Electroencephalogram signal processing method, device, program, and vehicle seat

Abstract

To reduce a variety of artifacts included in a brain wave signal without complicating a structure.SOLUTION: A brain wave signal of a subject is acquired (step 100), abnormal value reduction processing that reduces abnormal value whose amplitude is threshold or larger with respect to the acquired brain wave signal is performed (steps 102 to 106), first filter processing is performed to multiply a movement average filter with the brain wave signal via the abnormal value reduction processing is performed (step 108), DC off-set reduction processing reducing a DC off-set component is performed with respect to the brain wave signal via the first filter processing (step 110), and second filter processing to multiply a high-pass filter and a low-pass filter with the brain wave signal via the DC off-set reduction processing is performed (step 112).SELECTED DRAWING: Figure 4

Description

[Technical field]

[0001] The present disclosure relates to an electroencephalogram signal processing method, an electroencephalogram signal processing device, an electroencephalogram signal processing program, and a vehicle seat. [Background technology]

[0002] In the technology described in Patent Document 1, an electroencephalogram signal generated by a driver while the vehicle is running is acquired by an electroencephalogram measuring device. Furthermore, the occurrence of a biological artifact (an artifact due to body movement) caused by, for example, a head shaking movement of the driver is detected, for example, by a camera or by detecting an abnormally large electroencephalogram voltage. Furthermore, a filter corresponding to the detected biological artifact is selected, for example, from a memory of a controller. Then, using the selected filter, a true electroencephalogram signal is acquired from the electroencephalogram signal acquired by the electroencephalogram measuring device, with the influence of the biological artifact removed. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2017-42261 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, the technology described in Patent Document 1 has a drawback in that it requires a camera or the like for detecting body movements, making the configuration complicated.In addition, the technology described in Patent Document 1 has a drawback in that it can only reduce predetermined biological artifacts (artifacts for which a corresponding filter is created in advance and stored in memory) among various artifacts contained in an EEG signal.

[0005] For example, the technology described in Patent Document 1 cannot reduce artifacts caused by body movements when unexpected body movements occur, such as simultaneous blinking, yawning, and head shaking, because a filter corresponding to the artifacts caused by the body movements is not stored. Also, for example, the technology described in Patent Document 1 does not take into account artifacts caused by the environment contained in the EEG signal or artifacts during communication, and cannot reduce artifacts caused by the environment or artifacts during communication.

[0006] The present disclosure has been made in consideration of the above-mentioned facts, and aims to provide an EEG signal processing method, an EEG signal processing device, an EEG signal processing program, and a vehicle seat that can reduce various artifacts contained in EEG signals without complicating the configuration. [Means for solving the problem]

[0007] The electroencephalogram signal processing method according to a first aspect acquires an electroencephalogram signal from a subject, performs an outlier reduction process on the acquired electroencephalogram signal to reduce outliers whose amplitude is equal to or greater than a threshold, performs a first filter process on the electroencephalogram signal that has undergone the outlier reduction process by applying a smoothing filter, performs a DC offset reduction process on the electroencephalogram signal that has undergone the first filter process by reducing a DC offset component, and performs a second filter process on the electroencephalogram signal that has undergone the DC offset reduction process by applying a high-pass filter and a low-pass filter.

[0008] In the first aspect, the electroencephalogram signal of the subject is subjected to the abnormal value reduction process, the first filter process, the DC offset reduction process, and the second filter process in this order. As a result, as is clear from the results of the experiment conducted by the inventors of the present application (described later), various artifacts contained in the electroencephalogram signal can be reduced. In addition, the first aspect does not require a camera or the like for detecting the body movement of the subject, and therefore does not lead to a complicated configuration.

[0009] In the second aspect, in the first aspect, the abnormal value reduction process is performed by replacing the value of data whose amplitude is equal to or greater than a threshold value with the intermediate value of the data before and after the data.

[0010] According to the second aspect, it is possible to realize, through a simple process, the abnormal value reduction process for reducing abnormal values ​​whose amplitude is equal to or greater than a threshold value.

[0011] In a third aspect, in the first or second aspect, a significant frequency band in the power spectrum of the EEG signal that has undergone the second filter processing is determined, and the subject's state of consciousness is determined from the determined significant frequency band.

[0012] According to the present disclosure, various artifacts contained in an EEG signal can be reduced, and therefore, according to the third aspect, significant frequency bands can be accurately determined from an EEG signal in which various artifacts have been reduced, thereby enabling the subject's state of consciousness to be accurately determined.

[0013] In a fourth aspect, in any one of the first to third aspects, an electroencephalogram signal is acquired from a subject in a non-resting state.

[0014] According to the fourth aspect, even if the subject is not in a resting state, it is possible to obtain an electroencephalogram signal of the subject with various artifacts reduced.

[0015] In a fifth aspect, in any one of the first to fourth aspects, an electroencephalogram signal is acquired from a subject who is driving a vehicle.

[0016] According to the fifth aspect, it is possible to obtain an electroencephalogram signal of a subject with various artifacts reduced, even when the subject is driving a vehicle.

[0017] A sixth aspect is any one of the first to fifth aspects, wherein the threshold value in the abnormal value reduction process is 150 μV or a value exceeding this value.

[0018] When the subject is not at rest, the magnitude of the artifact caused by body movement is 150 μV or more. In the sixth aspect, by setting the threshold value in the abnormal value reduction process to 150 μV or more, when the subject is not at rest, the artifact caused by body movement can be appropriately reduced.

[0019] A seventh aspect is, in any of the first to sixth aspects, to acquire first information related to artifacts caused by body movements of the subject, which are contained in the electroencephalogram signal of the subject, and set at least processing parameters of the abnormal value reduction processing based on the acquired first information.

[0020] The magnitude of artifacts due to the subject's body movements contained in the subject's electroencephalogram signal varies depending on the subject's personal constitution (e.g., how easily he or she sweats, etc.). In the seventh aspect, first information (e.g., information on the subject's constitution, etc.) related to artifacts due to the subject's body movements is acquired, and at least processing parameters of the abnormal value reduction process are set based on the acquired first information, so that artifacts due to body movements can be appropriately reduced.

[0021] The eighth aspect is, in any of the first to seventh aspects, obtaining second information related to environmental artifacts contained in the electroencephalogram signal of the subject, and setting processing parameters of the second filtering process based on the obtained second information.

[0022] The artifacts due to the environment contained in the electroencephalogram signal of the subject vary depending on the environment such as the arrangement of electromagnetic wave sources in the vicinity. In the eighth aspect, second information (e.g., ID of the surrounding environment) related to the artifacts due to the environment is acquired, and processing parameters of the second filtering process are set based on the acquired second information, so that the artifacts due to the environment can be appropriately reduced.

