Apparatus and method for selecting goods using brainwave signals

Abstract

An apparatus and method for selecting a product using a brain wave signal are disclosed. The method for selecting a product includes receiving information about at least one product from a service point by a receiver, displaying the received information on a predetermined area of a moving body by a display, collecting a brain wave signal of at least one passenger in the moving body for a predetermined time in response to the displayed information by a sensor, and determining a selection of the passenger by analyzing the collected brain wave signal by a controller.

Description

[0001] Cross-reference to related applications

[0002] This application claims priority and benefit to Korean Patent Application No. 10-2019-0129730, filed on October 18, 2019, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This disclosure relates to methods and devices for controlling mobile bodies. Background Technology

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

[0005] As a means of transportation, a vehicle (or mobile object) is a very important means and tool for living in the modern world. Furthermore, a vehicle itself can be considered a special thing that is meaningful to someone.

[0006] With technological advancements, the functions offered by vehicles have also evolved. For example, in recent years, vehicles have gone beyond simply transporting passengers to their destinations; they now also meet the demand for faster and safer arrival. Furthermore, new features have been added to vehicle systems to satisfy passengers' aesthetic tastes and enhance their comfort. Additionally, existing features such as steering wheels, transmissions, and accelerator / decelerator systems have been 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 provides an apparatus and method for selecting goods based on passengers' brainwave signals.

[0009] This disclosure provides an apparatus and method for selecting goods in a moving body based on passenger steady-state visual evoked potentials (SSVEPs).

[0010] The technical objectives of this disclosure are not limited to those described above, and other unmentioned technical objectives will be clearly understood by those skilled in the art through the following description.

[0011] In one form of this disclosure, a device for selecting goods using brainwave signals may include: a receiver for receiving information about at least one item from a service point; a display for displaying the received information on a predetermined area of ​​a mobile body; a sensor for collecting brainwave signals of at least one passenger in the mobile body within a predetermined time period in response to the received information; and a controller for determining the passenger's selection by analyzing the collected brainwave signals.

[0012] Brainwave signals may include steady-state visual evoked potentials (SSVEP).

[0013] Service points can be locations that provide drive-through (DT) services within a predetermined range of the mobile vehicle.

[0014] When the transmission and reception between the mobile unit and the service point is based on a local area network (LAN), the predetermined range can be the range within which the LAN can transmit and receive.

[0015] Goods can be at least one of the following: items provided by the service point, services provided by the service point, and information about the service point.

[0016] Information about a product can be at least one of the following: product image, product type, product price, product quantity, product name, new menu, event, and discount, provided by the service point.

[0017] The predetermined area can be a predetermined area of ​​the display that can be projected onto the moving body.

[0018] The predetermined area can be the windshield, side windshields, rear windshield, or a predetermined area on a projection display other than the windshield.

[0019] At least one of the form and size of the reserved area can be determined based on information about the goods.

[0020] The predetermined area can be determined based on at least one of the passenger's position and the passenger's gaze position while the vehicle is in motion.

[0021] The display can show received information about the goods on a predetermined area of ​​the moving body based on a predetermined frequency.

[0022] The predetermined frequency may include at least one of the following: output frequency, frequency output mode, and output duration.

[0023] The output frequency can be set differently based on the information about the product displayed in the designated area.

[0024] The analysis can compare the magnitude of SSVEP collected at a predetermined time with a predetermined threshold.

[0025] The threshold can be determined based on the output frequency of SSVEP.

[0026] The controller can control the movement or send reservation information to the service point based on the passenger's confirmed selection.

[0027] The controller can further process the checks to determine whether the passenger's chosen option meets the passenger's intent.

[0028] Additionally, according to this disclosure, a method for selecting goods using brainwave signals can be provided. This method may include: receiving information about at least one item from a service point by a receiver; displaying the received information on a predetermined area of ​​a mobile body by a display; collecting brainwave signals of at least one passenger in the mobile body by a sensor within a predetermined time period in response to the displayed information; and determining the passenger's selection by a controller analyzing the collected brainwave signals.

[0029] Brainwave signals may include steady-state visual evoked potentials (SSVEP).

[0030] Service points can be locations that provide drive-through (DT) services within a predetermined range of the mobile vehicle.

[0031] When the transmission and reception between the mobile unit and the service point is based on a local area network (LAN), the predetermined range can be the range within which the LAN can transmit and receive.

[0032] Goods can be at least one of the following: items provided by the service point, services provided by the service point, and information about the service point.

[0033] Information about a product can be at least one of the following: product image, product type, product price, product quantity, product name, new menu, event, and discount, provided by the service point.

[0034] The predetermined area can be a predetermined area of ​​the display that can be projected onto the moving body.

