Brain wave detection system, brain wave detection method, and noise reduction processing program

Abstract

To provide a brain wave detection system, a brain wave detection method, and a noise reduction processing program capable of reducing a noise included in a measured brain wave.SOLUTION: A brain wave detection system 1 according to an embodiment comprises: a brain wave measurement device 10 for measuring a brain wave; a pulse wave measurement device 20 for measuring a pulse wave; and a noise reduction processing device 50 for performing processing of reducing a noise of the measured brain wave by comparing a time-series signal of the measured brain wave as a measured signal with a time-series signal of the pulse wave measured in a measurement time zone corresponding to the time-series signal of the measured brain wave, as a reference signal.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present disclosure relates to an electroencephalogram detection system, an electroencephalogram detection method, and a noise reduction processing program.

Background Art

[0002] Patent Document 1 discloses an electroencephalogram detection device that corrects measured electroencephalograms using sensor values from sensors that detect myoelectric potentials due to body movements such as chewing and blinking.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Myoelectric noise may be generated from all the muscles in the head and neck, but in the electroencephalogram detection device of Patent Document 1, it is difficult to attach sensors to all the muscles. Therefore, it is difficult to reduce noise caused by muscles to which sensors for detecting myoelectric potentials are not attached.

[0005] The present disclosure has been made to solve such problems, and provides an electroencephalogram detection system, an electroencephalogram detection method, and a noise reduction processing program capable of reducing noise included in measured electroencephalograms.

Means for Solving the Problems

[0006] The electroencephalogram detection system according to this embodiment includes an electroencephalograph that measures an electroencephalogram, a photoplethysmograph that measures a photoplethysmogram, uses the time-series signal of the measured electroencephalogram as a measurement signal, and compares the time-series signal of the photoplethysmogram measured in the measurement time zone corresponding to the time-series signal of the electroencephalogram as a reference signal, and a noise reduction processing device that performs a process of reducing the noise of the measured electroencephalogram. With such a configuration, the noise included in the measured electroencephalogram can be reduced.

[0007] In the electroencephalogram detection system, the noise reduction processing device may filter the measured electroencephalogram and the measured photoplethysmogram in at least one frequency band, use the time-series signal of the filtered electroencephalogram as the measurement signal, and compare the time-series signal of the filtered photoplethysmogram measured in the measurement time zone corresponding to the time-series signal of the electroencephalogram as the reference signal, thereby performing the process of reducing the noise of the electroencephalogram. With such a configuration, the noise of the electroencephalogram can be reduced for each frequency band.

[0008] In the electroencephalogram detection system, the photoplethysmograph may measure the photoplethysmogram at least at either the carotid artery bifurcation or the subclavian artery bifurcation. With such a configuration, the photoplethysmogram closely related to the electroencephalogram can be measured.

[0009] In the electroencephalogram detection system, the noise reduction processing device may perform the process of reducing the noise of the measured electroencephalogram by performing lock-in amplifier processing. With such a configuration, the noise can be reduced by lock-in amplifier processing.

[0010] The electroencephalogram detection method according to this embodiment includes a step of measuring an electroencephalogram, a step of measuring a photoplethysmogram, and a step of using the time-series signal of the measured electroencephalogram as a measurement signal, and comparing the time-series signal of the photoplethysmogram measured in the measurement time zone corresponding to the time-series signal of the electroencephalogram as a reference signal, thereby performing a process of reducing the noise of the measured electroencephalogram. With such a configuration, the noise included in the measured electroencephalogram can be reduced.

[0011] The noise reduction processing program according to this embodiment causes a computer to execute a procedure for reducing the noise of the measured electroencephalogram by comparing the measured time-series signal of the electroencephalogram as a measurement signal with the time-series signal of the plethysmogram measured in the measurement time zone corresponding to the time-series signal of the electroencephalogram as a reference signal. With such a configuration, it is possible to reduce the noise included in the measured electroencephalogram.