[0023] The electroencephalogram signal processing device of the ninth aspect includes an acquisition unit that acquires an electroencephalogram signal of a subject, an abnormal value reduction unit that performs an abnormal value reduction process on the electroencephalogram signal acquired by the acquisition unit to reduce abnormal values ​​whose amplitude is equal to or greater than a threshold, a first filter unit that performs a first filter process that applies a smoothing filter to the electroencephalogram signal that has undergone the abnormal value reduction process by the abnormal value reduction unit, a DC offset reduction unit that performs a DC offset reduction process that reduces a DC offset component on the electroencephalogram signal that has undergone the first filter process by the first filter unit, and a second filter unit that performs a second filter process that applies a high-pass filter and a low-pass filter to the electroencephalogram signal that has undergone the DC offset reduction process by the DC offset reduction unit.

[0024] According to the ninth aspect, similarly to the first aspect, various artifacts contained in the electroencephalogram signal can be reduced without complicating the configuration.

[0025] A vehicle seat according to a tenth aspect includes a seat body on which a driver of the vehicle sits, an electroencephalogram measuring unit that measures the electroencephalogram of the vehicle driver seated in the seat body and outputs an electroencephalogram signal, and an electroencephalogram signal processing device according to the ninth aspect that acquires the electroencephalogram signal output from the electroencephalogram measuring unit as the electroencephalogram signal of the subject.

[0026] According to the tenth aspect, similarly to the first aspect, it is possible to obtain an electroencephalogram signal with various artifacts reduced without complicating the configuration.

[0027] An EEG signal processing program according to an eleventh aspect causes a computer to execute processing including acquiring an EEG signal from a subject, performing an abnormal value reduction process on the acquired EEG signal to reduce abnormal values ​​whose amplitude is equal to or greater than a threshold, performing a first filter process on the EEG signal that has undergone the abnormal value reduction process by applying a smoothing filter to the EEG signal that has undergone the first filter process, performing a DC offset reduction process on the EEG signal that has undergone the first filter process by reducing a DC offset component, and performing a second filter process on the EEG signal that has undergone the DC offset reduction process by applying a high-pass filter and a low-pass filter to the EEG signal that has undergone the DC offset reduction process.

[0028] According to the eleventh aspect, similarly to the first aspect, various artifacts contained in the electroencephalogram signal can be reduced without complicating the configuration. Effect of the Invention

[0029] The present disclosure has an advantage of being able to reduce various artifacts contained in electroencephalogram signals without complicating the configuration. [Brief description of the drawings]

[0030] [Figure 1] 1 is a side view of a vehicle seat according to an embodiment; [Diagram 2] 2 is a block diagram showing a schematic configuration of the electroencephalogram signal processing ECU according to the first embodiment. FIG. [Diagram 3] FIG. 2 is a functional block diagram of the electroencephalogram signal processing ECU according to the first embodiment. [Figure 4] 4 is a flowchart showing electroencephalogram signal processing according to the first embodiment. [Diagram 5] FIG. 11 is a block diagram showing a schematic configuration of an electroencephalogram signal processing ECU according to a second embodiment. [Figure 6] 11 is a table showing an example of a processing parameter DB. [Figure 7] 11 is a table showing an example of a threshold DB. [Figure 8] FIG. 11 is a functional block diagram of an electroencephalogram signal processing ECU according to a second embodiment. [Figure 9] 10 is a flowchart showing electroencephalogram signal processing according to the second embodiment. [Figure 10] FIG. 1 is a diagram showing a 15 Hz sine wave used as a signal simulating a true electroencephalogram signal in an experiment conducted by the present inventors. [Figure 11] FIG. 11 is a diagram showing the power spectrum of the sine wave shown in FIG. [Figure 12] FIG. 2 is a diagram showing a pseudo-electroencephalogram signal created by the present inventors. [Figure 13] FIG. 13 is a diagram showing the power spectrum of the pseudo-electroencephalogram signal shown in FIG. 12. [Figure 14] 13 is a diagram showing the power spectrum of an electroencephalogram signal when only an outlier reduction process is performed on a pseudo-electroencephalogram signal. FIG. [Figure 15] FIG. 13 is a diagram showing the power spectrum of the electroencephalogram signal when only the first filter processing (moving average filter) is performed on the pseudo-electroencephalogram signal. [Figure 16] FIG. 13 is a diagram showing the power spectrum of the electroencephalogram signal when only DC offset reduction processing is performed on the pseudo-electroencephalogram signal. [Figure 17] FIG. 13 is a diagram showing the power spectrum of the electroencephalogram signal when only the second filtering process (high-pass filter and low-pass filter) is performed on the pseudo-electroencephalogram signal. [Figure 18] 13 is a diagram showing an electroencephalogram signal when an outlier reduction process is performed on a pseudo-electroencephalogram signal. FIG. [Figure 19] FIG. 19 is a diagram showing the power spectrum of the electroencephalogram signal shown in FIG. 18. [Figure 20] 19 is a diagram showing an electroencephalogram signal obtained by performing a first filtering process (moving average filter) on the electroencephalogram signal shown in FIG. 18. FIG. [Figure 21] FIG. 21 is a diagram showing the power spectrum of the electroencephalogram signal shown in FIG. 20. [Figure 22] FIG. 21 is a diagram showing an electroencephalogram signal when a DC offset reduction process is performed on the electroencephalogram signal shown in FIG. 20. [Figure 23] FIG. 23 is a diagram showing the power spectrum of the electroencephalogram signal shown in FIG. 22. [Figure 24] 23 is a diagram showing the electroencephalogram signal when second filtering (high-pass filter and low-pass filter) is performed on the electroencephalogram signal shown in FIG. 22. FIG. [Diagram 25] FIG. 25 is a diagram showing the power spectrum of the electroencephalogram signal shown in FIG. 24. [Figure 26] FIG. 13 is a diagram showing the power spectrum of an EEG signal when the pseudo-EEG signal is subjected to the following processing in the order of (3) DC offset reduction processing → (1) outlier reduction processing → (2) first filter processing → (4) second filter processing. [Figure 27]This is a line graph showing the power spectrum of an EEG signal when the pseudo-EEG signal is subjected to the following processing in the order of (4) second filter processing → (1) outlier reduction processing → (2) first filter processing → (3) DC offset reduction processing. [Figure 28] This is a line graph showing the power spectrum of an EEG signal when the pseudo-EEG signal is subjected to the following processing in the order of (2) first filter processing → (1) outlier reduction processing → (3) DC offset reduction processing → (4) second filter processing. [Figure 29] This is a line graph showing the power spectrum of an EEG signal when the pseudo-EEG signal is subjected to the following processes in order: (1) outlier reduction processing → (2) first filter processing → (4) second filter processing → (3) DC offset reduction processing. [Diagram 30] This is a diagram showing the power spectrum of an EEG signal when the pseudo-EEG signal is subjected to the following processing in the order of (2) first filter processing → (3) DC offset reduction processing → (4) second filter processing → (1) outlier reduction processing. [Diagram 31] This is a diagram showing the power spectrum of an EEG signal when the pseudo-EEG signal is subjected to the following processing in the order of (1) outlier reduction processing → (3) DC offset reduction processing → (4) second filter processing → (2) first filter processing. [Diagram 32] This is a diagram showing the power spectrum of an EEG signal when the pseudo-EEG signal is subjected to the following processing in the order of (1) outlier reduction processing → (3) DC offset reduction processing → (2) first filter processing → (4) second filter processing. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0031] Hereinafter, an example of an embodiment of the present disclosure will be described in detail with reference to the drawings.

