Mobile tool user authentication apparatus and method using brain waves

Abstract

The present invention relates to a mobile tool user authentication device and method using brain waves. The mobile tool user authentication device using brain wave signals includes a receiver configured to receive a predetermined user input from a passenger of a mobile tool, a display configured to display a preset image list to the passenger on a predetermined area of the mobile tool based on the received user input, a sensor configured to collect brain wave signals of the passenger for a predetermined time in response to the displayed image list, and a controller configured to perform authentication on the passenger by analyzing the collected brain wave signals.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority from Korean Patent Application No. 10-2019-0153683, filed on November 26, 2019, which is hereby incorporated by reference herein. Technical Field

[0003] The invention relates to a method and device for controlling a mobile tool. Background Art

[0004] The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.

[0005] As one of the means of transportation, vehicles (or mobile tools) are very important means and tools in life in the modern world. In addition, for some people, mobile tools themselves can be regarded as things with special meaning.

[0006] As technology advances, the functionality provided by mobility devices is also evolving. For example, in recent years, mobility devices have evolved beyond simply transporting passengers to their destinations to meet their needs for faster and safer travel. Furthermore, new devices are constantly being added to mobility systems to cater to passengers' aesthetic tastes and comfort. Furthermore, existing devices such as steering wheels, transmissions, and acceleration / deceleration devices are being developed to provide users with more functionality.

[0007] Meanwhile, brain-computer interfaces (BCIs) or brain-machine interfaces (BMIs) are fields that utilize brainwave signals to control computers or machines based on human intentions. ERP (Event-Related Potential) is closely related to cognitive function. Summary of the Invention

[0008] The present invention relates to a method and device for controlling a mobile tool. Specific embodiments relate to a method and device for controlling a mobile tool.

[0009] Embodiments of the present invention provide an apparatus and method for authenticating a user of a mobile tool based on a passenger's brainwave signals.

[0010] Another embodiment of the present invention provides an apparatus and method for performing authentication on a passenger by analyzing a brainwave signal of the passenger in response to an image displayed on a display of a mobile tool.

[0011] The embodiments of the present invention are not limited to the above-mentioned embodiments, 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, a mobile means user authentication device using brainwave signals can be provided. The device can include a receiver for receiving a predetermined user input from a passenger of a mobile means, a display for displaying a preset image list to the passenger on a predetermined area of the mobile means based on the received user input, a sensor for collecting brainwave signals of the passenger for a predetermined time in response to the displayed image list, and a controller for performing authentication of the passenger by analyzing the collected brainwave signals.

[0013] The passenger can be a person sitting on a driver's seat of the mobile means.

[0014] The passenger can sit on a first seat of the mobile means, and the first seat can be occupied by the passenger who plays a leading role in control of the mobile means.

[0015] The user input can be a pressure having a predetermined degree or more, which is applied to at least one of a start button, a brake pedal, a seat, and a steering wheel of the mobile means.

[0016] The image list can include at least one or more images having different image characteristics.

[0017] The image characteristics can include at least one of chroma, color depth, brightness, contrast, sharpness, softness, and content information.

[0018] The image list can include images having a predetermined relevance to the passenger.

[0019] The images having a predetermined relevance to the passenger can be images that activate brainwave signals in the frontal lobe or certain regions of the frontal lobe of the passenger.

[0020] The number of images constituting the image list can be set by the user input, or can be preset in the mobile means.

[0021] The display can display at least one or more images constituting the image list in a sequential order on the predetermined area of the mobile means.

[0022] The at least one or more images can be at least some of all images constituting the image list.

[0023] The display can display each of the at least one or more images on the predetermined area for a predetermined time.

[0024] The predetermined time can be different for each of the at least one or more images.

[0025] The predetermined area can include an area of at least one of a display capable of being projected, a head-up display (HUD), and a navigation display in the mobile means.

[0026] The sensor can collect the brainwave signals of the passenger who gazes at the image displayed on the predetermined area of the mobile means for a predetermined time.

[0027] The collected brainwave signals can be brainwave signals in at least one of a time domain, a frequency domain, and a spatial domain.

[0028] The analysis can include determining whether the brainwave signal characteristic information collected for the predetermined time is similar to the pre-stored brainwave signal characteristic information of each passenger.

[0029] The brainwave signal characteristic information of each passenger can be pre-learned brainwave signal characteristic information of each passenger corresponding to each image of the image list.

[0030] For at least one image of the image list, the determination of the similarity can be determined based on whether the number of results of determining that the brainwave signal characteristic information of each passenger is similar to the pre-stored brainwave signal characteristic is greater than or equal to a predetermined value.

[0031] For at least one image of the image list, the determination of the similarity can be determined by using a predetermined number of images having a higher priority.

[0032] The controller can further include providing the passenger with a result of authentication of the passenger.

[0033] In addition, according to an embodiment of the present invention, a mobile means user authentication method using brainwave signals can be provided. The method can include receiving a predetermined user input from a passenger of a mobile means; displaying a pre-set image list to the passenger on a predetermined area of the mobile means based on the received user input; collecting brainwave signals of the passenger for a predetermined time in response to the displayed image list; and performing authentication of the passenger by analyzing the collected brainwave signals.

[0034] The passenger can be a person who sits on a driver's seat of the mobile means.

[0035] The passenger can sit on a first seat of the mobile means, and the first seat can be occupied by a passenger who plays a leading role in control of the mobile means.

[0036] The user input can be a pressure having a predetermined degree or more, which is applied to at least one of a start button, a brake pedal, a seat, and a steering wheel of the mobile means.

[0037] The image list can include at least one or more images having different image characteristics.

[0038] The image characteristics can include at least one of chroma, color depth, brightness, contrast, sharpness, softness, and content information.

[0039] The image list can include images having predetermined relevance to the passenger.

[0040] The image having predetermined relevance to the passenger can be an image that activates the brain wave signal in the frontal lobe or certain regions of the frontal lobe of the passenger.

[0041] The number of images constituting the image list can be set by user input, or can be preset in the mobile means.

[0042] The display on the predetermined region of the mobile means can be to display at least one or more images constituting the image list in a sequential order on the predetermined region of the mobile means.

[0043] The at least one or more images can be at least some of all images constituting the image list.