Effect of the Invention

[0012] According to this embodiment, it is possible to provide an electroencephalogram detection system, an electroencephalogram detection method, and a noise reduction processing program that can reduce the noise included in the measured electroencephalogram.

Brief Description of the Drawings

[0013]

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Mode for Carrying Out the Invention

[0014] Hereinafter, this embodiment will be described throughout the present disclosure, but the scope of the claims is not limited to the following embodiments. Also, not all of the configurations described in the embodiments are necessarily essential as means for solving the problems. For the sake of clarity of explanation, the following description and drawings have been appropriately omitted and simplified. In each drawing, the same elements are denoted by the same reference numerals, and duplicate explanations are omitted as necessary.

[0015] (Embodiment 1) A brain wave detection system according to Embodiment 1 will be described. The brain wave detection system of this embodiment uses the measured time-series signal of the brain wave as a measurement signal, and compares it with the time-series signal of the pulse wave measured in the measurement time zone corresponding to the time-series signal of the brain wave as a reference signal, thereby performing a process of reducing the noise of the measured brain wave. That is, by using the pulse wave signal, the signal-to-noise ratio as the amplitude of the brain wave signal is improved. First, the <configuration of the brain wave detection system> will be described below. Then, the <brain wave detection method> using the brain wave detection system will be described.

[0016] <Configuration of the brain wave detection system> FIG. 1 is a diagram illustrating the configuration of a brain wave detection system according to Embodiment 1. As shown in FIG. 1, the brain wave detection system 1 includes a brain wave measuring device 10, a pulse wave measuring device 20, and a noise reduction processing device 50.

[0017] The brain wave measuring device 10 measures the brain waves of a living body. Specifically, the brain wave measuring device 10 measures the time-series signal of the brain wave. The living body is, for example, a human being who is a subject. The brain wave measuring device 10 includes a sensor 11 and a main body 12. The sensor 11 is, for example, attached to the scalp of a human head and senses information on human brain waves from outside the living body. Information on human brain waves is, for example, voltage. Note that the sensor 11 may sense current, magnetic field, etc. in addition to voltage as information on human brain waves. The sensor 11 is non-invasively attached to the living body.

[0018] The sensor 11 outputs the detected electroencephalogram information to the main body 12 of the electroencephalograph 10. The main body 12 of the electroencephalograph 10 measures the time change of voltage or the like output from the sensor 11. The sensor 11 is connected to the main body 12 by a wired or wireless communication line. Further, the main body 12 is connected to the noise reduction processing device 50 by a wired or wireless communication line. The main body 12 outputs the measured electroencephalogram to the noise reduction processing device 50.

[0019] The pulse wave measuring device 20 measures the pulse wave of a living body. Specifically, the pulse wave measuring device 20 measures the time series signal of the pulse wave. The pulse wave is the waveform information of the living body formed by the pulse interval, the blood ejection volume, and the physical characteristics of the blood vessels. The pulse wave measuring device 20 includes a sensor 21 and a main body 22. The sensor 21 is, for example, attached to the skin of a human neck or supraclavicular region, and measures the information of the human pulse wave from the outside of the living body. Specifically, the sensor 21 may be arranged in at least one of the vicinity of the bifurcation of the left and right carotid arteries and the vicinity of the bifurcation of the subclavian artery. The information of the human pulse wave is, for example, the pulse pressure. Note that the sensor 21 may sense, in addition to the pulse pressure, the blood flow volume or the like as the information of the human pulse wave. For example, it may be sensed by a photoelectric plethysmogram. The sensor 21 is non-invasively attached to the living body.