[0032] [First embodiment] A vehicle seat 10 shown in Fig. 1 includes a seat body 12. The seat body 12 is provided in a driver's seat of a vehicle, and is seated by a vehicle driver 20, who is an example of a test subject. The driver 20 seated in the seat body 12 operates a steering wheel 22 and pedals (not shown) to drive the vehicle when the vehicle is traveling. In the first embodiment, the vehicle seat 10 is provided in a vehicle of a specific model.

[0033] The seat main body 12 includes a seat cushion portion 14, a seat back portion 16, and a headrest portion 18. The headrest portion 18 is attached to an upper end portion in the vertical direction of the vehicle of the seat back portion 16 so as to be slidable along the length direction of the seat back portion 16. The lower end portion in the vertical direction of the vehicle of the seat back portion 16 is attached to a rear end portion in the longitudinal direction of the vehicle of the seat cushion portion 14 via a rotation mechanism (not shown), and is rotatable relative to the seat cushion portion 14 about an axis along the width direction of the vehicle.

[0034] The vehicle seat 10 also includes an electroencephalogram measuring device 24 and an electroencephalogram signal processing ECU (Electronic Control Unit) 26. The electroencephalogram measuring device 24 is helmet-shaped and is worn on the head of the driver 20 of the vehicle who is seated in the seat body 12. The electroencephalogram measuring device 24, while worn on the head of the driver 20, measures the electroencephalogram of the driver 20 and outputs an electroencephalogram signal representing the measured electroencephalogram to the electroencephalogram signal processing ECU 26 via wireless communication. The electroencephalogram measuring device 24 is an example of an electroencephalogram measuring section.

[0035] The electroencephalogram signal processing ECU 26 is housed in the seat cushion section 14. As shown in Fig. 2, the electroencephalogram signal processing ECU 26 includes a CPU (Central Processing Unit) 28, a memory 30 such as a ROM (Read Only Memory) or a RAM (Random Access Memory), and a non-volatile storage section 32 such as a HDD (Hard Disk Drive) or an SSD (Solid State Drive). The electroencephalogram signal processing ECU 26 also includes a wireless communication section 34 for wirelessly communicating with the electroencephalogram measuring device 24, and an alarm section 36 having a voice output function and a vibration generation function. The CPU 28, the memory 30, the storage section 32, the wireless communication section 34, and the alarm section 36 are connected to each other via an internal bus 38 so as to be able to communicate with each other.

[0036] The storage unit 32 stores an electroencephalogram signal processing program 40. The electroencephalogram signal processing ECU 26 functions as each of the functional units shown in Fig. 3 when the electroencephalogram signal processing program 40 is read from the storage unit 32 and loaded into the memory 30, and the electroencephalogram signal processing program 40 loaded into the memory 30 is executed by the CPU 28. That is, the electroencephalogram signal processing ECU 26 functions as an acquisition unit 60, an abnormal value reduction unit 62, a first filter unit 64, a DC offset reduction unit 66, a second filter unit 68, a significant band determination unit 70, and a consciousness state determination unit 72. The electroencephalogram signal processing ECU 26 is an example of an electroencephalogram signal processing device.

[0037] The acquisition unit 60 acquires the electroencephalogram signal of the vehicle driver 20 from the electroencephalogram measuring device 24. The abnormal value reduction unit 62 performs an abnormal value reduction process on the electroencephalogram signal acquired by the acquisition unit 60, reducing abnormal values ​​whose amplitude is equal to or greater than a threshold. The first filter unit 64 performs a first filter process that applies a moving average filter to the electroencephalogram signal that has been subjected to the abnormal value reduction process by the abnormal value reduction unit 62. The moving average filter is an example of a smoothing filter. The DC offset reduction unit 66 performs a DC offset reduction process on the electroencephalogram signal that has been subjected to the first filter process by the first filter unit 64, reducing a DC offset component.

[0038] The second filter unit 68 performs second filtering processing by applying a high-pass filter and a low-pass filter to the EEG signal that has been subjected to the DC offset reduction processing by the DC offset reduction unit 66. The significant band determination unit 70 determines a significant frequency band in the power spectrum of the EEG signal that has been subjected to the second filtering processing by the second filter unit 68. The consciousness state determination unit 72 determines the consciousness state of the vehicle driver 20 from the significant frequency band determined by the significant band determination unit 70.

[0039] Next, as an operation of the first embodiment, electroencephalogram signal processing executed by the electroencephalogram signal processing ECU 26 according to the first embodiment while the driver 20 is driving the vehicle will be described with reference to FIG.

[0040] In step 100, the acquisition unit 60 acquires an electroencephalogram signal representing the electroencephalogram of the driver 20 for a predetermined period of time from the electroencephalogram measuring device 24 by wireless communication. When the driver 20 makes a body movement such as blinking or shaking his / her head, an artifact caused by the body movement is added to the electroencephalogram signal. In addition, electromagnetic wave sources (e.g., harnesses, car navigation devices, etc.) are present around the vehicle seat 10 provided in the driver's seat of the vehicle, and the above-mentioned electroencephalogram signal is added with an artifact caused by the environment depending on the arrangement of the electromagnetic wave sources present in the vicinity. In addition, when wireless communication is performed between the electroencephalogram measuring device 24 and the electroencephalogram signal processing ECU 26, an artifact caused by the communication is also added to the above-mentioned electroencephalogram signal.

[0041] In step 102, the abnormal value reduction unit 62 determines whether or not the EEG signal acquired by the acquisition unit 60 includes a portion whose amplitude is equal to or greater than the threshold voltage. In this step 102, it is determined whether or not the EEG signal includes a portion that includes artifacts due to body movement. In this embodiment, since the EEG of the driver 20 who is not at rest, more specifically, who is operating the vehicle, is acquired, the magnitude of the artifacts due to body movement added to the EEG signal becomes larger than when the subject is at rest. For this reason, in the first embodiment, the threshold voltage is preset to 150 μV or a value greater than that (for example, 200 μV).