[0044] The display on the predetermined region of the mobile means can be to display each of the at least one or more images on the predetermined region for a predetermined time.

[0045] The predetermined time can be different for each of the at least one or more images.

[0046] The predetermined region can include a region of at least one of a display capable of projection, a head-up display (HUD), and a navigation display in the mobile means.

[0047] The collection of the predetermined time can be to collect the brain wave signal of the passenger gazing at the image displayed on the predetermined region of the mobile means for a predetermined time.

[0048] The collected brain wave signal can be a brain wave signal in at least one of a time domain, a frequency domain, and a spatial domain.

[0049] The analysis can include determining whether the brain wave signal characteristic information collected for a predetermined time is similar to the pre-stored brain wave signal characteristic information of each passenger.

[0050] The brain wave signal characteristic information of each passenger can be pre-learned brain wave signal characteristic information of each passenger corresponding to each image of the image list.

[0051] For at least one image of the image list, the determination of the similarity can be determined based on whether the number of results of determining that the brain wave signal characteristic information of each passenger is similar to the pre-stored brain wave signal characteristic is greater than or equal to a predetermined value.

[0052] For at least one image of the image list, the determination of the similarity can be determined by using a predetermined number of images having a higher priority.

[0053] The authentication of the passenger can further include providing the passenger with a result of the authentication of the passenger.

[0054] The features briefly described above in relation to the embodiments of the present application are merely exemplary aspects of the following detailed description of the present application and do not limit the scope of the present application.

[0055] According to the embodiments of the present application, an apparatus and a method for authenticating a user of a mobile means based on a brainwave signal of a passenger can be provided.

[0056] In addition, according to the embodiments of the present application, an apparatus and a method for performing authentication of a passenger by analyzing a brainwave signal of the passenger in response to an image displayed on a mobile means can be provided.

[0057] Effects obtained in the embodiments of the present application are not limited to the above-mentioned effects, and other effects not mentioned above will be clearly understood by those skilled in the art from the above description. BRIEF DESCRIPTION OF DRAWINGS

[0058] In order to be able to better understand the present application, various embodiments of the present application will be described by giving examples with reference to the accompanying drawings, in which:

[0059] Figure 1 is a graph showing a general waveform of an ERN of one embodiment of the present application;

[0060] Figure 2 is a graph showing a general waveform of an ERN and a Pe according to one embodiment of the present application;

[0061] Figure 3 is a graph showing a deflection characteristic of a Pe according to another embodiment of the present application;

[0062] Figure 4A and Figure 4B are graphs showing measurement regions of an ERP and a Pe, respectively, of one embodiment of the present application;

[0063] Figure 5 is a graph showing a general waveform of an ERN and a CRN according to one embodiment of the present application;

[0064] Figure 6 is a graph showing EEG measurement channels corresponding to brain cortex regions of one embodiment of the present application;

[0065] Figure 7 is a block diagram showing a configuration of an apparatus for performing user authentication based on a brainwave signal of a passenger according to one embodiment of the present application;

[0066] Figure 8A andFigure 8B is a graph showing EEG characteristics of a user gazing at an image having different characteristics according to an embodiment of the present application;

[0067] Figure 9 is a flowchart showing a method of operating a user authentication device according to an embodiment of the present application. DETAILED DESCRIPTION

[0068] The following description is merely exemplary in nature and is not intended to limit the present application, application, or uses. It should be understood that throughout the drawings, corresponding reference numerals indicate like or corresponding parts and features.

[0069] Exemplary embodiments of the present application will be described in detail, with reference to the accompanying drawings, so as to allow those skilled in the art to easily implement and practice the apparatus and method provided by the embodiments of the present application. However, the present application can be implemented in various ways, and the scope of the present application should not be construed as being limited to the exemplary embodiments.

[0070] In describing the embodiments of the present application, when it is deemed that a well-known function or configuration can obscure the spirit of the present application, detailed description thereof will not be made.

[0071] In the embodiments of the present application, it will be understood that when an element is referred to as being "connected to", "coupled to" or "combined to" another element, it can be directly connected to or coupled to or combined to 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 application, the terms "comprise", "include", "have" and the like indicate the presence of the described features, numbers, steps, operations, elements, components and / or combinations thereof, but do not exclude the presence or addition of one or more other features, numbers, steps, operations, elements, components and / or combinations thereof.

[0072] 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 element, and do not indicate the order 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 application. Similarly, a second element can also be termed a first element.

[0073] In the embodiments of the present application, different elements are called in order to clearly describe the features of various elements, and do not mean that the elements are physically separated from each other. That is, a plurality of different elements can be combined into a single hardware unit or a single software unit, or 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 present application. Also, terms such as "unit" or "module" should be understood as a unit that processes at least one function or operation and can be implemented as a hardware manner (for example, a processor), a software manner, or a combination of a hardware manner and a software manner.

[0074] In the embodiments of the present application, all constituent elements described in various forms should not be interpreted as essential elements, and some 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 present application. In addition, embodiments configured by adding one or more elements to various elements also fall within the scope of the present application.

[0075] As the brain electrical activity of neurons constituting the brain, an electroencephalogram signal (or brain signal, brain wave) represents a biological signal that directly and indirectly reflects a conscious state or an unconscious state of a person. The electroencephalogram signal can be measured in each region of the scalp of a human body, has a wavelength frequency of mainly 30 Hz or less, and has a potential difference of several microvolts. According to brain activity and state, various waveforms can occur. Research is being conducted to control an interface using an electroencephalogram signal according to a person's intention. The electroencephalogram signal can be obtained by using Electro Encephalo Graphy (EEG) that utilizes an electrical signal caused by brain activity, Magneto Encephalo Graphy (MEG) that utilizes a magnetic signal occurring together with the electrical signal, functional Magnetic Resonance Imaging (fMRI), or functional Near-Infrared Spectroscopy (fNIRS) that utilizes a change in oxygen saturation in blood. Although the fMRI and the fNIRS are useful techniques for measuring brain activity, generally, the fMRI has a low temporal resolution and the fNIRS has a low spatial resolution. Due to these limitations, the EEG signal is widely used due to good portability and temporal resolution.

[0076] The electroencephalogram signal varies in space over time according to brain activity. Since the electroencephalogram signal is generally difficult to analyze and its waveform is not easily analyzed visually, various processing methods have been proposed.

