Apparatus and method for determining ride comfort of a passenger using brainwave signals

Abstract

The present disclosure relates to an apparatus and method for determining ride comfort of a passenger using a brain wave signal, the apparatus including an analyzer configured to determine first ride comfort information of a passenger based on information about a sitting posture of the passenger in a moving body; a sensor configured to collect a brain wave signal of the passenger in the moving body for a predetermined time; and a controller configured to control the moving body, wherein the analyzer is configured to determine second ride comfort information obtained by correcting the first ride comfort information by analyzing the collected brain wave signal based on the first ride comfort information, and wherein the controller is configured to control the moving body based on the determined second ride comfort information.

Description

[0001] Cross-reference to related applications

[0002] This application claims priority to Korean Patent Application No. 10-2019-0135833, filed on October 29, 2019, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This disclosure relates to methods and apparatus for controlling mobile bodies. More specifically, this disclosure relates to methods and apparatus for controlling mobile bodies that determine the riding comfort of passengers based on brainwave signals of passengers in a mobile body. Background Technology

[0004] The statements in this section provide only background information in connection with this disclosure and may not constitute prior art.

[0005] Vehicles (or mobile bodies), as a means of transportation, are a very important means and tool for living in the modern world. Furthermore, the mobile body itself can be considered a special thing that gives meaning to someone.

[0006] With technological advancements, the functions offered by mobile devices have also evolved. For example, in recent years, mobile devices have not only transported passengers to their destinations but have also met the demand for faster and safer arrival. Furthermore, new devices are being added to mobile device systems to satisfy passengers' aesthetic tastes and comfort needs. Additionally, existing devices such as steering wheels, transmissions, and accelerator / decelerator mechanisms are being developed to provide users with even more functionality.

[0007] Meanwhile, brain-computer interfaces or brain-machine interfaces are the field of controlling computers or machines according to a person's intentions using brainwave signals. ERPs (Event-Related Potentials) are closely related to cognitive function. Summary of the Invention

[0008] This disclosure relates to methods and apparatus for controlling mobile bodies. Specific embodiments relate to methods and apparatus for controlling mobile bodies.

[0009] Embodiments of the present invention provide an apparatus and method for determining the riding comfort of a mobile passenger based on brainwave signals of the mobile passenger.

[0010] Another embodiment of the present invention provides an apparatus and method for controlling internal devices of a mobile body in relation to the passenger's sitting posture based on brainwave signals of a passenger in the mobile body.

[0011] The embodiments disclosed herein are not limited to those described above, and other embodiments not mentioned will be clearly understood by those skilled in the art through the following description.

[0012] According to an embodiment of the present application, an apparatus for determining ride comfort of a passenger using a brain wave signal includes an analyzer configured to determine first ride comfort information of the passenger based on seating posture information about at least one passenger in a moving body, a sensor configured to collect a brain wave signal of the passenger in the moving body for a predetermined time, and a controller configured to control the moving body. The analyzer determines second ride comfort information obtained by correcting the first ride comfort information by analyzing the collected brain wave signal based on the first ride comfort information, and the controller controls the moving body based on the determined second ride comfort information.

[0013] The seating posture information about the passenger can include at least one of personal information of the passenger, health condition information of the passenger, seating information of the passenger, past posture information of the passenger, a preferred seating posture of the passenger, and driving environment information.

[0014] The first ride comfort information of the passenger can be evaluation information of the seating posture of the passenger.

[0015] The evaluation information of the seating posture of the passenger can be expressed as a numerical value.

[0016] The brain wave signal can be at least one of an alpha wave, a beta wave, and a theta wave.

[0017] The analysis can include comparing an amplitude of the brain wave signal collected for the predetermined time with a predetermined threshold value.

[0018] The brain wave signal can include a theta wave, and the analyzer can determine that a state of the passenger is a stress state when an amplitude of the theta wave is greater than or equal to a predetermined threshold value.

[0019] The brain wave signal can include an alpha wave, and the analyzer can determine that a state of the passenger is a comfortable state when an amplitude of the alpha wave is greater than or equal to a predetermined threshold value.

[0020] The brain wave signal can include a beta wave, and the analyzer can determine that a state of the passenger is a stress state when an amplitude of the beta wave is greater than or equal to a predetermined threshold value.

[0021] When the first ride comfort information of the passenger has a first value, the analyzer can determine the second ride comfort information using only a predetermined brain wave signal among the brain wave signal.

[0022] When the first ride comfort information of the passenger has a second value, the analyzer can apply a predetermined weight to a threshold value.

[0023] The moving body can include at least one of a seat, a steering wheel, a rearview mirror, a console box, a navigation device, and a voice device.

[0024] When the second ride comfort information of the passenger is determined as the first state, the controller can control an angle of at least one of a seat, a steering wheel, a rearview mirror, and a console box of the passenger.

[0025] When the second ride comfort information of the passenger is determined as the second state, the controller can control a position of at least one of a seat, a steering wheel, a rearview mirror, and a console box of the passenger.

[0026] According to an embodiment of the present disclosure, a method of determining a ride comfort of a passenger using a brain wave signal includes determining first ride comfort information of the passenger based on seating information about at least one passenger in a moving body, collecting a brain wave signal of the passenger in the moving body for a predetermined time, determining second ride comfort information obtained by correcting the first ride comfort information by analyzing the collected brain wave signal based on the first ride comfort information, and controlling the moving body based on the determined second ride comfort information.

[0027] The seating information about the passenger can include at least one of personal information of the passenger, health information of the passenger, seating information of the passenger, past posture information of the passenger, a preferred seating posture of the passenger, and driving environment information.

[0028] The first ride comfort information of the passenger can be evaluation information of a seating posture of the passenger.

[0029] The evaluation information of the seating posture of the passenger can be expressed as a numerical value.

[0030] The brain wave signal can be at least one of an alpha wave, a beta wave, and a theta wave.

[0031] The analyzing can include comparing an amplitude of the brain wave signal collected for the predetermined time with a predetermined threshold value.

[0032] The brain wave signal can include a theta wave, and the determining of the second ride comfort information can include determining a state of the passenger as a stress state when an amplitude of the theta wave is greater than or equal to a predetermined threshold value.

[0033] The brain wave signal can include an alpha wave, and the determining of the second ride comfort information can include determining a state of the passenger as a comfortable state when an amplitude of the alpha wave is greater than or equal to a predetermined threshold value.

[0034] The brain wave signal can include a beta wave, and the determining of the second ride comfort information can include determining a state of the passenger as a stress state when an amplitude of the beta wave is greater than or equal to a predetermined threshold value.

[0035] The determining of the second ride comfort information can include, when the first ride comfort information of the passenger has a first value, determining the second ride comfort information using only a predetermined brain wave signal among the brain wave signal.

[0036] Determining the second ride comfort information can include applying a predetermined weight to the threshold value when the first ride comfort information of the passenger has a second value.