[0042] If the determination in step 102 is positive, the process proceeds to step 104. In step 104, the abnormal value reduction unit 62 removes data of a portion of the electroencephalogram signal acquired by the acquisition unit 60, the data having an amplitude equal to or greater than the threshold voltage. Then, in step 106, the abnormal value reduction unit 62 interpolates the data removed in step 104 with the median value of the data existing before and after the removed data (an intermediate value other than the median value may be used), and the process proceeds to step 108. Since the amplitude of artifacts due to body movement is large, the artifacts due to body movement are reduced first in steps 102 to 106 (one example of the abnormal value reduction process by the abnormal value reduction unit 62) as described above. If the determination in step 102 is negative, steps 104 and 106 are skipped and the process proceeds to step 108.

[0043] In step 108, the first filter unit 64 performs a first filter process in which a moving average filter is applied to the EEG signal that has been subjected to the abnormal value reduction process by the abnormal value reduction unit 62. In the first filter process, high frequency components are suppressed by smoothing the change in the EEG signal. Note that the section of the moving average filter is set in advance according to artifacts caused by body movements that are added to the EEG signal in a specific vehicle model in which the vehicle seat 10 is installed.

[0044] In step 110, the DC offset reduction section 66 performs a DC offset reduction process to reduce a DC offset component on the electroencephalogram signal that has been subjected to the first filtering process by the first filtering section 64. In the DC offset reduction process, low frequency components are mainly suppressed in order to reduce artifacts during communication.

[0045] In step 112, the second filter unit 68 performs a second filtering process in which the electroencephalogram signal that has been subjected to the DC offset reduction process by the DC offset reduction unit 66 is filtered through a high-pass filter and a low-pass filter. Note that the cutoff frequency of the high-pass filter and the cutoff frequency of the low-pass filter are preset in accordance with the frequency band of artifacts caused by the environment that are added to the electroencephalogram signal in a specific vehicle model in which the vehicle seat 10 is installed.

[0046] The filter waveform in the second filtering process is mountain-shaped, and even if the second filtering process is performed, artifacts that cannot be completely removed remain near the cutoff frequency of the filter. In this embodiment, in order to minimize the artifacts that cannot be completely removed, the first filtering process by the first filtering unit 64 and the DC offset reduction process by the DC offset reduction unit 66 are performed in advance.

[0047] In this manner, in this embodiment, the electroencephalogram signal of the driver 20 is subjected to the abnormal value reduction process by the abnormal value reduction section 62, the first filtering process by the first filtering section 64, the DC offset reduction process by the DC offset reduction section 66, and the second filtering process by the second filtering section 68 in that order. This makes it possible to obtain an electroencephalogram signal in which various artifacts have been reduced, as shown in Fig. 25 as an example.

[0048] In step 114, the significant band determination section 70 performs FFT (Fast Fourier Transform) on the EEG signal from which various artifacts have been reduced to obtain a power spectrum, and performs significant band determination processing to determine significant frequency bands in the obtained power spectrum. This significant band determination processing includes a method of determining a frequency band in which a maximum peak occurs on the power spectrum of the EEG signal as significant, and a method of integrating the power spectrum of the EEG signal for each frequency band, such as α waves, β waves, δ waves, and θ waves, and determining a frequency band in which the integration result shows a maximum value as significant. Either method may be applied in step 114.

[0049] In step 116, the consciousness state determination unit 72 performs consciousness state determination processing to determine the consciousness state of the vehicle driver 20 from the significant frequency band determined by the significant band determination unit 70, and to warn the driver 20 as necessary. Specifically, for example, when the significant frequency band determined by the significant band determination unit 70 is a frequency band corresponding to θ waves, the consciousness state determination unit 72 determines that the driver 20 is in a drowsy consciousness state, and causes the notification unit 36 ​​to output a warning sound or a warning message and generate a vibration. This makes it possible to wake up the driver 20.

[0050] In step 118, the EEG signal processing ECU 26 determines whether or not the driving operation by the driver 20 has been completed. If the determination in step 118 is negative, the process returns to step 100, and steps 100 to 118 are repeated until the determination in step 118 is positive. When the driving operation by the driver 20 has been completed, the determination in step 118 is positive, and the EEG signal processing is terminated.

[0051] As described above, in the first embodiment, an electroencephalogram signal of a subject is acquired, and an abnormal value reduction process is performed on the acquired electroencephalogram signal to reduce abnormal values ​​whose amplitude is equal to or greater than a threshold. A first filter process is performed on the electroencephalogram signal that has been subjected to the abnormal value reduction process to apply a smoothing filter. A DC offset reduction process is performed on the electroencephalogram signal that has been subjected to the first filter process to reduce a DC offset component. Then, a second filter process is performed on the electroencephalogram signal that has been subjected to the DC offset reduction process to apply a high-pass filter and a low-pass filter. This makes it possible to reduce various artifacts contained in the electroencephalogram signal without complicating the configuration.

[0052] In the first embodiment, the abnormal value reduction process replaces the value of data whose amplitude is equal to or greater than a threshold value with the intermediate value of the data before and after the data. This makes it possible to easily realize the abnormal value reduction process that reduces abnormal values ​​whose amplitude is equal to or greater than a threshold value.

[0053] In the first embodiment, a significant frequency band is determined in the power spectrum of the electroencephalogram signal that has been subjected to the second filtering process, and the state of consciousness of the subject is determined from the determined significant frequency band. This makes it possible to accurately determine the significant frequency band from the electroencephalogram signal in which various artifacts have been reduced, and therefore to accurately determine the state of consciousness of the subject.

[0054] In the first embodiment, an electroencephalogram signal of a subject in a non-resting state is acquired. This makes it possible to obtain an electroencephalogram signal of a subject in which various artifacts are reduced, even if the subject is in a non-resting state.

[0055] In the first embodiment, an electroencephalogram signal is acquired from a subject (driver 20) who is driving a vehicle. This makes it possible to obtain an electroencephalogram signal from the subject in which various artifacts are reduced, even when the subject is driving a vehicle.

[0056] In the first embodiment, the threshold value in the abnormal value reduction process is set to 150 μV or more, which makes it possible to appropriately reduce artifacts caused by body movement when the subject is not in a resting state.

[0057] Second Embodiment Next, a second embodiment of the present disclosure will be described. Note that the same parts as those in the first embodiment are given the same reference numerals and the description thereof will be omitted.

