.

The sensing unit 200 is used to sense the activity of the nerve outside the nerve cell, and is also used to apply electrical stimulation to the nerve cell.

The electric stimulus applied to the sensing unit 200 may not induce the activity of the neuron when the intensity of the stimulus is low.

The sensing unit 200 may include one of fine active electrodes formed to be arranged in an arrayed structure. The microactive electrode may be one of a transition metal, Si, Ge, and As, including carbon nanotubes (CNTs), Ti, V, Cr, Zn, Y, Zr, Nb and W, A noble metal and a metal containing one or more of a combination of one or more of the group consisting of one or more of the following metals: Ru, Rd, Pd, Os, Ir and Pt, An oxide system, and the like. Wherein the metal oxide system has the formula M x O y wherein M is at least one of transition metals, Si, Ge, and As, including at least one of Ti, V, Cr, Zn, Y, Zr, Nb, A platinum group system comprising one or more combinations of one or more of Ru, Rd, Pd, Os, Ir and Pt, and a precious metal group comprising one or more of Ag, Au and Pt, (Oxygen), and x and y may be quantitative ratios of metal and oxygen, respectively.

The detection unit 300 detects a transient response signal in a response signal sensed by the sensing unit 200, corrects the detected transient response signal, extracts a neural signal band from the corrected transient response signal, The neural signal detecting unit 300 may include a first signal processing unit 230 and a second signal processing unit 240. [

The first signal processing unit 230 amplifies the reaction signal output from the sensing unit 200, converts the amplified reaction signal into a digital signal, and samples it. The first signal processing unit 230 includes an amplification unit 231 and an analog-to-digital conversion unit 232.

The amplification unit 231 outputs the response signal as an amplified signal having a predetermined voltage gain. The amplification unit 231 may include an operational amplifier or an amplifier including a transistor having a voltage gain value within a range of 1 to 10. [

The analog-to-digital converter 232 converts the amplified signal output from the amplifier 231 into a digital signal. The analog-to-digital converter 232 converts the input range including ± 10 V to a resolution of 24 bits, and can be a 24-bit analog-to-digital converter with an anti-aliasing filter. In the analog-to-digital converter, the sampling rate is 10 kS / s or more to prevent distortion of the nerve signal band.

The second signal processing unit 240 corrects a distortion part of the transient response signal in the sampled digital signal and extracts a neural signal from the corrected signal. The second signal processing unit 240 may include a correction unit 241 and a filter unit 242.

The correcting unit 241 corrects the distorted portion of the transient response signal in the digital signal and performs a function of moving the signal immediately after stimulation to the baseline.

The correcting unit 241 corrects the transient response signal using a method of extracting a transient response signal that does not include the neural signal using the partial curve approximation method in the transient response signal including the neural signal, can do. And a total 2.8 ms section including samples for the preceding 0.8 ms time period and samples for the following 2 ms based on the data point with respect to one data point in extracting the transient response signal not including the neural signal ≪ / RTI > can be used. The partial curve approximation of the partial curve approximation method may be based on a cubic polynomial.

The corrector 241 can determine the variable used in the partial curve approximation method based on at least one of the time of lowering to the baseline and the rate of change of the existing action potential magnitude. The correcting unit 241 can select a variable in which the transient response is most rapidly removed from the set of variables in which the size of the neural signal is more than 85%.

The filter unit 242 performs a function of extracting the action potential band of the nerve signal from the corrected signal.

The filter section 242 may be a low pass filter that passes a band of 0 to 8 kHz.

FIG. 2 is a flow chart for explaining a method of measuring a cranial nerve signal using a cranial nerve signal measuring apparatus for measuring a cranial nerve signal immediately after stimulation shown in FIG. 1, and FIG. 3 is a flowchart for explaining the step S200 4 is a flow chart for explaining step S210 shown in FIG. 2 in more detail.

As shown in FIG. 2, the brain signal measurement method S100 includes a sensing step S100 and a sensing step S200.

The sensing step S100 may be a step of sensing the response signal from the neuron through the electrical stimulation signal,

The detecting step S200 is a step of detecting the reaction signal in the transient response signal generated by the electric stimulation through a partial curve approximation method and a filtering method.