[0037] The mobile body can include at least one of a seat, a steering wheel, a rearview mirror, a console box, a navigation device, and a voice device.

[0038] Controlling the mobile body can include controlling an angle of at least one of a seat, a steering wheel, a rearview mirror, and a console box of the passenger when the second ride comfort information of the passenger is determined as the first state.

[0039] Controlling the mobile body can include controlling a position of at least one of a seat, a steering wheel, a rearview mirror, and a console box of the passenger when the second ride comfort information of the passenger is determined as the second state.

[0040] The features described briefly above in relation to embodiments of the disclosure are merely exemplary aspects of the following detailed description of the disclosure and do not limit the scope of the disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0041] In order to facilitate the understanding of the disclosure, various embodiments of the disclosure will now be described by way of example and with reference to the accompanying drawings, in which:

[0042] Figure 1 is a graph illustrating a general waveform of an ERN of an embodiment of the disclosure;

[0043] Figure 2 is a graph illustrating a general waveform of an ERN and a Pe according to an embodiment of the disclosure;

[0044] Figure 3 is a graph illustrating a deflection characteristic of a Pe according to another embodiment of the disclosure;

[0045] Figure 4A and Figure 4B are graphs illustrating measurement regions of an ERP and a Pe, respectively, according to an embodiment of the disclosure;

[0046] Figure 5 is a graph illustrating a general waveform of an ERN and a CRN according to an embodiment of the disclosure;

[0047] Figure 6 is a graph illustrating EEG measurement channels corresponding to brain cortex regions according to an embodiment of the disclosure;

[0048] Figure 7 is a block diagram illustrating a configuration of an apparatus for determining a ride comfort of a passenger based on a brainwave signal of the passenger according to an embodiment of the disclosure;

[0049] Figure 8A andFigure 8B is a diagram illustrating a process of comparing a brain wave signal of each frequency band with a predetermined threshold according to an embodiment of the present application; and

[0050] Figure 9 is a flowchart illustrating a method of operating an apparatus for determining ride comfort of a user according to an embodiment of the present application. DETAILED DESCRIPTION

[0051] The following description is merely exemplary in nature and is not intended to limit the disclosure, application, or uses. It should be understood that throughout the drawings, corresponding reference numerals indicate like or corresponding parts and features.

[0052] Exemplary embodiments of the present disclosure will be described in detail, such that one of ordinary skill in the art to which the present disclosure pertains will be able to easily carry out the devices and methods provided by the embodiments of the present disclosure, with reference to the accompanying drawings. However, the present disclosure can be embodied in various forms, and the scope of the present disclosure should not be interpreted as being limited to the exemplary embodiments.

[0053] In describing the embodiments of the present disclosure, when well-known functions or configurations can obscure the spirit of the present disclosure, they will not be described in detail.

[0054] In the embodiments of the present disclosure, it will be understood that when an element is referred to as being "connected to", "coupled to" another element, or "combined with" another element, it can be directly connected or coupled to the other element or combined with the other element, or there can be an intermediate element therebetween. It will be further understood that, when used in the embodiments of the present disclosure, the terms "comprise", "include", "have", and the like specify the presence of the stated features, integers, steps, operations, elements, components, and / or combinations thereof, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or combinations thereof.

[0055] It will be understood that, although the terms "first", "second", etc. can be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another, but not to describe a sequence or priority of the elements. For example, a first element discussed below can be termed a second element without departing from the teachings of the present disclosure. Similarly, a second element can also be termed a first element.

[0056] In the embodiments of the disclosure, elements distinguished are referred to as clearly describing the features of various elements, and do not mean that the elements are physically separated from each other. That is, a plurality of distinguished elements can be combined into a single hardware unit or a single software unit, and conversely, one element can be implemented by a plurality of hardware units or software units. Therefore, although not specifically mentioned, the integrated form of various elements or the separated form of one element can fall within the scope of the disclosure. Also, terms such as "unit" or "module" should be understood as a unit that processes at least one function or operation and can be embodied in a hardware manner (for example, a processor), a software manner, or a combination of a hardware manner and a software manner.

[0057] In the embodiments of the disclosure, all constituent elements described in various forms should not be interpreted as essential elements, and some of the constituent elements can be optional elements. Therefore, embodiments configured in some form by various subsets of constituent elements can also fall within the scope of the disclosure. In addition, embodiments configured by adding one or more elements to various elements can also fall within the scope of the disclosure.

[0058] As electrical activity of neurons constituting the brain, a brain wave signal (or brain signal, brain wave) refers to a biological signal that directly or indirectly reflects a conscious or unconscious state of a person. The brain wave signal can be measured in each region of the scalp of a person, and has a wavelength having a frequency of mainly 30 Hz or less, and a potential difference of several microvolts. According to brain activity and state, various waveforms can occur. Research is being conducted on interface control using a brain wave signal according to a person's intention. The brain wave signal can be obtained by using EEG (electroencephalogram) using an electrical signal caused by brain activity, MEG (magnetoencephalogram) using a magnetic signal that occurs together with the electrical signal, and fMRI (functional magnetic resonance imaging) or fNIRS (functional near-infrared spectroscopy) using a change in oxygen saturation in blood. Although fMRI and fNIRS are useful techniques for measuring brain activity, generally, fMRI has a low temporal resolution and fNIRS has a low spatial resolution. Due to these limitations, the EEG signal is widely used due to its excellent portability and temporal resolution advantages.

[0059] The brain wave signal varies spatially and temporally according to brain activity. Since the brain wave signal is generally difficult to analyze and its waveform is not easily visualized for analysis, various processing methods have been proposed.

[0060] For example, according to the number of oscillations (frequency), the brain wave signal can be classified based on a frequency band (power spectral classification). This classification regards the measured brain wave signal as a simple signal of a linear sum at each specific frequency, and decomposes the signal into individual frequency components and indicates the corresponding amplitude. By using pre-processing commonly used for noise cancellation, Fourier transform into the frequency domain, and a band-pass filter (BPF), the brain wave signal at each frequency can be obtained.

[0061] More specifically, according to the frequency band, the brain wave can be classified as a delta (δ), theta (θ), alpha (α), beta (β), and gamma (γ) wave. The delta wave is a brain wave having a frequency of 3.5 Hz or less and an amplitude of 20 μV to 200 μV, which mainly occurs in normal deep sleep or in a newborn. In addition, the delta wave can increase as our understanding of the physical world decreases. In general, the theta wave is a brain wave having a frequency of 3.5 Hz to 7 Hz, which mainly occurs in a state of emotional stability or sleep.