[0058] In the second embodiment, the vehicle seat 10 is provided in a vehicle of any one of a plurality of vehicle types. As shown in Fig. 5, the brainwave signal processing ECU 26 according to the second embodiment is connected via a communication line to a vehicle-side ECU 50 mounted in the vehicle in which the vehicle seat 10 is provided (hereinafter referred to as a "specific vehicle"). The vehicle-side ECU 50 includes a CPU, a memory, a storage unit, etc., similar to the brainwave signal processing ECU 26, and a vehicle type ID representing the vehicle type of the specific vehicle is stored in the storage unit of the vehicle-side ECU 50. The brainwave signal processing ECU 26 acquires the vehicle type ID of the specific vehicle from the vehicle-side ECU 50.

[0059] A processing parameter DB 42 is stored in the storage unit 32 of the EEG signal processing ECU 26. In the second embodiment, environmental artifacts added to the EEG signal are measured in advance for each vehicle model, and processing parameters are determined in advance for each vehicle model according to the artifact measurement results. Here, the processing parameters include the cutoff frequency of the high-pass filter and the cutoff frequency of the low-pass filter in the second filtering process by the second filter unit 68. As shown in FIG. 6, the processing parameter DB 42 stores the processing parameters determined for each vehicle model in association with the vehicle model ID.

[0060] In the second embodiment, the wireless communication unit 34 is capable of wireless communication with a wearable device 52, such as a smart watch, worn by the driver 20. The wearable device 52 has a function of detecting the physical constitution of the driver 20, such as the level of sweatiness, and transmits the physical constitution of the driver 20, including the level of sweatiness, detected by the function, to the EEG signal processing ECU 26 as physical constitution information of the driver 20.

[0061] A threshold value DB 44 is stored in the storage unit 32 of the EEG signal processing ECU 26. In the second embodiment, a threshold value for the abnormal value reduction process by the abnormal value reduction unit 62 is determined in advance for each physical constitution (e.g., level of sweatiness) of the driver 20. As shown in Fig. 7, the threshold value DB 44 registers the threshold value determined for each physical constitution (e.g., level of sweatiness) of the driver 20 in association with physical constitution information of the driver 20.

[0062] In the second embodiment, the electroencephalogram signal processing ECU 26 includes a communication section 46 (see FIG. 5) for communicating with the vehicle-side ECU 50. The electroencephalogram signal processing ECU 26 also functions as a parameter setting section 74 shown in FIG 8. The parameter setting section 74 performs steps 90 to 96 of the electroencephalogram signal processing according to the second embodiment (see FIG. 9).

[0063] That is, in step 90, the parameter setting unit 74 acquires the vehicle model ID of the specific vehicle from the vehicle-side ECU 50. In step 92, the parameter setting unit 74 reads out the processing parameters registered in the processing parameter DB 42 in association with the vehicle model ID acquired in step 90. Then, of the read processing parameters, the cutoff frequency of the high-pass filter and the cutoff frequency of the low-pass filter in the second filter process are set in the second filter unit 68. Note that the vehicle model ID is an example of the second information.

[0064] Furthermore, in step 94, the parameter setting unit 74 acquires constitutional information of the driver 20 from the wearable terminal 52. In step 96, the parameter setting unit 74 reads out a threshold value registered in the threshold DB 44 in association with the constitutional information acquired in step 94, and sets the read-out threshold value in the abnormal value reduction unit 62. The constitutional information is an example of the first information. Furthermore, in the electroencephalogram signal processing according to the second embodiment (FIG. 9), steps 94 and 95 may be executed prior to steps 90 and 92. Furthermore, steps 90 and 92 may be executed prior to step 112, and do not have to be executed at the beginning of the electroencephalogram signal processing (FIG. 9).

[0065] As described above, in the second embodiment, the first information (physical information of the driver) related to artifacts caused by the subject's body movements included in the electroencephalogram signal of the subject is acquired, and at least the processing parameters of the abnormal value reduction process are set based on the acquired first information, thereby making it possible to appropriately reduce artifacts caused by the body movements.

[0066] In the second embodiment, second information (vehicle type ID) related to artifacts caused by the environment included in the electroencephalogram signal of the subject is acquired, and processing parameters of the high-pass filter and the low-pass filter of the second filtering process are set based on the acquired second information, thereby making it possible to appropriately reduce artifacts caused by the environment.

[0067] In the second embodiment, the first information (physical information of the driver) related to artifacts caused by the subject's body movements is acquired, and the processing parameters (thresholds) of the abnormal value reduction processing are set based on the acquired first information. However, the present disclosure is not limited to this, and the processing parameters of the first filter processing (e.g., the interval of the moving average filter) may also be set in addition to the processing parameters of the abnormal value reduction processing based on the acquired first information. However, if the interval of the moving average filter is set too long (e.g., 30 seconds or more), it may adversely affect the consciousness state determination processing in the subsequent stage, so it is preferable to set the value within the range of approximately 5 to 10 seconds.

[0068] In the second embodiment, the wearable terminal 52 detects the physical characteristics of the driver 20, such as the level of sweatiness, but the present disclosure is not limited to this. For example, a biosensor may be built into the seat body 12 of the vehicle seat 10, and the physical characteristics of the driver 20, such as the level of sweatiness, may be detected by the biosensor.

[0069] Further, although the above describes an embodiment in which a moving average filter is applied as an example of a smoothing filter, the present disclosure is not limited to this, and other smoothing filters such as a weighted average filter, a weighted moving average filter, and an exponential moving average filter may also be applied.

[0070] In addition, although the above describes an embodiment in which the EEG signal processing ECU 26 of the vehicle seat 10 also performs the significant band determination process and the consciousness state determination process, the present disclosure is not limited to this. For example, the EEG signal processing ECU 26 of the vehicle seat 10 may output the EEG signal in which various artifacts have been reduced to an external device (e.g., the vehicle-side ECU 50), and the significant band determination process and the consciousness state determination process may be performed by the external device.

[0071] Furthermore, in the above description, the EEG signal processing ECU 26, which is an example of the EEG signal processing device according to the present disclosure, is incorporated in the vehicle seat 10 to process the EEG signal of the driver 20 who drives the vehicle, but the present disclosure is not limited to this. According to the present disclosure, it is possible to obtain an EEG signal with reduced artifacts even when the subject is not in a resting state, and therefore the present disclosure is applicable to various EEG application technologies.

[0072] Further, although the above describes a state in which the EEG signal processing program 40 according to the present disclosure is pre-stored (installed) in the memory unit 32, the EEG signal processing program according to the present disclosure can also be provided in a form in which it is recorded on a non-temporary recording medium such as an HDD, SSD, or DVD. EXAMPLES

[0073] Next, an experiment conducted by the present inventors to verify the effectiveness of the electroencephalogram signal processing method according to the present disclosure will be described.