[0020] The electroencephalogram detection system 1 of the present embodiment is a technology that utilizes the state in which the electroencephalogram and the pulse wave are correlated. Therefore, the positions for acquiring the pulse wave are preferably the bifurcation of the left and right carotid arteries and the bifurcation of the subclavian artery. The major blood vessels flowing into the brain are branched at the bifurcation of the carotid artery and the bifurcation of the subclavian artery. There are the frontal lobe, the parietal lobe, etc. at the end of the branch. The brain regions to be observed are, for example, the frontal lobe and the parietal lobe. Therefore, it is desirable to measure the pulse wave at the bifurcation of the left and right carotid arteries and the bifurcation of the subclavian artery. Thereby, the pulse wave related to the blood flow of the internal carotid artery and the vertebral artery can be measured.

[0021] The sensor 21 outputs the sensed pulse wave information to the main body 22 of the pulse wave measuring device 20. The main body 22 of the pulse wave measuring device 20 measures the time change such as the pulse pressure output from the sensor 21. The sensor 21 is connected to the main body 22 by a wired or wireless communication line. Further, the main body 22 is connected to the noise reduction processing device 50 by a wired or wireless communication line. The main body 22 outputs the measured pulse wave to the noise reduction processing device 50.

[0022] The noise reduction processing device 50 uses the measured time series signal of the brain wave as the measurement signal. Also, the noise reduction processing device 50 uses the time series signal of the pulse wave measured in the measurement time zone corresponding to the time series signal of the brain wave as the reference signal. Then, the noise reduction processing device 50 performs a process of reducing the noise of the measured brain wave by comparing the measurement signal and the reference signal. Specifically, the noise reduction processing device 50 separates the brain wave signal and the pulse wave signal for each frequency band by a band-pass filter. Thereafter, the noise reduction processing device 50 delays the pulse wave signal in time and adjusts it to correspond to the measurement time zone of the brain wave. Also, the noise reduction processing device 50 adjusts the amplitude component of the brain wave and the amplitude component of the pulse wave. Then, the noise reduction processing device 50 performs a process of reducing the noise of the measured brain wave by performing a lock-in amplifier process using the measurement signal and the reference signal.

[0023] FIG. 2 is a block diagram illustrating the noise reduction processing device 50 according to Embodiment 1. As shown in FIG. 2, the noise reduction processing device 50 includes a control unit 50a, a communication unit 50b, a storage unit 50c, an interface unit 50d, a waveform information acquisition unit 51, a filtering unit 52, a time series adjustment unit 53, a gain adjustment unit 54, and a processing unit 55. The control unit 50a, the communication unit 50b, the storage unit 50c, the interface unit 50d, the waveform information acquisition unit 51, the filtering unit 52, the time series adjustment unit 53, the gain adjustment unit 54, and the processing unit 55 each have functions as control means, communication means, storage means, interface means, waveform information acquisition means, filtering means, time series adjustment means, gain adjustment means, and processing means.

[0024] The noise reduction processing device 50 is an information processing device including a computer. The control unit 50a includes a processor such as a CPU (Central Processing Unit), MPU (Micro Processing Unit), ECU (Electronic Control Unit), FPGA (Field-Programmable Gate Array), or ASIC (Application Specific Integrated Circuit). The control unit 50a has a function as an arithmetic unit that performs control processing, arithmetic processing, and the like. Further, the control unit 50a controls the operations of each component such as the communication unit 50b, the storage unit 50c, the interface unit 50d, the waveform information acquisition unit 51, the filtering unit 52, the time series adjustment unit 53, the gain adjustment unit 54, and the processing unit 55.

[0025] Each component of the noise reduction processing device 50 can be realized, for example, by executing a program under the control of the control unit 50a. More specifically, each component can be realized by the control unit 50a executing a program stored in the storage unit 50c. Further, by recording the necessary program on an arbitrary non-volatile recording medium and installing it as needed, each component may be realized. Also, each component is not limited to being realized by software by a program, and may be realized by any combination of hardware, firmware, and software.

[0026] The communication unit 50b receives the time series signals of the brain waves and pulse waves measured by the electroencephalograph 10 and the plethysmograph 20 from the electroencephalograph 10 and the plethysmograph 20.