[0062] In addition, the theta wave is mainly generated in the parietal lobe and occipital lobe, and can occur during calm concentration, to restore memory or meditation. In general, the alpha wave is a brain wave having a frequency of 8 Hz to 12 Hz, which mainly occurs in a state of relaxation and comfort. In addition, the alpha wave is usually generated in the occipital lobe when at rest, and can be attenuated in sleep. In general, the beta wave is a brain wave having a frequency of 13 Hz to 30 Hz, which mainly occurs in a tolerable stress state or in a situation that causes attention to some extent. In addition, the beta wave is mainly generated in the frontal lobe, and is related to brain activity in a state of wakefulness or concentration, pathological phenomena, and drug effects. The beta wave can occur in a wide area of the entire brain. In addition, specifically, the beta wave can be classified into an SMR wave having a frequency of 13 Hz to 15 Hz, a mid-beta wave having a frequency of 15 Hz to 18 Hz, and a high-beta wave having a frequency of 20 Hz or more. Since the beta wave seems to be stronger under anxious and tense stress, it is called a stress wave. The gamma wave is a brain wave usually having a frequency of 30 Hz to 50 Hz, which mainly occurs in a state of strong excitement or in a high-level cognitive information processing process. In addition, the gamma wave can occur during the state of wakefulness of consciousness and REM sleep, and can also overlap with the beta wave.

[0063] Each brain wave signal according to a frequency band is associated with a specific cognitive function. For example, a delta wave is associated with sleep, a theta wave is associated with working memory, and an alpha wave is associated with attention or inhibition. Thus, the characteristics of each frequency band of brain wave signals selectively show a specific cognitive function. In addition, each frequency band of brain wave signals can show some different aspects in each measured portion on the head surface. The cerebral cortex can be divided into frontal cortex, parietal cortex, temporal cortex, and occipital cortex. These portions can have some different roles. For example, the occipital cortex corresponding to the back of the head has a main visual cortex, and thus can mainly process visual information. The parietal cortex located near the top of the head has a somatosensory cortex, and thus can process motor / sensory information. In addition, the frontal cortex can process information related to memory and thinking, and the temporal cortex can process information related to hearing and smell.

[0064] Meanwhile, for another example, brain wave signals can be analyzed by using ERP (Event Related Potential). ERP is an electrical change in the brain associated with external stimulation or internal mental processes. ERP refers to a signal including electrical activity of the brain, which is caused by a stimulus including specific information (e.g., image, voice, sound, execution command, etc.) after a certain time after the stimulus is presented.

[0065] In order to analyze ERP, a process of separating a signal from noise is required. The averaging method can be mainly used. Specifically, by averaging brain waves measured based on the stimulus start time, brain waves unrelated to the stimulus can be removed, and only relevant potentials, i.e., brain activity generally associated with stimulus processing, are selected.

[0066] Since ERP has a high temporal resolution, it is closely related to the study of cognitive functions. ERP is an electrical phenomenon caused by external stimulation or associated with internal states. According to the type of stimulation, ERP can be divided into auditory-related potential, visual-related potential, somatosensory-related potential, and olfactory-related potential. According to the nature of the stimulus, ERP can be divided into exogenous ERP and endogenous ERP. Exogenous ERP has a waveform determined by external stimulation, is associated with automatic processing, and mainly occurs in the initial stage of being stimulated. For example, exogenous ERP is brainstem potential. On the other hand, endogenous ERP is determined by internal cognitive processes or mental processes or states unrelated to the stimulus, and is associated with "controlled processes." For example, endogenous ERP is P300, N400, P600, CNV (Contingent Negative Variation), etc.

[0067] The names of ERP peaks generally include polarity and a latent period (difference in reaction time), and each peak of each signal has a respective definition and meaning. For example, a positive polarity is P, a negative polarity is N, and P300 indicates a positive peak measured about 300 milliseconds after the start of a stimulus. In addition, 1, 2, 3, or a, b, c, etc. are applied according to the order of appearance. For example, P3 indicates the third positive polarity in the waveform after the start of a stimulus.

[0068] Hereinafter, various ERPs will be described.

[0069] For example, N100 is related to a response to an unpredictable stimulus.

[0070] MMN (mismatch negativity) can be generated not only by a focused stimulus but also by an unfocused stimulus. MMN can be used as an indicator of whether a sensory memory (echoic memory) works before attention is initially caused. P300, which will be described below, appears in the process of attention and making a judgment, whereas MMN is analyzed as a process occurring in the brain before attention.

[0071] For another example, N200 (or N2) is mainly generated according to visual and auditory stimuli, and is related to short-term memory or long-term memory, which is a type of memory after attention, and P300 described below.

[0072] For another example, P300 (or P3) mainly reflects attention to a stimulus, cognitive recognition of a stimulus, memory search, and reduction of uncertainty, and is related to a perceptual decision that distinguishes external stimuli. Since the generation of P300 is related to cognitive functions, P300 is generated regardless of the type of stimulus that appears. For example, P300 can be generated in auditory stimuli, visual stimuli, and somatosensory stimuli. P300 is widely used in research on brain-computer interfaces.

[0073] For another example, N400 is related to language processing, and is caused when a sentence or auditory stimulus with a semantic error appears. In addition, N400 is related to a memory process, and can reflect a process of retrieving or searching for information from long-term memory.

[0074] For another example, P600, as an indicator showing a process of reconstruction or recovery, is related to a process of more accurately processing a stimulus based on information stored in long-term memory.

[0075] For another example, CNV refers to a potential that appears 200 ms to 300 ms or even several seconds in a later stage. It is also called a slow potential (SP), and is related to anticipation, preparation, mental priming, association, attention, and motor activity.

[0076] For example, the ERN (Error-Related Negativity) or Ne (Error Negativity) is an event-related potential (ERP) generated by an error or mistake. This can occur when a subject makes a mistake in a sensorimotor task or the like. More specifically, the ERN is generated when a subject recognizes a mistake or error, and its negative peak mainly appears in the frontal and central regions about 50 ms to 150 ms. In particular, its negative peak can occur in a case where a mistake related to a motor response can occur, and can also be used to indicate a negative self-judgment.

[0077] Hereinafter, the main features of the ERN will be described in more detail.

[0078] Figure 1 is a diagram showing a general waveform of the ERN according to an embodiment of the disclosure.

[0079] Referring to Figure 1 , a negative potential value is depicted above the horizontal axis, and a positive potential value is depicted below the horizontal axis. In addition, it can be confirmed that an ERP having a negative peak is generated within a predetermined time range after a response to an arbitrary movement is initiated. Here, the response can mean a case where a mistake or error occurs (error response). In addition, the predetermined time range can be about 50 ms to 150 ms. Alternatively, the predetermined time range can be about 0 ms to 100 ms. Meanwhile, in the case of a correct response, a negative peak of the generated ERP is smaller than the ERN.