More specifically, the detecting step S100 is a step of sensing the reaction signal, which is reacted in the nerve cell, by the sensing unit 200 after applying the electric stimulation signal to the nerve cell.

Referring to FIG. 3, the detecting step S200 includes a sampling step S210 and a signal processing step S220.

The sampling step (S210) may be a step of amplifying the response signal, converting the amplified response signal to a digital signal, and sampling the processed signal.

The signal processing step S220 may include detecting a transient response signal in the sampled digital signal, correcting the distorted portion of the detected transient response signal, and extracting a neural signal from the corrected transient response signal .

More specifically, referring to FIG. 4, the signal processing step S220 includes a correction step S221 and an extraction step S222.

The correcting step S221 may be a step of correcting a distortion part of the transient response signal in the sampled digital signal. A method of extracting a transient response signal that does not include the neural signal using the partial curve approximation method in the transient response signal including the neural signal in the correcting step (S221), and then obtaining the difference between the two signals. The variable used in the partial curve approximation method can be determined based on the time of lowering to the baseline and the rate of change of the existing action potential size in the correction step S221.

And a total 2.8 ms section including samples for the preceding 0.8 ms time period and samples for the following 2 ms based on the data point with respect to one data point in extracting the transient response signal not including the neural signal ≪ / RTI > can be used for all sampled data points that contain transient responses.

In the correction step (S221), the partial curve approximation of the partial curve approximation method may be based on a third order polynomial.

The extracting step S222 may be a step of extracting a nerve signal from the transient response signal corrected in the correcting step S221. A low pass filtering that passes a band of 0 to 8 kHz can be performed.

Here, the method according to the present invention may be implemented in the form of a program command that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like, alone or in combination. The program instructions recorded on the medium may be those specially designed and constructed for the present invention or may be available to those skilled in the art of computer software. Examples of computer-readable media include magnetic media such as hard disks, floppy disks and magnetic tape; optical media such as CD-ROMs and DVDs; magnetic media such as floppy disks; Magneto-optical media, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, flash memory, and the like. Examples of program instructions include machine language code such as those produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the present invention, and vice versa.

5 illustrates a process of extracting a nerve signal by removing a transient response at a stimulation electrode using a method of measuring a cranial nerve signal according to an embodiment of the present invention

FIG. 5 shows the transient response at the stimulating electrode due to the current stimulation and the action potential hidden therein.

5, a curve 510 represents a neural signal including a transient response sensed by the sensing unit 200, and a curve 520 represents a transient response signal obtained by a partial curve approximation method at the detector 300 . Curve 530 represents the difference between the transient response signal 510 including the neural signal and the transient response signal 520 not including it. The present invention extracts a neural signal from a difference value (530).

FIG. 6 is a diagram for explaining the degree to which the magnitude of the action potential is reduced by the partial curve approximation method of the combination of three variables.

Referring to FIG. 6, the variable used in the partial curve approximation method may include a pre-window length and a post-window length. The determination criterion of the first used variable needs to preserve at least 85% of the magnitude of the action potential. FIG. 6 shows a detailed drawing after restoration of some of the parameters. In FIG. 6, (0.6: 0.6), (0.8: 2) and (3: 3) mean free window size and post window size (ms), respectively. 6, it can be seen that as the sum of the two window lengths becomes smaller, the magnitude of the action potential decreases.

FIG. 7 is a graph showing a graph for comparing the restored neural signals with the restored neural signals by the second-order Butterworth digital filtering method with respect to various combinations of the parameters of the partial curve approximation method.

In FIG. 7, the triangle 701 represents the stimulation time point, the x axis represents time, and the y axis represents the size of the nerve signal. In FIG. 7, (0.8: 2), (0.6: 0.6) and (3: 3) mean the combination of variables (free window size (ms) and post window size , And the graph shows the result of removing the transient response by each combination of variables. In addition, we use a high pass filter with 500Hz HPF and 200Hz cutoff frequency to compare with the degree of elimination of the transient response by the filter. As a result, it can be confirmed that the transient response removal performance is worse than the partial curve approximation method.