[0027] The storage unit 50c may have a storage device such as a memory or a hard disk, for example. The storage device is, for example, a ROM (Read Only Memory) or a RAM (Random Access Memory), etc. The storage unit 50c has a function for storing a control program, an arithmetic program, etc. executed by the control unit 50a. Also, the storage unit 50c has a function for temporarily storing processing data, etc. The storage unit 50c may store the time series signals of the brain waves and pulse waves received by the communication unit 50b.

[0028] The interface unit 50d is, for example, a user interface. The interface unit 50d has an input device such as a keyboard, a touch panel, or a mouse, and an output device such as a display or a speaker. The interface unit 50d receives an operation of data input by a user (operator, etc.) and outputs information to the user.

[0029] The waveform information acquisition unit 51 acquires the time series signals of the brain waves and pulse waves that the communication unit 50b has acquired from the electroencephalograph 10 and the photoplethysmograph 20. The filtering unit 52 filters the measured brain waves and the measured pulse waves in at least one frequency band. Therefore, the noise reduction processing device 50 may perform noise reduction processing using the filtered time series signal of the brain waves and the filtered time series signal of the pulse waves.

[0030] The time series adjustment unit 53 adjusts the time series between the time series signal of the brain waves and the time series signal of the pulse waves. Specifically, the time series adjustment unit 53 acquires the time series signal of the pulse waves acquired in the measurement time band corresponding to the time series signal of the brain waves. In order to make the measurement time bands of the brain waves and the pulse waves correspond, for example, the delay time of the pulse waves corresponding to the brain waves may be measured in advance, or a signal serving as a landmark may be selected.

[0031] The gain adjustment unit 54 adjusts the gain (amplitude) between the time-series signal of the electroencephalogram and the time-series signal of the pulse wave. The processing unit 55 uses the measured time-series signal of the electroencephalogram as a measurement signal and compares it with the time-series signal of the pulse wave acquired in the measurement time zone corresponding to the time-series signal of the electroencephalogram as a reference signal. Thereby, the processing unit 55 performs a process of reducing the noise of the measured electroencephalogram. For example, the processing unit 55 performs a lock-in amplifier process using the measured electroencephalogram as a measurement signal and the measured pulse wave as a reference signal.

[0032] <Electroencephalogram Detection Method> Next, the electroencephalogram detection method will be described. FIG. 3 is a flowchart diagram illustrating the electroencephalogram detection method according to Embodiment 1. As shown in FIG. 3, the electroencephalogram detection method includes an electroencephalogram measurement step (step S11) for measuring an electroencephalogram, a pulse wave measurement step (step S12) for measuring a pulse wave, and a noise reduction processing step (step S13) for performing a process of reducing the noise of the electroencephalogram.

[0033] As shown in step S11 of FIG. 3, an electroencephalogram is measured. For example, the electroencephalogram measuring device 10 measures the electroencephalogram of a living body. Also, as shown in step S12, a pulse wave is measured. For example, the pulse wave measuring device 20 measures the pulse wave of a living body. It is preferable that step S11 and step S12 be performed simultaneously. However, since the time series of the electroencephalogram and the pulse wave is adjusted in the time series adjustment step (step S23) described later, the order of execution of step S11 and step S12 is not limited. That is, step S11 may be performed before step S12 or after step S12.

[0034] FIG. 4 is a graph illustrating a time-series signal of a pulse wave according to Embodiment 1, where the horizontal axis represents time and the vertical axis represents intensity. As shown in FIG. 4, the pulse wave measuring device 20 measures a time-series signal of the pulse wave. The pulse wave measuring device 20 preferably measures the pulse wave at least at any one of the carotid artery bifurcation and the subclavian artery bifurcation on the left and right sides of the subject. When a photoplethysmograph is applied to the carotid artery bifurcation and the subclavian artery bifurcation, a pulse wave can be observed. This is considered to be approximately proportional to the amount of blood flowing into the brain. Therefore, the pulse wave measured from the carotid artery bifurcation and the subclavian artery bifurcation is closely related to the brain wave. Thus, the noise of the brain wave can be reduced by a noise reduction process using the pulse wave as a reference signal.