[0077] For example, the brain wave signal can be divided (power spectrum division) based on a frequency band according to the number of oscillations (frequency). The division regards the measured brain wave signal as a linear sum of simple signals at each specific frequency, decomposes the signal into each frequency component, and indicates the corresponding amplitude. By using pre-processing commonly used for noise cancellation, Fourier transform to the frequency domain, and a band-pass filter (BPF), the brain wave signal at each frequency can be obtained.

[0078] More specifically, according to the frequency band, the brain wave can be divided into a delta wave, a theta wave, an alpha wave, a beta wave, and a gamma wave. The delta wave is a brain wave having a frequency of 3.5 Hz or less and an amplitude of 20 μV ~ 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 ~ 7 Hz, which mainly occurs in a state of emotional stability or sleep.

[0079] In addition, the theta wave is mainly generated in the parietal lobe and occipital lobe, and can occur in calm concentration during reminiscence or meditation. In general, the alpha wave is a brain wave having a frequency of 8 Hz ~ 12 Hz, which mainly occurs in a state of relaxation and comfort. In addition, the alpha wave is usually generated in the occipital lobe at rest, and can decrease in sleep. In general, the beta wave is a brain wave having a frequency of 13 Hz ~ 30 Hz, which mainly occurs in a state of tolerable tension, or occurs when a certain degree of attention is given. In addition, the beta wave is mainly generated in the frontal lobe, and is related to a state of wakefulness or concentration of brain activity, a pathological phenomenon, and a drug effect. The beta wave can occur in a wide area of the entire brain. In addition, specifically, the beta wave can be divided into an SMR wave having a frequency of 13 Hz ~ 15 Hz, a middle beta wave having a frequency of 15 Hz ~ 18 Hz, and a high beta wave having a frequency of 20 Hz or more. Since the beta wave seems to be stronger under stress such as anxiety and tension, it is called a stress wave. The gamma wave is a brain wave usually having a frequency of 30 Hz ~ 50 Hz, which mainly occurs in a state of strong excitement or a high-level cognitive information processing process. In addition, the gamma wave can occur in a state of conscious wakefulness and during REM sleep, and can overlap with the beta wave.

[0080] Each brain wave signal depending on 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 suppression. Thus, the properties of each frequency band of brain wave signals selectively show a specific cognitive function. In addition, in each measurement site on the head surface, the brain wave signals of each frequency band can show somewhat different appearances. The cerebral cortex can be divided into frontal cortex, parietal cortex, temporal cortex, and occipital cortex. These sites can have somewhat different roles. For example, the occipital cortex corresponding to the back of the head has a primary 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.

[0081] In addition, for another example, brain wave signals can be analyzed by utilizing an event-related potential (ERP). The ERP is a change in brain electricity associated with external stimulation or internal mental processes. The ERP refers to a signal including brain electrical activity of the brain caused by stimulation at a certain time after the appearance of the stimulation, which includes specific information (e.g., images, speech, sound, execution commands, etc.).

[0082] In order to analyze the ERP, a process of separating a signal from noise is required. The mean method can be mainly used. In particular, by taking a mean of brain waves measured based on the stimulation start time, brain waves unrelated to the stimulation can be removed, and only relevant potentials, i.e., brain activities generally associated with stimulation processing, are selected.

[0083] Since the ERP has a high temporal resolution, it is closely related to the study of cognitive functions. The ERP is an electrical phenomenon induced by external stimulation or associated with internal states. According to the type of stimulation, the ERP can be divided into auditory-related potentials, visual-related potentials, somatosensory-related potentials, and olfactory-related potentials. According to the nature of the stimulation, the ERP can be divided into exogenous ERP and endogenous ERP. The waveform of the exogenous ERP is determined by external stimulation, is associated with automatic processing, and mainly occurs in the initial stage of being stimulated. For example, the exogenous ERP is a brainstem potential. On the other hand, the endogenous ERP is determined by internal cognitive processes or mental processes or states, is independent of the stimulation, and is associated with a "controlled process". For example, the endogenous ERP is P300, N400, P600, Contingent Negative Variation (CNV), etc.

[0084] The name of the ERP peak generally includes the polarity and latency, and each peak of each signal has a respective definition and meaning. For example, a positive potential is P, a negative potential is N, and P300 indicates a positive peak measured about 300 ms after the start of the 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 potential in the waveform after the start of the stimulus.

[0085] Hereinafter, various ERPs will be described.

[0086] For example, N100 is related to a response to an unpredictable stimulus.

[0087] Mismatch Negativity (MMN) can be generated not only by a focused stimulus but also by a non-focused stimulus. MMN can be used as an indicator of whether sensory memory (echoic memory) operates before initial attention. P300 to be described below appears in the process of attention and judgment, as a process occurring in the brain before attention, and MMN is analyzed.

[0088] For another example, N200 (or N2) is mainly generated according to visual stimuli and auditory stimuli, and is related to short-term memory or long-term memory, which is a type of memory after attention, together with P300 described below.

[0089] For another example, P300 (or P3) mainly reflects attention to a stimulus, stimulus cognition, memory search, and reduction of uncertainty, and is related to a perceptual decision to distinguish external stimuli. Since the generation of P300 is related to cognitive function, 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.

[0090] For another example, N400 is related to language processing and is induced 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.

[0091] For another example, P600 is related to a process of more accurately processing a stimulus based on information stored in long-term memory, as an indicator of a reconstruction process or a recovery process.

[0092] For another example, CNV refers to a potential that appears for 200 ms to 300 ms or even several seconds in a subsequent stage. It is also called a slow potential (SP) and is related to expectation, preparation, psychological readiness, association, attention, and motor activity.

[0093] For another example, the ERN (Error Related Negativity) or Ne (Error Negativity) is an event-related potential (ERP) generated by a mistake or error. It 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 a negative peak thereof occurs mainly in a frontal region and a central region about 50 ms to 150 ms after the response. In particular, it 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.

[0094] Hereinafter, main features of the ERN will be described in more detail.

[0095] Figure 1 FIG. 1 is a graph showing a typical waveform of the ERN according to one embodiment of the present application.