The curve 710 represents the neural signal extracted by using the variable (0.8: 2) as a variable used in the partial curve approximation method. The transient response is rapidly eliminated by the partial curve approximation method of a specific combination of variables, It can be seen that the nerve signal in the transient response becomes measurable. In particular, the combination of symmetric variables (3: 3) is a combination of variables presented by Wagenaar et al. It can be seen that the combination of asymmetric window sizes (0.8: 2) presented in this invention exhibits better performance.

Each of Figures 8A and 8B shows the size preservation rate (%) of the action potential with respect to the two variable pre-window length and post-window length used and the time point at which it is restored to the baseline FIG. 8C is a diagram showing a formula for a cost obtained by extracting the preservation rate and the restoration time with a weight of 1: 1, and FIG. 2 is a view showing costs in two dimensions.

In FIG. 8 (a), each pixel value indicates the time at which the partial curve approximation result reaches three times the standard deviation in the steady state after stimulation, and a smaller value indicates better transient response elimination performance. In FIG. 8 (b), each pixel value represents the degree of preservation of the magnitude of a 200 Hz high-pass filtered magnitude and shape action potential by a partial curve approximation result as shown in FIG. In Fig. 8 (d), each pixel value is a value determined by the equation in Fig. 8 (c), and the unit is percent (%).

8 (d), region 840 is a preferred embodiment of a group of variables required by the present invention. More preferably, the free window size and the post window size required by the present invention can be 0.8 ms and 2 ms, respectively.

FIG. 9 is a block diagram illustrating a cranial nerve signal measuring system for measuring a cranial nerve signal immediately after stimulation according to an embodiment of the present invention.

9, the cerebral nerve signal measuring system 900 includes a stimulus signal generator 910, a switch 920, a microelectrode 930, a 10x gain amplifier 940, an NI 9239 analog-to-digital converter 950, Method partial curve approximation method 960 and an LPF (Low Pass filter) 970. [ The stimulus signal generator 910, the switch 920, the fine electrode 930, the 10x gain amplifier 940, the NI 9239 950, the corrector 960 and the LPF (Low Pass filter) An embodiment of the stimulus signal generator 220, the switch 210, the sensing unit 200, the amplification unit 231, the ADC 232 and the correction unit 241 and the filter unit 242 shown in FIG. 1 have. The NI 9239 940 is a 24-bit ADC 955 that includes an anti-aliasing LFP 951 and the anti-aliasing LFP 951 is a lowpass filter with a cutoff frequency of 12.5 kHz, To a 24-bit digital signal. The LPF (Low Pass Filter) 970 is a low-pass filter having a cutoff frequency of 8 kHz.

10 is a view for comparing measured electrode potentials when a commercially available neural measurement system is used and when a neural signal measurement system according to the present invention is used.

10, the curve 1010 shows the measured electrode potential when a commercialized multichannel system is used, and the curve 1020 shows the electrode potential measured using the cerebral nerve signal measuring system 900 according to the present invention And the measured electrode potential. It can be seen that a high-gain amplification of the transient response of the electrode through the curve 1010 and the resulting saturation of the amplifier result in a non-measurable period 1090 of about 20 ms that fails to extract the nerve signal. It can be seen that the cerebral nerve signal measuring system 900 according to the present invention does not have this section and it is possible to restore the signal immediately after the stimulus 1080. [

The existing measurement system implements additional hardware such as a blanking circuit, a discharge circuit, and a sample and hold circuit to measure neural signals immediately after stimulation, or extracts action potentials by a digital signal processing method. On the other hand, And a method of approximating a partial curve having an asymmetric window size to a non-saturated transient response signal measured using a high resolution ADC to extract an action potential as a method of detecting a neural signal included in a transient response.

Therefore, according to the present invention, it is possible to record a neural signal from a minimum of 2 ms after excitation through a simple configuration of a single amplification stage, a high-resolution digital conversion, and a digital signal processing method. This is about 3 ms earlier than suggested by existing instruments and allows faster response measurements.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the scope of the present invention is not limited to these exemplary embodiments. It will be possible to change it.