[0035] The predetermined area can be the windshield, side windshields, rear windshield, or a predetermined area on a projection display other than the windshield.

[0036] At least one of the form and size of the reserved area can be determined based on information about the goods.

[0037] The predetermined area can be determined based on at least one of the passenger's position or the passenger's gaze position while the vehicle is in motion.

[0038] The display step may include displaying information about the receipt of goods on a predetermined area of ​​the mobile body based on a predetermined frequency.

[0039] The predetermined frequency may include at least one of the following: output frequency, frequency output mode, and output duration.

[0040] The output frequency can be set differently based on the information about the product displayed in the designated area.

[0041] The analysis can compare the magnitude of SSVEP collected within a predetermined time period with a predetermined threshold.

[0042] The threshold can be determined based on the output frequency of SSVEP.

[0043] The method may further include steps of controlling the vehicle or sending reservation information to a service point based on the passenger's determined selection.

[0044] The method may further include a step of checking whether the passenger's chosen option satisfies the passenger's intent.

[0045] The features briefly outlined above are merely exemplary aspects of the detailed description of this disclosure below and do not limit the scope of this disclosure.

[0046] Other application areas will become apparent from the description provided herein. It should be understood that the descriptions and specific examples are for illustrative purposes only and are not intended to limit the scope of this disclosure. Attached Figure Description

[0047] To make this disclosure readily understandable, various forms of this disclosure will now be described by way of example, with reference to the accompanying drawings, in which:

[0048] Figure 1 This is a diagram illustrating a general waveform of one form of ERN according to the present disclosure;

[0049] Figure 2 This is a diagram showing the general waveforms of ERN and Pe according to one form of this disclosure;

[0050] Figure 3 This is a diagram illustrating the deflection characteristics of Pe according to another form of this disclosure;

[0051] Figure 4A and Figure 4B This is a diagram showing the measurement areas of ERP and Pe in one form of this disclosure;

[0052] Figure 5 This is a diagram showing the general waveforms of ERN and CRN according to one form of this disclosure;

[0053] Figure 6 This is a diagram illustrating an EEG measurement channel corresponding to a cortical region according to one form of the present disclosure;

[0054] Figure 7 This is a block diagram illustrating the configuration of a device for selecting goods based on passengers' brainwave signals, according to one form of this disclosure;

[0055] Figure 8 This is a diagram used to explain the range of transmission and reception between a mobile body and a service point according to one form of this disclosure;

[0056] Figure 9It is a diagram used to illustrate a form of displaying information about goods on a predetermined area of ​​a moving body;

[0057] Figure 10 This is a flowchart illustrating a method of operating a goods selection device according to one form of the present disclosure;

[0058] Figure 11 This is a flowchart illustrating a method of operating a goods selection device according to another form of this disclosure; and

[0059] Figure 12 This is a flowchart illustrating a method of operating a goods selection device according to yet another form of this disclosure.

[0060] The accompanying drawings described herein are for illustrative purposes only and are not intended to limit the scope of this disclosure in any way. Detailed Implementation

[0061] The following description is exemplary in nature only and is not intended to limit this disclosure, its application, or its uses. It should be understood that in all the drawings, corresponding reference numerals denote the same or corresponding parts and features.

[0062] Exemplary forms of this disclosure will be described in detail so that those skilled in the art, in conjunction with the accompanying drawings, will readily understand and implement the devices and methods provided by this disclosure. However, this disclosure may be embodied in various forms, and its scope should not be construed as limited to the exemplary forms.

[0063] In describing the form of this disclosure, well-known functions or structures will not be described in detail where they may obscure the spirit of this disclosure.

[0064] In this disclosure, it will be understood that when an element is referred to as being "connected to," "coupled to," or "combined to" another element, it may be directly connected to, coupled to, or combined to another element, or there may be intermediate elements between them. It will be further understood that, when used in this disclosure, the terms "comprising," "including," "having," etc., specify the presence of the said feature, integer, step, operation, element, component, and / or combination thereof, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or combinations thereof.

[0065] It will be understood that although the terms “first,” “second,” etc., may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used only to distinguish one element from another, and not to indicate the order or priority between elements. For example, without departing from the teachings of this disclosure, the first element discussed below may be referred to as the second element. Similarly, the second element may also be referred to as the first element.

[0066] In this disclosure, the term "distinguished element" clearly describes the characteristics of various elements and does not imply that the elements are physically separate from each other. That is, multiple distinguished elements can be combined into a single hardware unit or a single software unit, and conversely, an element can be implemented by multiple hardware units or software units. Therefore, although not specifically stated, the integrated form of various elements or the separate form of a single element can fall within the scope of this disclosure. Similarly, terms such as "unit" or "module" should be understood as a unit that performs at least one function or operation, and this unit can be implemented in hardware (e.g., a processor), software, or a combination of hardware and software.