[0096] Reference 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 the ERP having a negative peak is generated in a predetermined time range after the response to an arbitrary movement is started. 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 to 100 ms. Further, in a case of a correct response, the generated ERP has a relatively smaller negative peak than the ERN.

[0097] As an ERP of an initial negative wave, the ERN is locked until an error response occurs. Further, it is known that the ERN reflects reinforcement activity of a dopaminergic system related to behavior monitoring. The ERN includes a frontal-striatal loop, which includes a rostral cingulate region. Meanwhile, dopamine is related to a brain reward system that generally forms a certain behavior and motivates a person, thereby providing a feeling of pleasure and fulfillment. 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.

[0098] In addition, the ERN can be generated within 0 to 100 ms after an error response starts during reading of an executive interference task (for example, a Go-noGo task, a Stroop task, a Flanker task, and a Simon task) through a frontal cortex.

[0099] In addition, together with the CRN described below, the ERN is known to reflect a general behavior monitoring system that can distinguish between correct behavior and incorrect behavior.

[0100] In addition, the fact that the ERN reaches a maximum amplitude at frontal cortex electrodes reflects the fact that the brain generator is located in the rostral cingulate region or the dorsal anterior cingulate cortex (dACC) region.

[0101] In addition, the ERN can show amplitude changes depending on the negative emotional state.

[0102] In addition, the ERN can be reported even in the case of behavioral monitoring based on external evaluation feedback processing (different from internal motor expression), and can be classified as the FRN described below.

[0103] In addition, the ERN can be generated not only when a mistake or error is recognized, but also before a mistake or error is recognized.

[0104] In addition, the ERN can be generated not only as a response to a mistake or error of his / her own, but also as a response to a mistake or error of another person.

[0105] In addition, the ERN can be generated not only as a response to a mistake or error, but also as a response to a predetermined execution task or anxiety or stress of a subject.

[0106] In addition, when a larger ERN peak is obtained, it can be considered to reflect a more serious mistake or error.

[0107] In addition, for another example, as an event-related potential (ERP) generated after the ERN, the Pe (positive error wave) is an ERP having a positive value, which is mainly generated at frontal cortex electrodes within about 150 ms ~ 300 ms after a mistake or error. It is known that the Pe is a reaction of recognizing a mistake or error and giving 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 called ERPs related to error monitoring.

[0108] Hereinafter, the main characteristics of the Pe will be described in more detail.

[0109] Figure 2 FIG. 1 is a graph showing a typical waveform of the ERN and the Pe according to the present application.

[0110] Reference Figure 2, the negative potential value is displayed above the positive potential value. In addition, it can be confirmed that the ERP having a negative peak value (i.e., ERN) is generated within a first predetermined time range after the response to the arbitrary movement is started. Here, the response can mean a case where a mistake or an 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 to 200 ms.

[0111] 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 is started. In addition, the second predetermined time range can be about 150 ms to 300 ms after the error is started. Alternatively, the second predetermined time range can mean about 200 ms to 400 ms.

[0112] Figure 3 is a graph showing a deflection characteristic of the Pe of one embodiment of the present application.

[0113] Reference Figure 3 As with the P3, the Pe also has a wide deflection characteristic, and the neural mass generator includes not only the posterior cingulate cortex region and the insular cortex region but also a more anterior cingulate cortex region.

[0114] In addition, the Pe can reflect an emotional evaluation of the error and the attention to the stimulus as with the P300. In addition, the ERN represents a conflict between the correct response and the error response, and the Pe is considered to be a response of realizing the mistake and paying more attention. In other words, the ERN is generated in the process of detecting the stimulus, and the Pe is generated according to the attention in the process of processing the stimulus. When the ERN and / or the Pe have relatively large values, respectively, these values are known to be related to adaptive behavior that aims to respond more slowly and more accurately after the mistake.

[0115] Figure 4A and Figure 4B is a graph showing a measurement region of the ERP and the Pe according to one embodiment of the present application.

[0116] The ERN and the Pe are considered to be ERPs related to error monitoring. Regarding the measurement region of the ERN and the Pe, the maximum negative value and the maximum positive value can be generally measured in the central region. However, there can be some differences depending on the measurement conditions. For example, Figure 4A is a main region of measuring the ERN, and the maximum negative value of the ERN can be generally measured in the midline frontal lobe or the central region (i.e., FCZ). In addition, Figure 4B is a main region of measuring the Pe, and a larger Pe positive value can be generally measured in the posterior midline region compared to the ERN.

[0117] Furthermore, for yet another example, the FRN (feedback-related negativity) is an event-related potential (ERP) that correlates with error detection obtained based on external evaluative feedback. The ERN and / or Pe are based on an internal monitoring process to detect errors. However, in the case of the FRN, when the FRN is obtained based on external evaluative feedback, it can operate similarly to the ERN process.

[0118] In addition, the FRN and the ERN can share many electrophysiological properties. For example, the FRN has a negative peak at frontal cortex electrodes within about 250 ms to 300 ms after the onset of negative feedback, and can be generated in the dorsal anterior cingulate cortex (dACC) region as the ERN.

[0119] In addition, as with 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.

[0120] For another example, the CRN (correct-related negativity) is an ERP generated by a correct trial, and is a negative value smaller than the ERN. As with the ERN, the CRN can be generated in an initial latency (e.g., 0 to 100 ms). Figure 5 FIG. 1 is a graph showing a typical waveform of the ERN and the CRN, which illustrates one embodiment of the present application.

[0121] For another example, the Pc (correct positivity) is an event-related potential generated after the CRN. It is an event-related potential generated within about 150 ms to 300 ms after the onset of a correct response. The relationship between the CRN and the Pc can be similar to the relationship between the ERN and the Pe.

[0122] In addition, the ERP can be divided into a stimulus-locked ERP and a response-locked ERP. The stimulus-locked ERP and the response-locked ERP can be divided according to criteria 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. Thus, based on the above criteria, generally, the stimulus-locked ERP is N100, N200, P2, P3, etc., and the response-locked ERP is ERN, Pe, CRN, Pc, FRN, etc.

[0123] Further, the brain waves can be classified according to expressed motivation. The brain waves can be classified into spontaneous brain waves (spontaneous potentials) expressed by the user's will and evoked brain waves (evoked potentials) naturally expressed according to external stimulation, which are irrelevant to the user's will. The spontaneous brain waves are expressed when the user moves or imagines moving by himself / herself, and the evoked brain waves are expressed through, for example, visual, auditory, olfactory, and tactile stimulation.