[0080] The ERN, as an initial negative ERP, is time-locked until a response error occurs. Further, it is known that the ERN reflects enhanced activity of the dopaminergic system related to behavior monitoring. The ERN includes a frontal-striatal loop including the lateral dorsal tegmental area. Meanwhile, dopamine is related to a brain reward system that generally forms a certain behavior and stimulates a person, thereby providing a sense of pleasure and enhanced feeling. When a behavior that repeatedly obtains an appropriate reward is learned as a habit. In addition, more dopamine is released through emotional learning, and a new behavior is attempted due to the release of dopamine. Accordingly, reward-driven learning is called reinforcement learning.

[0081] In addition, the ERN can be generated within 0 ms to 100 ms after an error response begins, which can be caused by frontal cortical guidance during an interference task (for example, a Go-noGo task, a Stroop task, a Flanker task, and a Simon task).

[0082] Further, it is known that the ERN, together with the CRN described below, reflects a regular behavior monitoring system that can distinguish between correct behavior and error behavior.

[0083] In addition, the fact that the ERN reaches a maximum amplitude at frontal cortex electrodes reflects that the brain generator is located in the lateral cingulate zone or in the region of the dorsal anterior cingulate cortex (dACC).

[0084] In addition, the ERN can show a change in amplitude depending on the state of negative emotion.

[0085] In addition, the ERN can be reported even in the case of behavior monitoring based on external evaluation feedback processing different from internal motivation expression, and can be classified as the FRN described below.

[0086] In addition, the ERN can be generated not only when a fault or error is recognized, but also before a fault or error is recognized.

[0087] Furthermore, the ERN can be generated not only as a response to his / her own fault or error, but also as a response to the fault or error of others.

[0088] Furthermore, the ERN can be generated not only as a response to a fault or error, but also as a response to anxiety or stress about a task or object scheduled to be performed.

[0089] In addition, since a larger ERN peak is obtained, it can be considered that a larger ERN peak reflects a more serious fault or error.

[0090] Meanwhile, as an event-related potential (ERP) occurring after the ERN, for yet another example, the Pe (positive error potential) is an ERP having a positive value, which occurs mainly at frontal cortex electrodes about 150 ms to 300 ms after a fault or error occurs. The Pe is referred to as a reaction of recognizing a fault or error and paying more attention. In other words, the Pe is related to an indicator of a conscious error information processing process after error detection. The ERN and the Pe are referred to as ERPs related to error monitoring.

[0091] Hereinafter, main features of the Pe will be described in more detail.

[0092] Figure 2 FIG. 1 is a graph showing a general waveform of the ERN and the Pe according to another embodiment of the disclosure.

[0093] Referring to FIG. 1, Figure 2 A negative potential value is depicted above a positive potential value. In addition, it can be confirmed that an ERP having a negative peak, i.e., the ERN, is generated within a first predetermined time range after a response to an arbitrary movement begins. Here, the response can mean a case where a fault or error occurs (error response). In addition, the first predetermined time range can be about 50 ms to 150 ms. Alternatively, the first predetermined time range can be about 0 ms to 200 ms.

[0094] In addition, it can be confirmed that the ERP having a positive peak value, i.e., Pe, is generated within a second predetermined time range after the ERN occurs. In addition, the second predetermined time range can be about 150 ms to 300 ms after the error occurs. Alternatively, the second predetermined time range can mean about 200 ms to 400 ms.

[0095] Figure 3 is a graph showing a deflection characteristic of Pe of one embodiment of the present disclosure.

[0096] Referring to Figure 3 As with P3, Pe has a wide deflection characteristic, and the cluster generator includes not only the area of the posterior cingulate cortex and the insular cortex but also the area of the anterior cingulate cortex.

[0097] In addition, Pe can reflect emotional evaluation of the error and attention to the stimulus such as P300. In addition, ERN indicates a conflict between a correct response and an error response, and Pe is considered to be a response that is aware of the fault and causes more attention. In other words, ERN can be generated in the process of detecting the stimulus, and Pe can be generated according to attention in the process of processing the stimulus. When ERN and / or Pe have relatively large values, respectively, it is known that these values are related to adaptive behavior that aims to respond more slowly and accurately after the fault.

[0098] Figure 4A and Figure 4B is a graph showing a measurement area of ERP and Pe according to one embodiment of the present disclosure.

[0099] ERN and Pe are called ERPs related to error monitoring. With respect to the measurement area of ERN and Pe, the maximum negative value and the maximum positive value can be generally measured in the central area. However, there can be some differences depending on the measurement conditions. For example, Figure 4A is a main area to measure ERN, and the maximum negative value of ERN can be generally measured in the midline frontal lobe or the central area, i.e., FCZ. In addition, Figure 4B is a main area to measure Pe, and a large Pe positive value can be generally measured in the posterior midline area compared to ERN.

[0100] Meanwhile, for yet another example, FRN (feedback-related negative potential) is an event-related potential (ERP) related to error detection based on external evaluation feedback. ERN and / or Pe detect errors based on an internal monitoring process. However, in the case of FRN, when FRN is obtained based on external evaluation feedback, it can be similar to the process of ERN.

[0101] In addition, the FRN and the ERN can share many electrophysiological characteristics. For example, the FRN has a negative peak at frontal cortex electrodes about 250 ms to 300 ms after the start of negative feedback, and can be generated in the dorsal anterior cingulate cortex (dACC) region like the ERN.

[0102] In addition, like the ERN, the FRN can reflect the reinforcement learning activity of the dopaminergic system. In addition, the FRN generally has a greater negative value than positive feedback, and can have a greater value for unpredictable cases than predictable results.

[0103] For another example, the CRN (correctly related negative potential) is an ERP generated by a correct trial, and is a negative value smaller than the ERN. Like the ERN, the CRN can be generated in an initial waiting period (e.g., 0 ms to 100 ms). Figure 5 FIG. 1 is a diagram showing general waveforms of the ERN and the CRN showing one embodiment of the present disclosure.

[0104] For another example, the Pc (correct positive potential) is an event-related potential generated after the CRN. It is an event-related potential generated about 150 ms to 300 ms after a correct response occurs. The relationship between the CRN and the Pc can be similar to the relationship between the ERN and the Pe.

[0105] Meanwhile, the ERP can be classified into a stimulus-locked ERP and a response-locked ERP. The stimulus-locked ERP and the response-locked ERP can be divided according to standards such as the cause of evoking the ERP and the response time. For example, an ERP evoked from the moment a word or a picture is externally presented to a user can be referred to as a stimulus-locked ERP. In addition, for example, an ERP evoked from the moment a user speaks or presses a button can be referred to as a response-locked ERP. Accordingly, based on the above standards, generally, the stimulus-locked ERP is N100, N200, P2, P3, etc., and the response-locked ERP is ERN, Pe, CRN, Pc, FRN, etc.