[0074] In this experiment, a 15 Hz sine wave (see FIG. 10) was used as a signal simulating a true EEG signal. FIG. 11 shows the power spectrum obtained by performing FFT on this 15 Hz sine wave. In addition, in order to simulate various artifacts superimposed on the EEG signal, a pseudo-EEG signal was created by multiplying the 15 Hz sine wave by a 1 Hz sine wave simulating artifacts during communication and a 50 Hz sine wave simulating artifacts caused by the environment, and further adding random noise to simulate artifacts due to body movement (FIG. 12). FIG. 13 shows the power spectrum obtained by performing FFT on this pseudo-EEG signal. The reason why artifacts due to body movement are simulated by random noise is to confirm whether artifacts due to body movement can be reduced even if they are artifacts due to unexpected body movement.

[0075] In the power spectrum of the pseudo-EEG signal shown in Fig. 13, frequency components corresponding to communication artifacts appear around 1 Hz, and frequency components corresponding to environmental artifacts appear around 50 Hz. In addition, random noise components corresponding to body movement artifacts appear in all frequency bands, and the 15 Hz frequency component corresponding to the true EEG signal is hidden by the noise.

[0076] As shown in Fig. 4, the electroencephalogram signal processing to which the electroencephalogram signal processing method according to the present disclosure is applied involves, after acquiring an electroencephalogram signal, performing the following processes in numerical order: (1) abnormal value reduction process (steps 102 to 106), (2) first filtering process (moving average filter) (step 108), (3) DC offset reduction process (step 110), and (4) second filtering process (high-pass filter and low-pass filter) (step 112). First, the results of an experiment in which each of the processes (1) to (4) was performed independently on the aforementioned pseudo-electroencephalogram signal will be described.

[0077] Figure 14 shows the power spectrum of the EEG signal when only the (1) outlier reduction process is performed on the pseudo-EEG signal. When only the (1) outlier reduction process is performed, the 15 Hz frequency component corresponding to the true EEG signal is attenuated. In addition, the 1 Hz peak is high, and there is also a peak at 50 Hz with the same power as 15 Hz. This may result in an erroneous determination of the driver's consciousness state in the subsequent stage.

[0078] Fig. 15 shows the power spectrum of the EEG signal when only the (2) first filtering process (moving average filter) is performed on the pseudo-EEG signal. When only the (2) first filtering process (moving average filter) is performed, no attenuation of the 15 Hz frequency component corresponding to the true EEG signal is observed, but the peak at 1 Hz is high and a peak with the same power as 15 Hz also exists at 50 Hz. This may result in an erroneous determination of the driver's consciousness state in the subsequent stage.

[0079] Fig. 16 shows the power spectrum of the EEG signal when only the (3) DC offset reduction process is performed on the pseudo-EEG signal. When only the (3) DC offset reduction process is performed, the 1 Hz peak can be cut, but there is a peak at 50 Hz that has the same power as that at 15 Hz. This may cause an erroneous determination of the driver's consciousness state in the subsequent stage.

[0080] Also, the power spectrum of the EEG signal when only the (4) second filtering process (high-pass filter and low-pass filter) is performed on the pseudo-EEG signal is shown in Figure 17. When only the (4) second filtering process (high-pass filter and low-pass filter) is performed, a peak appears at approximately 10 Hz, and the 15 Hz frequency component corresponding to the true EEG signal is attenuated and hidden by other frequency components. This causes an erroneous determination of the driver's consciousness state in the subsequent stage.

[0081] As described above, in the power spectra of the EEG signals when each of the processes (1) to (4) is performed alone (Figures 14 to 17), the 15 Hz frequency component simulating the true EEG signal is not significantly larger than the components in the other frequency bands, and it is clear that the performance in reducing artifacts is insufficient.

[0082] Next, the results when the processes (1) to (4) are performed on the simulated electroencephalogram signal in numerical order (in the order according to the electroencephalogram signal processing method according to the present disclosure) will be described.

[0083] First, when the pseudo-EEG signal is subjected to the (1) abnormal value reduction process, the waveform of the EEG signal changes as shown in Fig. 12 to Fig. 18, and its power spectrum changes as shown in Fig. 13 to Fig. 19. In the power spectrum shown in Fig. 19, the 50 Hz frequency component is reduced and the 15 Hz frequency component is slightly more prominent, but each artifact is not sufficiently reduced, so it can be said to be in an incomplete state. For this reason, there is a risk of an erroneous determination being made as to which frequency band is significant in the determination of the driver's consciousness state at a later stage.

[0084] Next, when the (2) first filter process (moving average filter) is performed on the EEG signal that has been subjected to the (1) abnormal value reduction process, the waveform of the EEG signal changes as shown in Fig. 18 to Fig. 20, and its power spectrum changes as shown in Fig. 19 to Fig. 21. In the power spectrum shown in Fig. 21, the frequency component of 15 Hz becomes slightly noticeable, but each artifact has not been sufficiently reduced, so it can be said to be in an incomplete state. For this reason, there is a risk of an erroneous determination being made as to which frequency band is significant in determining the driver's consciousness state at a later stage.

[0085] Next, when the electroencephalogram signal that has been subjected to (1) abnormal value reduction processing and (2) first filter processing (moving average filter) is subjected to (3) DC offset reduction processing, the waveform of the electroencephalogram signal changes as shown in FIG. 20 to FIG. 22, and its power spectrum changes as shown in FIG. 21 to FIG. 23. As is clear from comparing FIG. 22 with FIG. 20, the center of the amplitude of the electroencephalogram signal that has been subjected to DC offset reduction processing has shifted to near 0. In the power spectrum shown in FIG. 23, although the frequency components (artifacts during communication) around 1 Hz have been suppressed to about half, each artifact has not been sufficiently reduced, so it can be said to be in an incomplete state. For this reason, there is a risk of erroneous determination of which frequency band is significant in determining the driver's consciousness state in the subsequent stage.

[0086] Furthermore, when the EEG signal that has been subjected to (1) abnormal value reduction processing → (2) first filtering processing (moving average filter) → (3) DC offset reduction processing is subjected to (4) second filtering processing (high-pass filter and low-pass filter), the waveform of the EEG signal changes as shown in Figures 22 to 24, and its power spectrum changes as shown in Figures 23 to 25. In the power spectrum shown in Figure 25, each artifact is sufficiently reduced, and the frequency component of 15 Hz corresponding to the true EEG signal shows a prominent peak. Therefore, in the determination of the driver's consciousness state in the subsequent stage, it can be determined without fail that the frequency band around 15 Hz is significant.