[0124] Further, the brain wave signals can be measured according to the international 10-20 system. The international 10-20 system determines the measurement points of the brain wave signals according to the relationship between the electrode positions and the cerebral cortex regions.

[0125] Figure 6 FIG. 1 is a diagram illustrating EEG measurement channels corresponding to cerebral cortex regions according to an embodiment of the present application.

[0126] Referring to FIG. 1, 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, data can be obtained and each cerebral cortex region can be analyzed by using the data.

[0127] Figure 7 FIG. 2 is a block diagram illustrating a configuration of an apparatus for performing user authentication based on a passenger brain wave signal according to an embodiment of the present application.

[0128] In recent years, the advent of autonomous mobile tools and the increase in the amount of research on next-generation intelligent transportation systems (cooperative-intelligent transport systems, C-ITS) highlight the importance of mobile tool authentication or driver (or user) authentication or both. Specifically, in the case of driver authentication, since each operation of the mobile tool is determined by the driver, whether the driver is authenticated is an important issue. Further, driver authentication is necessary to prevent various mobile tool crimes, including theft in the mobile tool.

[0129] Embodiments of the present application can provide an apparatus and a method for authenticating a mobile tool driver by using a brain wave signal.

[0130] As a type of biometric identification, user authentication using brainwave signals has the characteristics of versatility, specificity, ease of collection, and low likelihood of forgery. In other words, some biometric identification technologies are not suitable for people with specific diseases or conditions, while technologies using brainwave signals can utilize brainwaves that anyone has (versatility). In addition, brainwave signals are unique to each person (specificity). In addition, brainwave signals (especially EEG) are not difficult to collect (easy to collect). At the same time, brainwave signals are difficult to forge, especially in authentication (low likelihood of forgery).

[0131] The mobile tool user authentication device of an embodiment of the present invention can provide a preset image list to a passenger to perform user authentication. In addition, the mobile tool user authentication device of the present invention can perform user authentication by analyzing the brain wave signal generated by the passenger in response to the provided image list.

[0132] refer to Figure 7 , the mobile tool user authentication device 700 may include a receiver 710, a display 720, a sensor 730 and / or a controller 740. However, it should be noted that only some components required for explaining the present embodiment are shown, and the components included in the mobile tool user authentication device 700 are not limited to the above examples. For example, two or more component units may be implemented in one component unit, and the operations performed in one component unit may be divided and implemented in two or more component units. In addition, some component units may be omitted, or additional component units may be added.

[0133] The mobile tool user authentication device 700 of the embodiment of the present invention can receive a predetermined user input from a passenger of the mobile tool. In addition, the receiver 710 can perform the operation.

[0134] Here, passenger can refer to the person sitting in the driver's seat of the vehicle. For example, the passenger can be the driver or user of the corresponding vehicle.

[0135] Alternatively, the user input may be used to provide information for initiating a user authentication process for a mobile vehicle. For example, the user input may be a predetermined amount of pressure applied to a start button or brake pedal. Alternatively, the user input may be a predetermined amount of pressure applied to a steering handle. Alternatively, the user input may be a predetermined amount of pressure applied to a passenger seat.

[0136] The user input may be different for each user. In other words, the size of the user input may be different for each user, and each user may use a different button or device.

[0137] The mobile tool user authentication apparatus 700 of the embodiment of the present application can display a preset image list to the passenger on a predetermined area of the mobile tool based on the received user input. In addition, the display 720 can perform the operation.

[0138] It is known that a brain wave signal (e.g., EEG) can show a specific signal pattern with respect to a specific visual stimulus. In other words, an EEG having different characteristics can be output from a user according to an image characteristic provided to the user.

[0139] Here, the image characteristic can include chroma, color depth, brightness, contrast, sharpness, mellowness, and content.

[0140] In addition, with respect to the image characteristic, a channel characteristic for measuring the brain wave signal can be considered. In other words, the brain wave signal can indicate different aspects in each measurement area of the head surface, the occipital lobe corresponding to the back of the head has a primary visual cortex, and thus can mainly process visual information, the parietal lobe located near the top of the head has a somatosensory cortex, and thus can process motion / sensation information. In addition, the frontal lobe can process information related to memory, advanced thinking, and / or emotion, and the temporal lobe can process information related to hearing and smell. Accordingly, a visual image capable of stimulating the occipital lobe, an image having a relationship capable of stimulating the frontal lobe with the user, or an advanced image can be considered.

[0141] For example, the brain wave signal generated according to the image characteristic can be a signal activated in the occipital lobe processing visual information, which is respectively responsive to chroma, color depth, brightness, contrast, sharpness, mellowness, and content information. Here, the content information can indicate other image characteristic information other than chroma, color depth, brightness, contrast, sharpness, and mellowness.

[0142] For example, EEG characteristics obtained when a user gazes at a black and white image and gazes at a color image can be different.

[0143] For another example, when EEG characteristics are obtained when a user gazes at an image having many low frequency characteristics and gazes at an image having many high frequency characteristics, the EEG characteristics can be different.

[0144] For another example, when EEG characteristics are obtained when a user gazes at a letter image and gazes at a human image, the EEG characteristics can be different.

[0145] For another example, when EEG characteristics are obtained when a user gazes at a letter image and gazes at a figure image, the EEG characteristics can be different.

[0146] For another example, when EEG characteristics are obtained when a user gazes at a number image and gazes at a Korean character / Roman alphabet image, the EEG characteristics can be different.

[0147] In addition, when a user gazes at an image having a predetermined correlation with the user and at an image having no correlation with the user, EEG characteristics can be different.

[0148] Here, the image having a predetermined correlation with the user can mean an image having a personal relationship with the user. For example, it can include a photo of the user, a photo of the user's family, a photo of the user's pet, a photo of the user's precious property, and a photo capable of making the user recall a specific experience. The image having a predetermined correlation with the user can have been stored by each user.