[0106] Meanwhile, the brain waves can be classified according to the motivation exhibited. The brain waves can be classified into spontaneous brain waves (spontaneous potentials) exhibited according to the user's will and evoked brain waves (evoked potentials) naturally exhibited according to external stimuli irrelevant to the user's will. The spontaneous brain waves can be displayed when the user moves or imagines movement by himself, and the evoked brain waves can be exhibited through, for example, visual, auditory, olfactory, and tactile stimuli.

[0107] Meanwhile, the brain wave signal can be measured according to the international 10-20 system. The international 10-20 system determines a measurement point of the brain wave signal based on the relationship between the electrode position and the cerebral cortex region.

[0108] Figure 6is a diagram illustrating EEG measurement channels corresponding to brain cortex regions according to an embodiment of the disclosure.

[0109] Referring to Figure 6 , the brain regions (frontal cortex FP1, FP2; frontal cortex F3, F4, F7, F8, FZ, FC3, FC4, FT7, FT8, FCZ; parietal cortex C3, C4, CZ, CP3, CP4, CPZ, P3, P4, PZ; temporal cortex T7, T8, TP7, TP8, P7, P8; occipital cortex O1, O2, OZ) correspond to 32 brain wave measurement channels. For each channel, the data can be obtained and used to analyze each brain cortex region.

[0110] Figure 7 is a block diagram illustrating a configuration of an apparatus for determining ride comfort of a passenger based on a brain wave signal of the passenger according to an embodiment of the disclosure.

[0111] Ride comfort can generally represent overall comfort of a moving body or vibration feeling of the moving body. Major factors affecting ride comfort include a suspension, a tire, a seat, an engine, a transmission, and a moving body body rigidity. The suspension is a general term for a damping system of a moving body, such as a spring supporting the weight of the moving body, and serves to prevent transmission of vibration of a road surface to a passenger. The vibration or impact felt by the passenger in the moving body can differ depending on the setting of the suspension. In addition, the tire is a device mainly used to filter out impact generated on a road surface, and ride comfort can vary depending on the sidewall or tire pressure of the tire. In addition, for the seat, a dedicated seat considering passenger comfort has been developed beyond the function of a simple chair. In addition, the engine and the transmission can affect the vibration and noise generated in the moving body. In addition, the moving body body rigidity can affect collision stability, driving performance, and ride comfort of the passenger. In addition, factors affecting ride comfort can include seating information of the passenger and driving environment information.

[0112] As described above, various factors of the moving body can affect the ride comfort of the passenger. However, since the ride comfort refers to the overall feeling of the passenger in the driving environment, in order to define the term ride comfort, not only objective factors such as moving body information, passenger seating information, and driving environment information, but also subjective factors such as psychological comfort and stability of the passenger need to be considered.

[0113] Meanwhile, the ride comfort can be represented by a posture of the passenger caused by objective factors or a psychological state of the passenger caused by subjective factors. Or, the posture of the passenger and the psychological state of the passenger can be represented as a numerical value.

[0114] Therefore, the ride comfort of the passenger can be corrected in consideration of subjective information such as the psychological state of the passenger, using objective information of the moving body and / or the passenger.

[0115] For example, even if the ride comfort of the passenger is primarily determined as state A using objective information of the moving body and / or the passenger, the ride comfort can be finally / secondarily determined as state B or state (A+a) in consideration of the psychological state of the passenger.

[0116] As another example, even if the ride comfort of the passenger is primarily determined as "a driving posture suitable for driving" using objective information of the moving body and / or the passenger, the ride comfort can be finally determined as "a driving posture unsuitable for driving" or "a posture that does not burden the waist but causes stress" in consideration of the psychological state of the passenger.

[0117] As another example, even if the ride comfort of the passenger is primarily determined as "a driving posture unsuitable for driving" using objective information of the moving body and / or the passenger, the ride comfort can be secondarily determined as "a driving posture difficult to drive but helpful to reduce stress" in consideration of the psychological state of the passenger.

[0118] As another example, even if the ride comfort of the passenger is primarily determined as "a posture that burdens the waist" using objective information of the moving body and / or the passenger, the ride comfort can be finally determined as "a posture that burdens the waist but is comfortable" in consideration of the psychological state of the passenger.

[0119] Meanwhile, since the brain wave signal can reflect the psychological state of the passenger or a response to a certain stimulus, secondary and final information about the ride comfort of the passenger can be acquired by analyzing the brain wave signal. For example, the brain wave signal classified for each frequency band can reflect feelings such as comfort, anxiety, and stress of the passenger.

[0120] For example, an alpha wave is generally a brain wave having a frequency of 8 Hz to 12 Hz, and mainly occurs in a relaxed state. In addition, the alpha wave is mainly generated in the frontal lobe or occipital lobe, and tends to decrease when stress occurs.

[0121] As another example, a beta wave is generally a brain wave having a frequency of 13 Hz to 30 Hz, and mainly occurs in a slightly tense state or when more than a certain degree of attention is paid. In addition, the beta wave is mainly generated in the frontal lobe, and appears stronger under stress such as anxiety and tension. Therefore, the beta wave is called a stress wave.

[0122] As another example, a theta wave is generally a brain wave having a frequency of 3.5 Hz to 7 Hz, and is mainly generated in the parietal lobe and occipital lobe. In addition, the theta wave tends to increase when stress occurs.

[0123] As described above, when no stress occurs, the alpha wave can be measured as a dominant signal, and when stress occurs, the beta wave and / or the theta wave can be measured as a dominant signal.

[0124] Accordingly, by analyzing the brain wave signals of the passenger collected for a predetermined time, it is possible to determine the psychological state of the passenger. That is, it is possible to determine whether the passenger is in a comfortable state or an anxious state by detecting the amplitude of the brain wave signals of each frequency, such as the alpha wave, the beta wave, and the theta wave.

[0125] As a result, it is possible to acquire the final / secondary ride comfort information of the passenger based on information about the sitting posture of the passenger or the primary ride comfort of the passenger.

[0126] Meanwhile, the moving body can include a vehicle, a moving / transportation device, etc.

[0127] Reference Figure 7 The device 700 for determining ride comfort using brain wave signals can include an analyzer 710, a sensor 720, and / or a controller 730. However, it should be noted that only some components necessary for explaining the present embodiment are illustrated, and the components included in the device 700 for determining ride comfort of a passenger are not limited to the above-described examples. For example, two or more constituent units can be implemented in one constituent unit, and the operations performed in one constituent unit can be divided and performed in two or more constituent units. In addition, some constituent units can be omitted, or additional constituent units can be added.

[0128] The device 700 for determining ride comfort of the embodiment of the disclosure can determine first ride comfort information of a passenger based on sitting posture information about at least one passenger in a moving body. In addition, the analyzer 710 can perform the above operations.

[0129] Here, the sitting posture information about the passenger can mean various information that can be used to determine the ride comfort of the passenger. In addition, the sitting posture information about the passenger can mean objective factors that affect the ride comfort of the passenger.