[0087] Furthermore, in order to verify the effectiveness of performing each of the processes (1) to (4) in numerical order (in accordance with the electroencephalogram signal processing method according to the present disclosure), the inventors of the present application conducted an experiment in which the execution order of each process was changed and each process was performed to confirm the power spectrum. The results of this experiment will be described below.

[0088] The first execution order is to perform the DC offset reduction process first. That is, the pseudo-EEG signal is subjected to the following processes in the order: (3) DC offset reduction process → (1) abnormal value reduction process → (2) first filter process (moving average filter) → (4) second filter process (high-pass filter and low-pass filter). The power spectrum of the EEG signal obtained by performing each process in the first execution order is shown in FIG. 26.

[0089] In the power spectrum shown in Fig. 26, the frequency component of 15 Hz corresponding to the true electroencephalogram signal has the maximum peak, but the peak of the frequency component around 25 Hz is not less high. Therefore, when a significant band determination process is performed on the power spectrum shown in Fig. 26 to determine the frequency band in which the maximum peak occurs as significant, there is a risk of misjudging the significant frequency band. In addition, the power spectrum shown in Fig. 26 has a high level on average for the frequency band around 5 Hz to 28 Hz. Therefore, when a significant band determination process is performed on the power spectrum shown in Fig. 26 to determine the frequency band in which the result of integrating each frequency band shows the maximum value as significant, there is a risk of misjudging the significant frequency band.

[0090] The second execution order is to perform the second filter processing (high-pass filter and low-pass filter) first. That is, the pseudo-EEG signal is subjected to the following processing in the order of (4) second filter processing (high-pass filter and low-pass filter) → (1) abnormal value reduction processing → (2) first filter processing (moving average filter) → (3) DC offset reduction processing. The power spectrum of the EEG signal obtained by performing each processing in the second execution order is shown in FIG. 27.

[0091] In the power spectrum shown in FIG. 27, the maximum peak is near 7 Hz, and the peak near 15 Hz corresponding to the true electroencephalogram signal is attenuated. Therefore, when a significant band determination process is performed on the power spectrum shown in FIG. 27 to determine the frequency band in which the maximum peak occurs as significant, an erroneous determination of the significant frequency band occurs. In addition, the power spectrum shown in FIG. 27 has several peaks in the frequency band near 7 Hz to 28 Hz. Therefore, even when a significant band determination process is performed on the power spectrum shown in FIG. 27 to determine the frequency band in which the result of integrating each frequency band shows the maximum value as significant, there is a risk of an erroneous determination of the significant frequency band.

[0092] The third execution order is to perform the first filter processing (moving average filter) first. That is, the pseudo-EEG signal is subjected to the following processing in the order of (2) first filter processing (moving average filter) → (1) abnormal value reduction processing → (3) DC offset reduction processing → (4) second filter processing (high-pass filter and low-pass filter). The power spectrum of the EEG signal obtained by performing each processing in the third execution order is shown in FIG. 28.

[0093] In the power spectrum shown in Fig. 28, the peak around 15 Hz, which corresponds to the true EEG signal, has been cut off and disappeared, and there are high peaks around 8 Hz and 23 Hz. Therefore, when a significant band determination process is performed on the power spectrum shown in Fig. 28 to determine the frequency band in which the maximum peak occurs as significant, a misjudgment of the significant frequency band occurs. Also, when a significant band determination process is performed to determine the frequency band in which the maximum value of the result of integrating each frequency band is the significant frequency band, there is a risk of misjudging the significant frequency band.

[0094] In the fourth execution order, the DC offset reduction process is performed last. That is, the pseudo-EEG signal is subjected to the following processes in the order of (1) abnormal value reduction process → (2) first filter process (moving average filter) → (4) second filter process (high-pass filter and low-pass filter) → (3) DC offset reduction process. The power spectrum of the EEG signal obtained by performing each process in the fourth execution order is shown in FIG. 29.

[0095] In the power spectrum shown in Fig. 29, there are high peaks near 5 Hz and 7 Hz, and no clear peak exists near 15 Hz, which corresponds to the true EEG signal. Therefore, when a significant band determination process is performed on the power spectrum shown in Fig. 29 to determine the frequency band in which the maximum peak occurs as significant, a significant frequency band is erroneously determined. In addition, when a significant band determination process is performed to determine the frequency band in which the maximum value of the result of integrating each frequency band is the significant frequency band, there is a risk of erroneously determining the significant frequency band.

[0096] In the fifth execution order, the abnormal value reduction process is performed last. That is, the pseudo-EEG signal is subjected to the following processes in the order of (2) first filter process (moving average filter) → (3) DC offset reduction process → (4) second filter process (high-pass filter and low-pass filter) → (1) abnormal value reduction process. The power spectrum of the EEG signal obtained by performing each process in the fifth execution order is shown in FIG. 30.

[0097] In the power spectrum shown in Fig. 30, there is a high peak near 11 Hz, and although there is a peak near 15 Hz corresponding to the true EEG signal, the height of the peak is relatively low. Therefore, when a significant band determination process is performed on the power spectrum shown in Fig. 30 to determine the frequency band in which the maximum peak occurs as significant, a misdetermination of the significant frequency band occurs. In addition, when a significant band determination process is performed to determine the frequency band in which the maximum value of the result of integrating each frequency band is the significant frequency band, there is a risk of misdetermination of the significant frequency band.

[0098] The sixth execution order is to perform the first filter process (moving average filter) last. That is, the pseudo-EEG signal is subjected to the following processes in the order of (1) abnormal value reduction process → (3) DC offset reduction process → (4) second filter process (high-pass filter and low-pass filter) → (2) first filter process (moving average filter). The power spectrum of the EEG signal obtained by performing each process in the sixth execution order is shown in FIG. 31.

[0099] In the power spectrum shown in FIG. 31, there is a high peak near 27 Hz, and the next highest peak is near 20 Hz. There is no clear peak near 15 Hz, which corresponds to the true EEG signal. Therefore, when a significant band determination process is performed on the power spectrum shown in FIG. 31 to determine the frequency band in which the maximum peak occurs as significant, a misdetermination of the significant frequency band occurs. In addition, when a significant band determination process is performed to determine the frequency band in which the result of integrating each frequency band shows the maximum value as significant, there is a risk of misdetermination of the significant frequency band.

[0100] The seventh execution order is an order in which the DC offset reduction process and the first filter process (moving average filter) are switched. That is, the pseudo-EEG signal is subjected to the following processes in the order: (1) abnormal value reduction process → (3) DC offset reduction process → (2) first filter process (moving average filter) → (4) second filter process (high-pass filter and low-pass filter). The power spectrum of the EEG signal obtained by performing the processes in the seventh execution order is shown in FIG. 32.