[0035] A time-series signal of the pulse wave is acquired via the sensor 21 disposed at the carotid artery bifurcation and the subclavian artery bifurcation. For example, the pulse wave of a subject in a steady state is measured for several tens of minutes to several hours. The sampling frequency is, for example, 500 Hz. Note that the sampling frequency can be any frequency between 10 and 1000 Hz. The pulse wave signal obtained in this way has a repetitive waveform as shown in FIG. 4. The pulse wave measuring device 20 outputs the acquired pulse wave to the noise reduction processing device 50.

[0036] Next, as shown in step S13, a process of reducing the noise of the measured brain wave is performed by comparing the measured time-series signal of the brain wave as a measurement signal with the time-series signal of the pulse wave acquired in the measurement time zone corresponding to the measured time-series signal of the brain wave as a reference signal.

[0037] <Noise reduction processing method> Next, the noise reduction processing method of step S13 described above will be explained. FIG. 5 is a flowchart illustrating the noise reduction processing method performed by the noise reduction processing apparatus 50 according to Embodiment 1. As shown in FIG. 5, the noise reduction processing method includes a waveform information acquisition step (step S21) of acquiring brain waves and pulse waves, a filtering step (step S22) of filtering the acquired waveform information in at least one frequency band, a time series adjustment step (step S23) of delaying one of the waveform information in the filtered frequency band to match the time series, a gain adjustment step (step S24) of adjusting the gain of one of the waveform information, and a noise reduction processing step (step S25) of performing noise reduction processing on the brain waves. Each step will be explained below.

[0038] <Waveform information acquisition step> The waveform information acquisition unit 51 of the noise reduction processing apparatus 50 acquires the brain waves and pulse waves received by the communication unit 50b from the electroencephalograph 10 and the pulse wave meter 20. The waveform information acquisition unit 51 acquires, for example, the time series signals of the brain waves and pulse waves from the communication unit 50b.

[0039] <Filtering step> FIG. 6 is a graph illustrating the signal waveforms obtained by filtering the pulse waves according to Embodiment 1 in each frequency band. The horizontal axis represents time, and the vertical axis represents intensity. In FIG. 6, HF, LF, VLF1, VLF2, VHF1, and VHF2 are shown as each frequency band. Also, the pulse wave waveform before filtering is shown in FIG. 6. FIG. 7 is a diagram illustrating each frequency band when filtering the waveform information related to the living body according to Embodiment 1. FIG. 7 also shows the approximate center frequencies in several frequency bands.

[0040] As shown in FIG. 7, the filtering unit 52 of the noise reduction processing apparatus 50 filters the acquired brain waves and pulse waves in at least one frequency band. Specifically, the filtering unit 52 filters the brain waves and pulse waves acquired by the waveform information acquisition unit 51 for each frequency band.

[0041] As shown in FIGS. 6 to 7, as the frequency band to be filtered, for example, VLF2 (0.004 to 0.015 Hz), VLF1 (0.015 to 0.04 Hz), LF (0.04 to 0.15 Hz), HF (0.15 to 0.4 Hz), VHF2 (0.4 to 1.5 Hz), VHF1 (1.5 to 4 Hz), UHF2 (4 to 15 Hz), and UHF1 (15 to 40 Hz) etc. are selected. Here, there are neural activity networks and activity centers in the brain that act in various frequency bands. For example, HF is the respiratory center and is the respiratory variation band of the inter-beat interval. Also, LF is the blood pressure center and is the blood pressure variation band. The bands of VHF2, VHF1, UHF2 and UHF1 include the δ wave (0.4 to 4.0 Hz) of the sleep network in the electroencephalogram, the θ wave (4.0 to 8.0 Hz) of the meditation network, the α wave (8.0 to 12.0 Hz) of the relaxation network, the β wave (12.0 to 30.0), and the γ wave (30 Hz to ) bands. By observing the cerebral blood flow and electroencephalogram for each of these specific frequency bands, brain activity can be precisely estimated.