[0130] For example, the sitting posture information about the passenger can include personal information of the passenger. For example, this can include physical information such as the gender, age, weight, actual height, and height after sitting of the passenger.

[0131] As another example, the sitting posture information about the passenger can include health state information of the passenger. For example, this can include the joint state, muscle fatigue, lumbar disc herniation, cervical disc herniation, etc. of the passenger.

[0132] As another example, the seating position information about the passenger can include driving environment information. For example, this can include information about a road on which the moving body is to travel, moving body model information, traffic volume information, and the like. Specifically, the information about the road on which the moving body is to travel can include information about whether the travel road is an ordinary road or an expressway, information about whether the travel road is a national road or a local road (in the case of a general road), and information about whether the travel road is a toll-free road or a toll road.

[0133] As another example, the seating position information about the passenger can include seating information of the passenger. For example, this can include moving body seat position / angle information, pressure distribution of each moving body seat back angle, passenger's pelvic angle, lumbar curvature, lumbosacral joint load, hip joint information, and the like.

[0134] As another example, the seating position information about the passenger can include past posture information of the passenger or a preset preferred seating position of the passenger.

[0135] As another example, the seating position information about the passenger can include information about a driving mode of the passenger.

[0136] Here, the first ride comfort information of the passenger can indicate objective evaluation information of the seating position of the passenger. For example, this can mean ergonomics (or anatomy) evaluation information determined using information about the passenger, health condition information, seating information of the passenger, and driving environment information.

[0137] For example, the first ride comfort information of the passenger can include "recommended posture" and "non-recommended posture".

[0138] As another example, the first ride comfort information of the passenger can include "posture suitable for driving", "posture unsuitable for driving", and "posture causing a load on the waist".

[0139] As another example, the first ride comfort information of the passenger can be expressed as a numerical value. The numerical value can indicate a numerical evaluation of the seating position of the passenger. For example, the numerical value can have a value from 0 to 100. The "posture suitable for driving" can be expressed as 100, and the "dangerous driving posture" can be expressed as 0.

[0140] As another example, each seating position information can be expressed as a numerical value. For example, the moving body seat position / angle information, the pressure distribution of each moving body seat back angle, the passenger's pelvic angle, the lumbar curvature, the lumbosacral joint load, the hip joint information, and the like can be expressed as a numerical value. In addition, the first ride comfort information can be acquired by collecting information expressed as a numerical value.

[0141] The device 700 for determining ride comfort of an embodiment of the disclosure can collect a brain wave signal of a passenger in a moving body for a predetermined time. Also, the sensor 720 can perform the above operations.

[0142] Here, the brain wave signal can mean a brain wave signal of each frequency band. For example, the brain wave signal can include an alpha wave, a beta wave, and a theta wave.

[0143] Also, the brain wave signal collected for a predetermined time can include measuring a brain wave signal of at least one passenger in a moving body, and detecting a brain wave signal of each frequency band from the measured brain wave signal.

[0144] The device 700 for determining ride comfort of an embodiment of the disclosure can determine second ride comfort information obtained by correcting the first ride comfort information by analyzing the collected brain wave signal. Also, the device 700 for determining ride comfort of an embodiment of the disclosure can determine second ride comfort information obtained by correcting the first ride comfort information by analyzing the collected brain wave signal based on the first ride comfort information. Also, the analyzer 710 can perform the above operations.

[0145] Here, the second ride comfort information of the passenger can mean information obtained by correcting the first ride comfort information. That is, it can mean evaluation information obtained by reflecting the psychological state of the passenger in the objective evaluation information of the sitting posture of the passenger.

[0146] For example, the second ride comfort information of the passenger can mean information obtained by adding evaluation information of the psychological state of the passenger to ergonomics (or anatomy) evaluation information determined using information about the passenger, health status information, seating information of the passenger, and driving environment information.

[0147] As another example, the second ride comfort information of the passenger can mean information obtained by correcting ergonomics (or anatomy) evaluation information determined using information about the passenger, health status information, seating information of the passenger, and driving environment information using evaluation information of the psychological state of the passenger.

[0148] As another example, the second ride comfort information of the passenger can include a "recommended posture" and a "non-recommended posture".

[0149] As another example, the second ride comfort information of the passenger can include a "posture suitable for driving", a "posture unsuitable for driving", and a "posture causing burden to the waist".

[0150] As another example, the second ride comfort information of the passenger can be expressed as a numerical value. The numerical value can represent a numerical evaluation of the passenger's ride posture. For example, the numerical value can have a value from 0 to 100. "A posture suitable for driving" can be expressed as 100, and "a dangerous driving posture" can be expressed as 0.

[0151] As another example, when the first ride comfort information is 50 and the second ride comfort information is "a posture causing stress", the second ride comfort information can finally be expressed as 30.

[0152] That is, when the first ride comfort information is expressed as a numerical value, the second ride comfort information is expressed as a secondary and final numerical value, which is corrected by reflecting the evaluation information of the passenger's psychological state.

[0153] As another example, even if the first ride comfort is determined as state A, the second ride comfort information of the passenger can be finally / secondarily determined as state B or (A+a) considering the passenger's psychological state. For example, the second ride comfort information of the passenger can be expressed as "a posture not causing burden to the waist but causing stress", "a posture difficult to drive but helping to reduce stress", or "a posture causing burden to the waist but comfortable".

[0154] As another example, the collected brain wave signals (e.g., alpha waves, beta waves, and theta waves) can be expressed as numerical values. In addition, the second ride comfort information can be acquired by collecting information expressed as numerical values.

[0155] Here, the analysis can include comparing the amplitude of the brain wave signal of each frequency band collected for a predetermined time with a predetermined threshold value. In addition, the analysis can include extracting the brain wave signal of each frequency band.

[0156] Here, the threshold value can be a predetermined value or a value input by the user.

[0157] Figure 8A and Figure 8B is a diagram illustrating a process of comparing the brain wave signal of each frequency band with a predetermined threshold value according to an embodiment of the present application.

[0158] For example, when the amplitude of the theta wave is greater than or equal to a predetermined threshold value, it can be determined that the state of the passenger is a stress state or a posture causing stress.

[0159] As another example, when the amplitude of the alpha wave is less than a predetermined threshold value, it can be determined that the state of the passenger is a stress state. Alternatively, when the amplitude of the alpha wave is greater than or equal to a predetermined threshold value, it can be determined that the state of the passenger is a comfortable state or a posture helping to reduce stress.

[0160] As another example, when the amplitude of the beta wave is greater than or equal to a predetermined threshold value, it can be determined that the state of the passenger is a stress state. Referring to Figure 8B In the time interval in which the amplitude of the beta wave is greater than or equal to the predetermined threshold value 810, it can be determined that the state of the passenger is a stress state.

[0161] Meanwhile, the state of the passenger can be determined by combining the brain wave signals of each frequency.