[0149] In addition, the image list can be composed of images whose characteristics are related to the function of each brain region. For example, images such as an occipital lobe, a parietal lobe, a frontal lobe, a prefrontal lobe, and a temporal lobe having a correlation with the function of each region can be included. For example, an electroencephalogram signal generated according to the characteristics of an image having a predetermined correlation with the passenger can be a signal activated in the frontal lobe (or a part of the frontal lobe) in response to an image having a predetermined correlation with the passenger, the frontal lobe processing information associated with memory, high-level thinking, and emotion. In other words, the image having a predetermined correlation with the passenger can be an image that activates an electroencephalogram signal in the frontal lobe or some region of the frontal lobe of the passenger.

[0150] Figure 8A And Figure 8B may be a graph showing EEG characteristics of a user gazing at images having different characteristics according to one embodiment of the present application. For example, Figure 8A And Figure 8B may be a graph showing EEG characteristics obtained from a user gazing at a black-and-white image and a color image, respectively. Alternatively, Figure 8A And Figure 8B may be a graph showing EEG characteristics obtained from a user gazing at an image having many low-frequency characteristics and gazing at an image having many high-frequency characteristics. Alternatively, Figure 8A And Figure 8B may be a graph showing EEG characteristics obtained from a user gazing at an image having a predetermined correlation with the user and gazing at an image having no correlation with the user.

[0151] In addition, the image list can include at least one or more images having different image characteristics.

[0152] For example, the image list can include a black-and-white image, a color image, a letter image, and a photo of the user.

[0153] For another example, the image list can include a black-and-white image, an image having many low-frequency characteristics, an image having many high-frequency characteristics, a number image, and a photo capable of making the user recall a specific experience.

[0154] The number of images constituting the image list can be set by user input or can be preset in the mobile means.

[0155] In addition, the mobile means user authentication apparatus 700 of the embodiment of the present application can display at least one or more images constituting the image list in a sequential order on a predetermined area of the mobile means.

[0156] Here, the at least one or more images can be at least some of all images constituting the image list.

[0157] Here, the sequential order can indicate the order of the images constituting the image list. For example, when the image list is constituted by images {A1,..., An} (n is an integer greater than 1), the sequential order can indicate the order from A1 to An.

[0158] The image order can be set by user input or can be preset in the mobile means.

[0159] Alternatively, the image stored by user input can be displayed on the predetermined area in preference to other images.

[0160] Alternatively, the image having a predetermined relevance to the user can be displayed on the predetermined area in preference to other images.

[0161] Here, each image can be displayed on the predetermined area for a predetermined time. The predetermined time can be different. Alternatively, the predetermined time of each image can be different based on the image characteristics.

[0162] In addition, each image displayed on the predetermined area can have a predetermined time interval. For example, there can be a time interval of tens of milliseconds to several seconds between image A1 and image A2. The time interval of each image can be different. Alternatively, the time interval of each image can be different based on the image characteristics. No image can be displayed for the time interval. Alternatively, an image having a predetermined color can be displayed during the time interval. The predetermined time interval between images enables the user to remove afterimages from the previous image.

[0163] Here, the predetermined area can be a predetermined area within a display capable of projection in the mobile means. The predetermined area can be a predetermined area on the front windshield, side window, rear windshield, and a projection display different from the windshield. The predetermined area can be determined based on at least one of the position of the passenger when the mobile means is traveling and the position at which the passenger gazes.

[0164] In addition, the predetermined area can be a predetermined area within a navigation display. Alternatively, it can be a predetermined area on a separate head up display (HUD).

[0165] In response to the displayed image list, the mobile means user authentication apparatus 700 of the embodiment of the present application can collect the brainwave signal of the passenger for a predetermined time. Also, the sensor 730 can perform the operation.

[0166] Here, the collection of the brainwave signal for a predetermined time can mean the collection of the brainwave signal of the passenger who gazes at the image displayed on the predetermined area of the mobile means for a predetermined time. The image can be the image constituting the image list.

[0167] Also, the collection of the brainwave signal for a predetermined time can mean the collection of the brainwave signal of the passenger for each image displayed on the predetermined area of the mobile means and included in the image list.

[0168] For example, when the image list is constituted by images {A1,..., An} (n is an integer greater than 1), and the images A1 to An are sequentially displayed in the predetermined area of the mobile means, the brainwave signal characteristic (or EEG feature) information of the passenger who gazes at the images A1 to An can be sequentially collected.

[0169] Here, the collected brainwave signal can mean the brainwave signal in at least one of a time domain, a frequency domain, and a spatial domain. Here, the spatial domain can mean a brainwave signal measurement channel.

[0170] The mobile means user authentication apparatus 700 of the embodiment of the present application can perform authentication of the passenger by analyzing the collected brainwave signal. Also, the controller 740 can perform the operation.

[0171] Here, the analysis can include determining whether the brainwave signal characteristic information collected for a predetermined time is similar to the pre-stored brainwave signal characteristic information of each passenger.

[0172] Here, the brainwave signal characteristic information of each passenger can be a result of prior learning depending on the passenger. For example, the prior learning can be performed for the passenger brainwave signal characteristic corresponding to each image of the image list. Also, the brainwave signal characteristic information of each passenger can be updated in real time.

[0173] For example, based on the similarity determination, the mobile means user authentication apparatus 700 of the embodiment of the present application can determine whether the collected brainwave signal characteristic information is similar to the pre-stored brainwave signal characteristic information of each passenger. Here, the similarity determination can apply various methods of determining similarity, such as a technique of extracting feature points between input images to determine similarity and other conventional techniques for image recognition or classification.

[0174] In addition, when determining the similarity, the similarity between the brainwave signal characteristics can be compared with a predetermined threshold value. The predetermined threshold value can vary according to the image corresponding to the brainwave signal characteristics. Here, the similarity between the brainwave signal characteristics can be expressed by a probability or a numerical value.

[0175] For example, when the image corresponding to the brainwave signal characteristics has a predetermined relevance to the user, the threshold value can have a greater value than when the image has no predetermined relevance to the user. For example, when the image corresponding to the brainwave signal characteristics has no predetermined relevance to the user and the threshold value is 0.6, the threshold value can be 0.8 for the image corresponding to the brainwave signal characteristics and having a predetermined relevance to the user. In other words, when the similarity for the brainwave signal characteristics having an image relevant to the user is more strictly determined, a device that can be more adaptively operated for each user can be provided.