[0042] FIG. 8 is a graph illustrating the signal waveform obtained by Fourier-transforming the pulse wave according to Embodiment 1. The horizontal axis represents the frequency, and the vertical axis represents the spectral power. As shown in FIG. 8, after Fourier-transforming the pulse wave such as in FIG. 4, it may be filtered for each frequency band. Then, an inverse Fourier transform may be performed.

[0043] <Time series adjustment step> The time series adjustment unit 53 of the noise reduction processing device 50 adjusts the measurement time zones of the time series signals of the electroencephalograms in each filtered frequency band and the time series signals of the pulse waves in each filtered frequency band. For example, one of the electroencephalogram and the pulse wave is delayed with respect to the other. In this way, the time series adjustment unit 53 acquires the time series signal of the pulse wave acquired in the measurement time zone corresponding to the time series signal of the electroencephalogram.

[0044] <Gain adjustment step> The gain adjustment unit 54 of the noise reduction processing device 50 adjusts the gains (amplitudes) of the time series signals of the brain waves in each filtered frequency band and the time series signals of the pulse waves in each filtered frequency band. For example, the gain of one of the brain waves and the pulse waves is increased with respect to the other. In this way, the gain adjustment unit 54 adjusts the gains of the brain waves and the pulse waves.

[0045] Figures 9 and 10 are graphs illustrating the brain waves and pulse waves according to Embodiment 1. The horizontal axis represents time, and the vertical axis represents intensity. The brain waves are indicated by eeg, and the pulse waves are indicated by bvp. Figures 9 and 10 show the brain waves of the left brain and the pulse waves of the left carotid artery bifurcation. Figure 10 shows the brain waves and pulse waves filtered in the θ wave band as the frequency band, with the time series and gain adjusted.

[0046] Figures 11 and 12 are graphs illustrating the brain waves and pulse waves according to Embodiment 1. The horizontal axis represents time, and the vertical axis represents intensity. Figures 11 and 12 show the brain waves of the right brain and the pulse waves of the right carotid artery bifurcation. Figure 12 shows the brain waves and pulse waves filtered in the θ wave band as the frequency band, with the time series and gain adjusted.

[0047] Figures 13 and 14 are graphs illustrating the brain waves and pulse waves according to Embodiment 1. The horizontal axis represents time, and the vertical axis represents intensity. Figures 13 and 14 show the brain waves of the left brain and the pulse waves of the left carotid artery bifurcation. Figure 14 shows the brain waves and pulse waves filtered in the α wave band as the frequency band, with the time series and gain adjusted.

[0048] Figures 15 and 16 are graphs illustrating the brain waves and pulse waves according to Embodiment 1. The horizontal axis represents time, and the vertical axis represents intensity. Figures 15 and 16 show the brain waves of the right brain and the pulse waves of the right carotid artery bifurcation. Figure 16 shows the brain waves and pulse waves filtered in the α wave band as the frequency band, with the time series and gain adjusted.

[0049] Figures 17 and 18 are graphs illustrating the brain waves and pulse waves according to Embodiment 1. The horizontal axis represents time, and the vertical axis represents intensity. Figures 17 and 18 show the brain waves of the left brain and the pulse waves of the left carotid artery bifurcation. Figures 17 and 18 show the brain waves and pulse waves that are filtered in the bands of LF, HF, δ wave, θ wave, α wave, β wave, and γ wave as frequency bands, and the time series and gain are adjusted. Different gains of the θ wave are also shown.

[0050] Figures 19 and 20 are graphs illustrating the brain waves and pulse waves according to Embodiment 1. The horizontal axis represents time, and the vertical axis represents intensity. Figures 19 and 20 show the brain waves of the right brain and the pulse waves of the right carotid artery bifurcation. Figures 19 and 20 show the brain waves and pulse waves that are filtered in the bands of LF, HF, δ wave, θ wave, α wave, β wave, and γ wave as frequency bands, and the time series and gain are adjusted. Different gains of the θ wave are also shown.