[0162] For example, when the ratio of the amplitude of the beta wave to the amplitude of the alpha wave is greater than or equal to a predetermined threshold value, it can be determined that the state of the passenger is a stress state.

[0163] As another example, when the ratio of the amplitude of the theta wave to the amplitude of the alpha wave is greater than or equal to a predetermined threshold value, it can be determined that the state of the passenger is a stress state.

[0164] As another example, when the ratio of the amplitude of the linear combination of the theta wave and the beta wave to the amplitude of the alpha wave is greater than or equal to a predetermined threshold value, it can be determined that the state of the passenger is a stress state.

[0165] As another example, only the brain wave signal having a predetermined threshold value can be selected from the brain wave signals of each frequency, and the selected brain wave signal can be used to determine the state of the passenger. That is, the selected brain wave signal can be a factor for improving the satisfaction of the ride comfort of the passenger.

[0166] Here, the amplitude of the brain wave signal of each frequency can represent the power of the frequency band within a predetermined range. That is, the amplitude of the brain wave signal of each frequency can represent the power of the frequency band transformed using the Fourier transform in the frequency domain.

[0167] Further, the analysis can include determining second ride comfort information obtained by correcting the first ride comfort information by analyzing the collected brain wave signals based on the first ride comfort information.

[0168] For example, when the ride comfort of the passenger is mainly determined as the state A using the objective information of the moving body and / or the passenger, a predetermined weight is applied to the predetermined threshold value according to the state A. The predetermined weight can be a predetermined value or a value input by the user. At this time, the brain wave signal can include an alpha wave, a beta wave, and a theta wave.

[0169] For example, when the ride comfort of the passenger is mainly determined as the "driving posture suitable for driving" using the objective information of the moving body and / or the passenger, the amplitude of the threshold value of the alpha wave that mainly occurs in a comfortable state can be set to be small.

[0170] As another example, when the ride comfort of the passenger is mainly determined as the "driving posture unsuitable for driving" using the objective information of the moving body and / or the passenger, the amplitude of the threshold value of the beta wave and / or the theta wave that tends to increase under stress can be set to be relatively small.

[0171] In addition, when the ride comfort of the passenger is mainly determined as the state A using the objective information of the moving body and / or the passenger, according to the state A, the psychological state of the passenger can be determined using only a specific brain wave signal among the brain wave signals of each frequency band.

[0172] For example, when the ride comfort of the passenger is mainly determined as the "driving posture suitable for driving" using the objective information of the moving body and / or the passenger, the psychological state of the passenger can be determined using only the alpha wave that mainly occurs in the comfortable state.

[0173] As another example, when the ride comfort of the passenger is mainly determined as the "driving posture unsuitable for driving" using the objective information of the moving body and / or the passenger, the psychological state of the passenger can be determined using only the beta wave and / or theta wave that tends to increase under stress.

[0174] The device 700 for determining the ride comfort of an embodiment of the disclosure can control the moving body based on the second ride comfort information. In addition, the controller 730 can perform the above operations.

[0175] Here, the moving body can include a predetermined device in the moving body. For example, the predetermined device can include a seat, a steering wheel, a shift lever, a pedal, a rearview mirror, a console box, a navigation device, a voice device, etc.

[0176] For example, when the second ride comfort information of the passenger is determined as the "driving posture unsuitable for driving", the angle and / or position of at least one of the seat, the steering wheel, the shift lever, the pedal, the rearview mirror, or the console box of the passenger can be controlled.

[0177] As another example, when the second ride comfort information of the passenger is determined as the "driving posture unsuitable for driving", the angle and / or position of the seat of the passenger can be displayed on the display device of the moving body. For example, when the hip point is taken as a reference point and the second ride comfort information is the "stress state", the position of the hip point is changed by the aging of the seat, the current position of the seat and the position of the seat to be adjusted (e.g., the original hip point position) can be displayed on the display device.

[0178] As another example, when the second ride comfort information of the passenger is determined as the "driving posture unsuitable for driving", the recommended seat angle and / or position of the passenger can be displayed on the display device of the moving body. The recommended seat angle and / or position can be a past position level of the passenger or a preset preferred sitting posture of the passenger.

[0179] As another example, when the second ride comfort information of the passenger is determined as the "driving posture unsuitable for driving", the voice device of the moving body can provide a notification indicating that the current posture is unsuitable for driving.

[0180] As another example, when the second ride comfort information of the passenger is determined as a "stress state", the passenger's hips can be checked, or the current posture of the passenger can be compared with the passenger's preset preferred sitting posture. In addition, information about the original hip point position or the preferred sitting posture of the passenger can be provided.

[0181] As another example, at the time of re-riding after the driving is completed, the second ride comfort information determined during the driving can be provided to the passenger. Alternatively, when the passenger gets on the moving body again after getting off the moving body, the second ride comfort information determined before getting off the moving body can be provided to the passenger.

[0182] Figure 9 is a flowchart illustrating an operation method of an apparatus for determining a ride comfort of a user according to an embodiment of the present application.

[0183] In step S901, first ride comfort information of a passenger can be determined based on information about a sitting posture of the passenger in a moving body.

[0184] Here, the sitting posture information about the passenger can mean various information that can be used to determine the ride comfort of the passenger. In addition, the sitting posture information about the passenger can mean objective factors that affect the ride comfort of the passenger.

[0185] Further, the first ride comfort information of the passenger can mean objective evaluation information of the sitting posture of the passenger. For example, this can mean ergonomics (or anatomy) evaluation information determined using information about the passenger, health condition information, seating information of the passenger, and driving environment information.

[0186] In step S902, a brain wave signal of a passenger in a moving body can be collected for a predetermined time.

[0187] Here, the brain wave signal can represent a brain wave signal of each frequency band. For example, the brain wave signal can include an alpha wave, a beta wave, and a theta wave.

[0188] In step S903, second ride comfort information obtained by correcting the first ride comfort information can be determined by analyzing the collected brain wave signal based on the first ride comfort information.

[0189] Here, the second ride comfort information of the passenger can represent information obtained by correcting the first ride comfort information. That is, this can mean evaluation information obtained by reflecting the psychological state of the passenger in the objective evaluation information of the sitting posture of the passenger.

[0190] Here, the analysis can include comparing the amplitude of the brainwave signal of each frequency band collected for a predetermined time with a predetermined threshold. In addition, the analysis can include extracting the brainwave signal of each frequency band.

[0191] For example, when the ride comfort of the passenger is mainly determined as the state A using the objective information of the moving body and / or the passenger, a predetermined weight is applied to the predetermined threshold according to the state A.

[0192] In step S904, the moving body can be controlled based on the second ride comfort information.

[0193] Here, the moving body can include a predetermined device in the moving body. For example, the predetermined device can include a seat, a steering wheel, a shift lever, a pedal, a rearview mirror, a console box, a navigation device, a voice device, etc.