[0101] In the power spectrum shown in FIG. 32, there is a high peak near 22 Hz, and the next highest peak is near 19 Hz. Although there are some peaks near 15 Hz, which corresponds to the true EEG signal, the height of the peaks is low. Therefore, when a significant band determination process is performed on the power spectrum shown in FIG. 32 to determine the frequency band in which the maximum peak occurs as significant, a misdetermination of the significant frequency band occurs. In addition, when a significant band determination process is performed to determine the frequency band in which the result of integrating each frequency band shows the maximum value as significant, there is a risk of misdetermination of the significant frequency band.

[0102] From the above results, it has been confirmed that performing each of the processes (1) to (4) on the EEG signal in numerical order (performing them in the order conforming to the EEG signal processing method of the present disclosure) is extremely effective in obtaining an EEG signal that is close to a true EEG signal with various artifacts reduced. [Explanation of symbols]

[0103] 10 Vehicle seats 12 Seat body 24 Electroencephalography equipment 26 EEG signal processing ECU 40 EEG signal processing program 60 Acquisition Department 62 Abnormal value reduction section 64 First filter section 66 DC offset reduction section 68 Second filter section 70 Significant band determination unit 72 Consciousness state determination unit 74 Parameter setting section

Claims

1. Environmental artifacts added to the EEG signal of a subject driving a vehicle are measured in advance for each vehicle model, and depending on the measurement results of the artifacts, the vehicle model as second information related to the environmental artifacts included in the EEG signal of the subject is stored in a memory unit for each vehicle model in correspondence with the first frequency and the second frequency in a second filter process in which the EEG signal is subjected to a high-pass filter with a cutoff frequency of a first frequency and a low-pass filter with a cutoff frequency of a second frequency higher than the first frequency, Acquiring an electroencephalogram signal of a subject who is driving a vehicle and acquiring the type of vehicle that the subject is driving; An abnormal value reduction process is performed on the acquired electroencephalogram signal to reduce abnormal values ​​having an amplitude equal to or greater than a threshold value. performing a first filtering process in which a smoothing filter is applied to the electroencephalogram signal that has been subjected to the abnormal value reduction process; performing a DC offset reduction process for reducing a DC offset component on the electroencephalogram signal that has been subjected to the first filtering process; A second filtering process is performed on the electroencephalogram signal that has been subjected to the DC offset reduction process, in which the first frequency that is stored in the storage unit in association with the vehicle model of the acquired vehicle is set as a cutoff frequency of the high-pass filter, and the second frequency that is stored in the storage unit in association with the vehicle model of the acquired vehicle is set as a cutoff frequency of the low-pass filter. EEG signal processing method.

2. The EEG signal processing method of claim 1, wherein the abnormal value reduction process involves replacing data values ​​whose amplitude is equal to or greater than a threshold with the intermediate value of the data before and after it.

3. Determining a significant frequency band in a power spectrum of the EEG signal that has undergone the second filtering process; 3. The electroencephalogram signal processing method according to claim 1, further comprising the step of determining a state of consciousness of the subject from the determined significant frequency band.

4. An EEG signal processing method described in any one of claims 1 to 3, which acquires an EEG signal from a subject in a non-resting state.

5. An EEG signal processing method as described in any one of claims 1 to 4, which acquires an EEG signal from a subject who is driving a vehicle.

6. An EEG signal processing method described in any one of claims 1 to 5, wherein the threshold value in the abnormal value reduction process is 150 μV or a value exceeding this.

7. A memory unit which measures in advance environmental artifacts added to the EEG signal of a subject driving a vehicle for each vehicle model, and stores, depending on the measurement results of the artifacts, the vehicle model as second information related to the environmental artifacts included in the EEG signal of the subject, in correspondence with the first frequency and the second frequency in a second filtering process which applies a high-pass filter having a cutoff frequency of a first frequency and a low-pass filter having a cutoff frequency of a second frequency higher than the first frequency, for each vehicle model; an acquisition unit that acquires an electroencephalogram signal of a subject who is driving a vehicle and acquires a type of the vehicle that the subject is driving; an abnormal value reduction unit that performs an abnormal value reduction process for reducing abnormal values ​​having amplitudes equal to or greater than a threshold value for the electroencephalogram signal acquired by the acquisition unit; a first filter unit that performs a first filter process of applying a smoothing filter to the electroencephalogram signal that has been subjected to the abnormal value reduction process by the abnormal value reduction unit; a DC offset reduction unit that performs a DC offset reduction process to reduce a DC offset component on the electroencephalogram signal that has been subjected to the first filtering process by the first filter unit; a second filter unit that performs a second filter process on the electroencephalogram signal that has been subjected to the DC offset reduction process by the DC offset reduction unit, the second filter process setting the first frequency that is stored in the storage unit in association with the vehicle model acquired by the acquisition unit as a cutoff frequency of the high-pass filter, and setting the second frequency that is stored in the storage unit in association with the vehicle model as a cutoff frequency of the low-pass filter; 13. An electroencephalogram signal processing device comprising:

8. A seat body on which a driver of a vehicle sits; an electroencephalogram (EEG) measuring unit that measures an electroencephalogram of a driver of a vehicle seated in the seat body and outputs an electroencephalogram signal; 8. The electroencephalogram signal processing apparatus according to claim 7, further comprising:

1. A vehicle seat comprising:

9. Environmental artifacts added to the EEG signal of a subject driving a vehicle are measured in advance for each vehicle model, and depending on the measurement results of the artifacts, the vehicle model as second information related to the environmental artifacts included in the EEG signal of the subject is stored in a memory unit for each vehicle model in correspondence with the first frequency and the second frequency in a second filter process in which the EEG signal is subjected to a high-pass filter with a cutoff frequency of a first frequency and a low-pass filter with a cutoff frequency of a second frequency higher than the first frequency, On the computer, Acquiring an electroencephalogram signal of a subject who is driving a vehicle and acquiring the type of vehicle that the subject is driving; Acquire an electroencephalogram (EEG) signal from a subject who is driving a vehicle; An abnormal value reduction process is performed on the acquired electroencephalogram signal to reduce abnormal values ​​having an amplitude equal to or greater than a threshold value. performing a first filtering process in which a smoothing filter is applied to the electroencephalogram signal that has been subjected to the abnormal value reduction process; performing a DC offset reduction process for reducing a DC offset component on the electroencephalogram signal that has been subjected to the first filtering process; A second filtering process is performed on the electroencephalogram signal that has been subjected to the DC offset reduction process, in which the first frequency that is stored in the storage unit in association with the vehicle model of the acquired vehicle is set as a cutoff frequency of the high-pass filter, and the second frequency that is stored in the storage unit in association with the vehicle model of the acquired vehicle is set as a cutoff frequency of the low-pass filter. The electroencephalogram signal processing program for executing processing including the above.

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