[0051] <Noise reduction processing> The processing unit 55 of the noise reduction processing device 50 reduces the noise of the measured brain waves by comparing the measured brain waves as measurement signals and the measured pulse waves as reference signals. Specifically, the processing unit 55 reduces the noise of the measured brain waves by performing lock-in amplifier processing. When the processing unit 55 performs the process of reducing the noise of the measured brain waves, the time series signal of the filtered brain waves is used as the measurement signal, and the time series signal of the filtered pulse waves acquired in the measurement time zone corresponding to the time series signal of the brain waves is used as the reference signal for comparison, thereby performing the above process of reducing the noise of the brain waves. In that case, the brain waves and pulse waves with the gain adjusted may be used. The processing unit 55 can reduce the noise of the brain waves shown in FIGS. 9-20 by performing noise reduction processing. In this way, the brain wave detection system 1 can detect brain waves with reduced noise compared to the waveforms of the brain waves shown in FIGS. 9-20.

[0052] Next, the effects of the present embodiment will be described. The electroencephalogram detection system 1 of the present embodiment performs a process of reducing the noise of the electroencephalogram by using the time-series signal of the electroencephalogram as a measurement signal and comparing the time-series signal of the pulse wave as a reference signal. Therefore, the noise included in the measured electroencephalogram can be reduced.

[0053] In the electroencephalogram measurement up to now, it has been difficult to improve the signal-to-noise ratio due to the influence of environmental noise and biological noise such as electromyogram associated with body movement. The electroencephalogram detection system 1 of the present embodiment uses not only the electroencephalogram but also the pulse wave. Originally, the measured electroencephalogram is based on the temporal change of the potential generated by the activities of nerve cells in the brain. Energy is required for the activities of nerve cells in the brain. Oxygen and glucose, which are such energies, are supplied to the active regions in the brain by blood. Therefore, when the activities of nerve cells in the brain become active, the blood volume to the active region increases. Therefore, the electroencephalogram and the blood flow volume (pulse wave) should be synchronized. Therefore, the electroencephalogram can be used as a measurement signal and the pulse wave can be used as a reference signal for comparison, and the noise included in the electroencephalogram can be reduced.

[0054] In addition, the noise reduction processing device 50 filters the measured electroencephalogram and pulse wave for each frequency band. Therefore, the noise of the electroencephalogram can be reduced for each frequency band. For example, the noise reduction processing device 50 performs lock-in amplifier processing using the measured signal and the reference signal obtained by band-pass filtering the electroencephalogram and the pulse wave in the δ, θ, α, β, and γ bands. Thereby, the noise reduction processing device 50 can reduce biological noise such as environmental noise and electromyogram, and can detect an electroencephalogram with a very high signal-to-noise ratio.

[0055] Note that the present disclosure is not limited to the above-described embodiment, and can be appropriately changed without departing from the gist. For example, the following noise reduction processing method and a noise reduction processing program for causing a computer to execute the noise reduction processing method are also included in the scope of the technical idea of the present embodiment.