[0194] For example, when the second ride comfort information of the passenger is determined as an "unfit driving posture", the angle and / or position of at least one of the seat, the steering wheel, the shift lever, the pedal, the rearview mirror, or the console box of the passenger can be controlled.

[0195] According to an embodiment of the present disclosure, it is possible to provide an apparatus and a method for determining the ride comfort of a passenger of a moving body based on a brainwave signal of the passenger of the moving body.

[0196] In addition, according to an embodiment of the present disclosure, it is possible to provide an apparatus and a method for controlling a moving body internal device related to a sitting posture of a passenger based on a brainwave signal of the passenger in the moving body.

[0197] Although exemplary methods of embodiments of the present disclosure are described as a series of operational steps for clarity of description, embodiments of the present disclosure are not limited to the order or sequence of the operational steps described above. The operational steps can be performed simultaneously or sequentially in different orders. Additional operational steps can be added and / or existing operational steps can be removed or replaced for implementing the methods of embodiments of the present disclosure.

[0198] Various embodiments of the present disclosure are not presented to describe all available combinations, but are presented to describe only representative combinations. Steps or elements of various embodiments can be used alone or can be combined.

[0199] In addition, various embodiments of the present disclosure can be embodied in the form of hardware, firmware, software, or a combination thereof. When the embodiments of the present disclosure are embodied in hardware components, it can be, for example, an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a general purpose processor, a controller, a microcontroller, a microprocessor, etc.

[0200] The scope of embodiments of the present disclosure includes software or machine- executable instructions (e.g., operating systems (OS), applications, firmware, programs) that enable the methods of various embodiments to be performed on a device or computer, and non-transitory computer-readable media for storing such software or machine-executable instructions so that the software or instructions can be executed on a device or computer.

[0201] The description of embodiments of the present disclosure is merely exemplary in nature and, thus, variations that do not depart from the essence of the present disclosure are intended to be within the scope of the present disclosure. Such variations are not to be regarded as a departure from the spirit and scope of the present disclosure.

[0202] While the application has been described with reference to illustrative embodiments, the description is not intended to be interpreted in a limiting sense. Various modifications and combinations of the illustrative embodiments along with other embodiments of the application will be apparent to persons skilled in the art upon reference to the description. It is therefore intended that the appended claims encompass any such modifications or embodiments.

Claims

1. An apparatus for determining ride comfort of a passenger using a brain wave signal, the apparatus comprising: an analyzer configured to determine first ride comfort information of at least one passenger based on information about a sitting posture of a passenger in a moving body; a sensor configured to collect a brain wave signal of the passenger in the moving body for a predetermined time; and a controller configured to control the moving body, wherein the analyzer is configured to determine second ride comfort information obtained by correcting the first ride comfort information by analyzing the collected brain wave signal based on the first ride comfort information, wherein the controller is configured to control the moving body based on the determined second ride comfort information, and wherein, when the first ride comfort information of the passenger has a first value, the analyzer is configured to determine the second ride comfort information using a predetermined brain wave signal corresponding to a comfort level indicated by the first value among the brain wave signal. The information about the sitting posture of the passenger includes at least one of personal information of the passenger, health condition information of the passenger, seating information of the passenger, past posture information of the passenger, a preferred sitting posture of the passenger, and driving environment information.

2. The apparatus of claim 1, wherein, The first ride comfort information of the passenger is evaluation information of the sitting posture of the passenger.

3. The apparatus of claim 1, wherein, 4.The apparatus of claim 1, wherein: the brain wave signal includes a theta wave; and the analyzer is configured to determine that a state of the passenger is a stress state when an amplitude of the theta wave is greater than or equal to a predetermined threshold. 5.The apparatus of claim 1, wherein: the brain wave signal includes an alpha wave; and the analyzer is configured to determine that a state of the passenger is a comfort state when an amplitude of the alpha wave is greater than or equal to a predetermined threshold. 6.The apparatus of claim 1, wherein: the brain wave signal includes a beta wave; and the analyzer is configured to determine that a state of the passenger is a stress state when an amplitude of the beta wave is greater than or equal to a predetermined threshold. The moving body includes at least one of a seat, a steering wheel, a rearview mirror, a console box, a navigation device, and a voice device.

7. The apparatus of claim 1, wherein, When the second ride comfort information of the passenger is determined to be a first state, the controller is configured to control an angle of at least one of the seat, the steering wheel, the rearview mirror, and the console box of the passenger.

8. The apparatus of claim 7, wherein, When the second ride comfort information of the passenger is determined to be a second state, the controller is configured to control a position of at least one of the seat, the steering wheel, the rearview mirror, and the console box of the passenger.

9. The apparatus of claim 7, wherein, 10.A method for determining ride comfort of a passenger using a brain wave signal, the method comprising: determining first ride comfort information of at least one passenger based on information about a sitting posture of a passenger in a moving body; collecting a brain wave signal of the passenger in the moving body for a predetermined time; and controlling the moving body based on the determined second ride comfort information. determining second ride comfort information obtained by correcting the first ride comfort information, by analyzing the collected brainwave signals based on the first ride comfort information; and controlling the moving body based on the determined second ride comfort information; wherein, when the first ride comfort information of the passenger has a first value, the second ride comfort information is determined using predetermined brainwave signals in the brainwave signals corresponding to a comfort level indicated by the first value.

11. The method of claim 10, wherein, The information about the sitting posture of the passenger includes at least one of personal information of the passenger, health condition information of the passenger, seating information of the passenger, past posture information of the passenger, a preferred sitting posture of the passenger, and driving environment information.

12. The method of claim 10, wherein, The first ride comfort information of the passenger is evaluation information of the sitting posture of the passenger.

13. The method of claim 10, wherein: The brainwave signals include theta waves; and Determining the second ride comfort information includes determining that a state of the passenger is a stress state when an amplitude of the theta waves is greater than or equal to a predetermined threshold value.

14. The method of claim 10, wherein: The brainwave signals include alpha waves; and Determining the second ride comfort information includes determining that a state of the passenger is a comfortable state when an amplitude of the alpha waves is greater than or equal to a predetermined threshold value.

15. The method of claim 10, wherein: The brainwave signals include beta waves; and Determining the second ride comfort information includes determining that a state of the passenger is a stress state when an amplitude of the beta waves is greater than or equal to a predetermined threshold value.

16. The method of claim 10, wherein, The moving body includes at least one of a seat, a steering wheel, a rearview mirror, a console box, a navigation device, and a voice device.

17. The method of claim 16, wherein, When the second ride comfort information of the passenger is determined to be a first state, controlling the moving body includes controlling an angle of at least one of the seat, the steering wheel, the rearview mirror, and the console box of the passenger.

18. The method of claim 16, wherein, When the second ride comfort information of the passenger is determined to be a second state, controlling the moving body includes controlling a position of at least one of the seat, the steering wheel, the rearview mirror, and the console box of the passenger.

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