100: brain nerve signal measuring apparatus 200:
210: switch 220: electrical stimulation signal generator
230: first signal processing unit 231:
232: ADC 240: second signal processor
241: Correction section 242: Filter section

Claims (16)

An electric stimulation signal generating unit for generating an electric stimulation signal;
A sensing unit sensing a response signal including a neural signal of the neuron in response to the generated electrical stimulation signal; And
Detecting a transient response signal in the sensed response signal, correcting the detected transient response signal, extracting a neural signal band from the corrected transient response signal, and detecting a neural signal present in the corrected transient response signal And a detector for detecting the cranial nerve signal.
The method according to claim 1,
The apparatus for measuring a cranial nerve signal,
Further comprising a switching element for selectively applying the electric stimulation signal to the sensing unit according to a change in time,
The switching device includes:
A bipolar junction transistor, a bipolar junction transistor (BJT), a field effect transistor (FET), a solid state relay, and a mechanical relay. Device.
The method according to claim 1,
The sensing unit includes:
And a fine active electrode formed to be arranged in a distributed arrangement in the structure of the nerve cell.
The method of claim 3,
Wherein the microactive electrode comprises:
Si, Ge, and As, which include one or more than one of carbon nanotubes (CNTs), Ti, V, Cr, Zn, Y, Zr, Nb and W, One of or a combination of one or more of platinum group metals, Ag, Au and Pt including one or a combination of one or more of Ru, Rd, Pd, Os, Ir and Pt, An electrode comprising at least one electrode,
Wherein the metal oxide system has the formula M x O y wherein M is at least one of transition metals, Si, Ge, and As, including at least one of Ti, V, Cr, Zn, Y, Zr, Nb, A platinum group system comprising one or more combinations of one or more of Ru, Rd, Pd, Os, Ir and Pt, and a precious metal group comprising one or more of Ag, Au and Pt, Wherein O is oxygen (Oxygen), and x and y are quantitative ratios of metal and oxygen, respectively.
The method according to claim 1,
The detection unit
A first signal processing unit for amplifying the reaction signal, converting the amplified reaction signal into a digital signal and sampling the processed signal; And
And a second signal processing unit for correcting a distorted part of the transient response signal in the sampled digital signal and performing filtering to extract a neural signal in the corrected transient response signal.
6. The method of claim 5,
Wherein the first signal processor comprises:
An amplifier for outputting the response signal as an amplified signal having a predetermined voltage gain; And
And an analog-to-digital converter converting the amplified signal to a digital signal.
The method according to claim 6,
Wherein,
And a voltage gain value within a range of 1 to 10.
8. The method according to claim 6 or 7,
Wherein,
An operational amplifier, and a transistor.
The method according to claim 6,
The analog-to-
And a 24-bit analog-to-digital converter having an input voltage of +/- 10V.
6. The method of claim 5,
Wherein the second signal processing unit comprises:
Using a partial curve approximation method in a transient response signal including the neural signal to extract a transient response signal that does not include the neural signal, and then obtaining a difference between the two signals; And
And a filter unit for extracting an action potential band of the nerve signal.
11. The method of claim 10,
Wherein,
Wherein the parameter used in the method of approximating the partial curve is determined based on at least one of a time of lowering to the baseline and a rate of change of the existing action potential size.
12. The method of claim 11,
Wherein,
Wherein the variable is selected such that the transient response is most rapidly removed from the set of variables in which the size of the neural signal is preserved by 85% or more.
11. The method of claim 10,
And a total 2.8 ms section including samples for the preceding 0.8 ms time period and samples for the following 2 ms based on the data point with respect to one data point in extracting the transient response signal not including the neural signal Wherein the samples existing in the brain are used.
11. The method of claim 10,
Wherein the partial curve approximation of the partial curve approximation method is based on a third order polynomial.
11. The method of claim 10,
The filter unit includes:
Pass filter for passing a band of 0 to 8 kHz.
Generating an electrical stimulation signal;
Sensing a response signal including a neural signal of a neuron in response to the generated electrical stimulation signal;
Amplifying the sensed reaction signal and converting the amplified reaction signal into a digital signal;
Detecting a transient response signal in the converted response signal and correcting the detected transient response signal; And
And extracting a nerve signal band from the corrected transient response signal to detect a nerve signal present in the corrected transient response signal.

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