[0176] For another example, when the image corresponding to the brainwave signal characteristics is a human image, the threshold value can be greater than when the image is a character image.

[0177] In other words, the mobile means user authentication device 700 of the embodiment of the present application can perform authentication of the passenger by determining whether similar. In addition, the controller 740 can perform the operation.

[0178] Further, determining the similarity can include a process of finally determining whether the brainwave signal characteristics are similar by combining the determination results of the similarity between the brainwave signal characteristics of each image included in the image list.

[0179] In addition, when the brainwave signal characteristics are finally determined to be similar, it can be considered that the passenger is authenticated.

[0180] When the image list is composed of images {A1,..., An} (n is an integer greater than 1), and the images A1 to An are sequentially displayed in a predetermined area of the mobile means, whether the brainwave signal characteristics are similar can be determined as follows.

[0181] For example, when the determination result as to whether the brainwave signal characteristics of each image are similar can be represented by 0 and 1, and when the number of determination results as to whether the brainwave signal characteristics of each of the images A1 to An are similar is equal to or greater than k (k is an integer greater than 1), the brainwave signal characteristics can be finally determined to be similar. In other words, when the number of determination results as to whether the brainwave signal characteristics of each image are similar is equal to or greater than k, the brainwave signal characteristics can be finally determined to be similar. Here, when the determination result as to whether they are similar is 0, the similarity between the brainwave signal characteristics is lower than a predetermined threshold. When the determination result as to whether they are similar is 1, the similarity between the brainwave signal characteristics is equal to or greater than the predetermined threshold.

[0182] For another example, when the determination result as to whether the brainwave signal characteristics of each image are similar can be represented by 0 and 1, and the result expressed by the weighted sum of the images A1 to An is equal to or greater than m (m is an integer greater than 1), the brainwave signal characteristics can be finally determined to be similar.

[0183] For yet another example, when the similarity is determined by using the top k (k is an integer greater than 1) images ranked in descending order of priority in a given image, authentication of the passenger can be performed. For example, when it is determined that the brainwave signal characteristics of p (1≤p≤k, p is an integer) or more of the top k images are similar, the brainwave signal characteristics can be finally determined to be similar.

[0184] For another example, when the brainwave signal characteristics of a predetermined image among the images A1 to An are determined to be dissimilar, the brainwave signal characteristics can be finally determined to be dissimilar. For example, when the brainwave signal characteristics corresponding to an image having a predetermined correlation with the user are determined to be dissimilar to the pre-stored brainwave signal characteristics, the brainwave signal characteristics can be finally determined to be dissimilar.

[0185] Meanwhile, the condition for performing authentication by determining the similarity can be set by user input.

[0186] In addition, the mobile tool user authentication device 700 can provide the passenger with the authentication result.

[0187] For example, when the authentication fails, a voice prompt informing of the authentication failure can be provided to the passenger.

[0188] For another example, when the authentication succeeds, a voice prompt informing of the authentication success can be provided to the passenger, or a voice prompt indicating a subsequent step (e.g., button input for starting the mobile tool) after the authentication can be provided to the passenger.

[0189] For yet another example, a pre-set voice prompt can be provided to the passenger according to the case of authentication failure or success.

[0190] For yet another example, when the authentication fails, the passenger can confirm whether to perform a re-authentication process.

[0191] Figure 9 is a flowchart showing a method of operating a user authentication device according to an embodiment of the present application.

[0192] At step S901, a predetermined user input can be received from a passenger of a mobile means.

[0193] Here, the passenger can be a person sitting on a driver seat of the mobile means. Alternatively, the passenger can be a person sitting on a first seat of the mobile means, which can mean a seat on which a passenger who can play a leading role in controlling the mobile means sits.

[0194] Here, the user input can be a pressure having a predetermined size or more applied to at least one of a start button, a brake pedal, a seat, and a steering wheel of the mobile means.

[0195] At step S902, a preset image list can be displayed to the passenger on a predetermined area of the mobile means based on the received user input.

[0196] For example, at least one or more images constituting the image list can be displayed on the predetermined area of the mobile means in a sequential order. Here, the at least one or more images can be at least some of all images constituting the image list.

[0197] For another example, each of the at least one or more images can be displayed on the predetermined area for a predetermined time. Here, the predetermined time can be different for each of the at least one or more images.

[0198] Here, the predetermined area can include at least one of a display, a head-up display (HUD), and a navigation display capable of being projected in the mobile means.

[0199] Here, the image list can be composed of at least one or more images having different image characteristics. Also, the image list can include images having a predetermined relevance to the passenger. Here, the images having a predetermined relevance to the passenger can be images that activate the brain wave signals in the frontal lobe or certain regions of the frontal lobe of the passenger. Also, the image list can be composed of images whose characteristics are related to the processing functions of each brain region. For example, images such as the occipital lobe, the parietal lobe, the frontal lobe, the prefrontal lobe, and the temporal lobe having characteristics related to the processing functions of each region can be included. For example, the brain wave signals generated according to the image characteristics having a predetermined relevance to the passenger can be signals activated in the frontal lobe (or a portion of the frontal lobe) in response to images having a predetermined relevance to the passenger, which processes information associated with memory, high-level thinking, and emotion.

[0200] The image characteristics can include at least one of chroma, color depth, brightness, contrast, sharpness, softness, and content information. Here, the content information can indicate other image characteristic information other than chroma, color depth, brightness, contrast, sharpness, and softness. For example, the brain wave signals generated according to the image characteristics can be signals activated when the occipital lobe processes visual information, which are respectively in response to chroma, color depth, brightness, contrast, sharpness, softness, and content information.

[0201] In addition, the number of images constituting the image list can be set by user input, or can be preset in the mobile tool.

[0202] In step S903, in response to the displayed image list, the brain wave signals of the passenger can be collected for a predetermined time.

[0203] For example, the brain wave signals of the passenger gazing at the image displayed on the predetermined region of the mobile tool can be collected for a predetermined time. Here, the collected brain wave signals can be brain wave signals in at least one of the time domain, the frequency domain, and the spatial domain.

[0204] Step S904 can include performing authentication of the passenger by analyzing the collected brain wave signals.

[0205] Here, the analysis can include determining whether the brain wave signal characteristic information collected for a predetermined time is similar to the pre-stored brain wave signal characteristic information of each passenger.