[0056] (Appendix 1) A step of measuring an electroencephalogram, A step of measuring a pulse wave; Using the time series signal of the measured brain wave as a measurement signal, and comparing it with the time series signal of the pulse wave measured in the measurement time zone corresponding to the time series signal of the brain wave as a reference signal, to perform a process of reducing the noise of the measured brain wave; A brain wave detection method comprising the above. (Appendix 2) In the step of performing the process of reducing the noise of the measured brain wave, Filter the measured brain wave and the measured pulse wave in at least one frequency band, use the time series signal of the filtered brain wave as the measurement signal, and compare it with the time series signal of the filtered pulse wave measured in the measurement time zone corresponding to the time series signal of the brain wave as the reference signal, thereby performing the above process of reducing the noise of the brain wave. The brain wave detection method according to Appendix 1. (Appendix 3) In the step of measuring the pulse wave, Measure the pulse wave at least at either the carotid artery bifurcation or the subclavian artery bifurcation. The brain wave detection method according to Appendix 1 or 2. (Appendix 4) In the step of performing the process of reducing the noise of the measured brain wave, Perform lock-in amplifier processing to perform the above process of reducing the noise of the measured brain wave. The brain wave detection method according to any one of Appendices 1 to 3. (Appendix 5) A noise reduction processing program that causes a computer to execute a procedure of using the time series signal of the measured brain wave as a measurement signal, and comparing it with the time series signal of the pulse wave measured in the measurement time zone corresponding to the time series signal of the brain wave as a reference signal, to perform a process of reducing the noise of the measured brain wave. (Appendix 6) In the procedure of performing the process of reducing the noise of the measured brain wave, Filter the measured brain waves and the measured pulse waves in at least one frequency band, use the time series signal of the filtered brain waves as the measurement signal, and compare the time series signal of the filtered pulse waves measured in the measurement time band corresponding to the time series signal of the brain waves as the reference signal, so as to cause the computer to execute the noise reduction process for reducing the noise of the brain waves, which is the noise reduction processing program described in Appendix 5. (Appendix 7) The pulse wave is the pulse wave measured at least at either the carotid artery bifurcation or the subclavian artery bifurcation. The noise reduction processing program described in Appendix 5 or 6. (Appendix 8) In the procedure for performing the process of reducing the noise of the measured brain waves, By performing lock-in amplifier processing, cause the computer to execute the process of reducing the noise of the measured brain waves, which is the noise reduction processing program described in any one of Appendices 5 to 7.

Explanation of symbols

[0057] 1 Electroencephalogram detection system 10 Electroencephalogram measuring device 11 Sensor 12 Main body part 20 Pulse wave measuring device 21 Sensor 22 Main body part 50 Noise reduction processing device 50a Control part 50b Communication part 50c Storage part 50d Interface part 51 Waveform information acquisition part 52 Filtering part 53 Time series adjustment part 54 Gain adjustment part 55 Processing part

Claims

1. An electroencephalograph for measuring brain waves, A plethysmograph for measuring pulse waves, A noise reduction processing device that uses the time series signal of the measured brain waves as a measurement signal, and compares the time series signal of the pulse waves measured in the measurement time zone corresponding to the time series signal of the brain waves as a reference signal, thereby performing a process of reducing the noise of the measured brain waves, An electroencephalogram detection system comprising the above.

2. The noise reduction processing device filters the measured brain waves and the measured pulse waves in at least one frequency band, uses the time series signal of the filtered brain waves as the measurement signal, and measures in the measurement time zone corresponding to the time series signal of the brain waves. By comparing the time series signal of the filtered pulse waves as the reference signal, the above process of reducing the noise of the brain waves is performed, The electroencephalogram detection system according to Claim 1.

3. The plethysmograph measures the pulse wave at at least one of the carotid artery bifurcation and the subclavian artery bifurcation, The electroencephalogram detection system according to Claim 1.

4. The noise reduction processing device performs a lock-in amplifier process to perform the above process of reducing the noise of the measured brain waves, The electroencephalogram detection system according to Claim 1.

5. A step of measuring brain waves, A step of measuring pulse waves, A step of using the time series signal of the measured brain waves as a measurement signal, and comparing the time series signal of the pulse waves measured in the measurement time zone corresponding to the time series signal of the brain waves as a reference signal, thereby performing a process of reducing the noise of the measured brain waves, An electroencephalogram detection method comprising the above.

6. A noise reduction processing program that causes a computer to execute a procedure for reducing the noise of the measured brain waves by comparing the time series signal of the measured brain waves as a measurement signal and the time series signal of the pulse waves measured in the measurement time zone corresponding to the time series signal of the brain waves as a reference signal.

Images