[0206] The brain wave signal characteristic information of each passenger can be pre-learned brain wave signal characteristic information of each passenger corresponding to each image of the image list.

[0207] In addition, the authentication result of the passenger can be provided to the passenger.

[0208] Effects obtained in the embodiments of the present application are not limited to the above-mentioned effects, and other effects not mentioned above can be clearly understood by those skilled in the art from the following description.

[0209] Although the exemplary methods of the present application are described as a series of operations for clarity, the present application is not limited to the order or sequence of the above-mentioned operations. The operations can be performed simultaneously or sequentially in different orders. Additional operations can be added and / or existing operations can be eliminated or replaced in order to implement the methods of the embodiments of the present application.

[0210] The various embodiments of the present application are presented not to describe all combinations, but to describe only representative combinations. The steps or elements in the various embodiments can be used alone or in combination.

[0211] In addition, the various embodiments of the present application can be implemented in the form of hardware, firmware, software, or a combination thereof. When the embodiments of the present application are implemented as hardware components, they can be, for example, application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), general purpose processors, controllers, microcontrollers, microprocessors, etc.

[0212] The scope of the present application includes software or machine-executable instructions (e.g., operating system (OS), application, firmware, program) that enable various forms of methods to be executed on a device or computer, and a non-transitory computer-readable medium that stores these software or machine-executable instructions so that the software or instructions can be executed on the device or computer.

[0213] The description of the embodiments of the present application is merely exemplary in nature and, thus, variations that do not depart from the essence of the application are intended to be within the scope of the present application. Such variations are not to be regarded as a departure from the spirit and scope of the present application.

Claims

1. A mobile tool user authentication device using brain wave signals, the device comprising: a receiver configured to receive predetermined user input from a passenger of the mobility vehicle; a display configured to display a preset list of images to a passenger on a predetermined area of ​​the mobile vehicle based on the received user input; a sensor configured to collect a passenger's brainwave signal within a predetermined time in response to the displayed image list; as well as A controller configured to analyze collected brainwave signals by determining whether characteristic information of the brainwave signals collected within a predetermined time is similar to pre-stored characteristic information of the passenger's brainwave signals, thereby performing authentication on the passenger, wherein a predetermined threshold value for images having a predetermined correlation with the passenger is greater than a predetermined threshold value for images having no predetermined correlation with the passenger.

2. The mobile tool user authentication device using brain wave signals according to claim 1, wherein: The predetermined user input is a pressure having a predetermined magnitude or more applied to at least one of a start button, a brake pedal, a seat, and a steering wheel of the moving tool.

3. The mobile tool user authentication device using brain wave signals according to claim 1, wherein: The preset image list includes at least one or more images with different image characteristics; The different image characteristics include at least one of chroma, color depth, brightness, contrast, clarity, softness and content information.

4. The mobile tool user authentication device using brain wave signals according to claim 1, wherein: The preset image list includes images that have a predetermined relevance to the passenger.

5. The mobile tool user authentication device using brain wave signals according to claim 4, wherein: The image having a predetermined relevance to the passenger is an image that activates brain wave signals in the passenger's frontal lobe or certain areas of the frontal lobe.

6. The mobile tool user authentication device using brain wave signals according to claim 1, wherein: The display is configured to display at least one or more images of a preset image list in a sequential order on a predetermined area of ​​the moving tool.

7. The mobile tool user authentication device using brain wave signals according to claim 6, wherein: The predetermined area includes an area of ​​at least one of a display capable of projection, a head-up display, and a navigation display in the mobile vehicle.

8. The mobile tool user authentication device using brain wave signals according to claim 1, wherein: The sensor is configured to collect brain wave signals of a passenger who is gazing at an image displayed on a predetermined area of ​​the mobile vehicle within a predetermined time.

9. The mobile tool user authentication device using brain wave signals according to claim 1, wherein: The controller is further configured to provide the passenger with an authentication result of the passenger.

10. A method for authenticating a mobile tool user using brain wave signals, the method comprising: receiving predetermined user input from a passenger of the mobile vehicle; Based on the received user input, displaying a preset list of images to the passenger at a predetermined area of ​​the mobile vehicle; In response to the displayed image list, collecting a passenger's brain wave signal within a predetermined time; Passenger authentication is performed by analyzing collected brainwave signals, wherein the analysis of the collected brainwave signals includes determining whether characteristic information of the collected brainwave signals is similar to pre-stored characteristic information of passenger brainwave signals, wherein a predetermined threshold value for an image having a predetermined correlation with the passenger is greater than a predetermined threshold value for an image having no predetermined correlation with the passenger.

11. The mobile tool user authentication method using brain wave signals according to claim 10, wherein: The predetermined user input is a pressure having a predetermined magnitude or more applied to at least one of a start button, a brake pedal, a seat, and a steering wheel of the moving tool.

12. The mobile tool user authentication method using brain wave signals according to claim 10, wherein: The preset image list includes at least one or more images with different image characteristics; The different image characteristics include at least one of chroma, color depth, brightness, contrast, clarity, softness and content information.

13. The mobile tool user authentication method using brain wave signals according to claim 10, wherein: The preset image list includes images that have a predetermined relevance to the passenger.

14. The mobile tool user authentication method using brain wave signals according to claim 13, wherein: The image having a predetermined relevance to the passenger is an image that activates brain wave signals in the passenger's frontal lobe or certain areas of the frontal lobe.

15. The mobile tool user authentication method using brain wave signals according to claim 10, wherein: The displaying on the predetermined area of ​​the moving tool includes displaying at least one or more images of a preset image list in a sequential order on the predetermined area of ​​the moving tool.

16. The mobile tool user authentication method using brain wave signals according to claim 15, wherein: The predetermined area includes an area of ​​at least one of a display capable of projection, a head-up display, and a navigation display in the mobile vehicle.

17. The mobile tool user authentication method using brain wave signals according to claim 10, wherein: Collecting the passenger's brain wave signal within the predetermined time includes collecting the passenger's brain wave signal while the passenger is gazing at an image displayed on a predetermined area of ​​the mobile vehicle within the predetermined time.

18. The mobile tool user authentication method using brain wave signals according to claim 10, further comprising: The passenger authentication result is provided to the passenger.

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