[0067] In this disclosure, all constituent elements described in various forms should not be construed as essential elements, but some constituent elements may be optional. Therefore, a configuration of various subsets of constituent elements in some form also falls within the scope of this disclosure. Furthermore, a configuration by adding one or more elements to various elements also falls within the scope of this disclosure.

[0068] Brainwave signals (or brain signals, brain waves) are biological signals that directly or indirectly reflect a person's conscious or unconscious state, representing the electrical activity of neurons that make up the brain. Brainwave signals can be measured in every region of the human scalp, with wavelengths primarily below 30 Hz and potential differences of a few microvolts. Various waveforms may appear depending on brain activity and state. Research is using brainwave signals for interface control based on a person's intentions. Brainwave signals can be obtained using EEG (electroencephalography), which utilizes electrical signals generated by brain activity; MEG (magnetoencephalography), which utilizes magnetic signals accompanying electrical signals; and fMRI (functional magnetic resonance imaging) or fNIRS (near-infrared spectroscopy), which utilizes changes in blood oxygen saturation. While fMRI and fNIRS are useful techniques for measuring brain activity, fMRI typically has low temporal resolution, and fNIRS has low spatial resolution. Due to these limitations, EEG signals are widely used because of their excellent portability and temporal resolution.

[0069] Brainwave signals vary spatially and temporally according to brain activity. Because brainwave signals are often difficult to analyze and their waveforms are not easily visualized, various processing methods have been proposed.

[0070] For example, brainwave signals can be classified based on the number of oscillations (frequency) (power spectrum classification). This classification treats the measured brainwave signal as a linear sum of simple signals at each specific frequency, decomposing the signal into individual frequency components and indicating their corresponding amplitudes. Brainwave signals at each frequency can be obtained using preprocessing typically used for noise cancellation, Fourier transform to the frequency domain, and bandpass filters (BPF).

[0071] More specifically, based on frequency bands, brain waves can be classified into delta (δ), theta (θ), alpha (α), beta (β), and gamma (γ) waves. Delta waves are brain waves with frequencies below 3.5 Hz and amplitudes ranging from 20 μV to 200 μV, primarily observed in normal deep sleep or in newborns. Furthermore, delta waves may increase as our understanding of the physical world decreases. Theta waves are typically brain waves with frequencies between 3.5 Hz and 7 Hz, primarily observed in emotionally stable states or during sleep.

[0072] In addition, theta waves are mainly generated in the parietal and occipital cortices and may appear during periods of calm concentration to recall memories or meditate. Alpha waves are typically brain waves with frequencies of 8 Hz to 12 Hz, primarily occurring in relaxed and comfortable states. Alpha waves are also typically generated in the occipital cortex during rest and may diminish during sleep. Beta waves are typically brain waves with frequencies of 13 Hz to 30 Hz, primarily occurring in tolerable states of tension or when attention is being drawn to them to some extent. Furthermore, beta waves are mainly generated in the frontal cortex and are associated with arousal or concentrated brain activity, pathological phenomena, and the effects of medication. Beta waves can appear in broad areas throughout the brain. Specifically, beta waves can be classified into SMR waves (13 Hz to 15 Hz), intermediate beta waves (15 Hz to 18 Hz), and high beta waves (above 20 Hz). Because beta waves appear to be stronger under stress similar to anxiety and tension, they are sometimes called stress waves. Gamma waves are brain waves that typically range from 30 Hz to 50 Hz and primarily occur during states of intense excitement or during higher cognitive information processing. Furthermore, gamma waves may appear during conscious wakefulness and REM sleep, and may also overlap with beta waves.

[0073] Each brainwave signal in a frequency band is associated with a specific cognitive function. For example, delta waves are associated with sleep, theta waves with working memory, and alpha waves with attention or inhibition. Therefore, the characteristics of brainwave signals in each frequency band selectively reveal specific cognitive functions. Furthermore, brainwave signals in each frequency band may exhibit some different aspects in each measurement portion of the head surface. The cerebral cortex can be divided into the frontal cortex, parietal cortex, temporal cortex, and occipital cortex. These regions may have some different functions. For example, the occipital cortex, corresponding to the back of the head, is the primary visual cortex and therefore primarily processes visual information. The parietal cortex, located near the top of the head, is the somatosensory cortex and therefore processes motor / sensory information. Additionally, the frontal cortex processes information related to memory and thinking, and the temporal cortex processes information related to hearing and smell.

[0074] Additionally, for another example, brainwave signals can be analyzed using ERPs (Event-Related Potentials). ERPs are electrical changes in the brain that are associated with external stimuli or internal psychological processes. ERPs refer to signals of electrical activity in the brain caused by stimuli that include specific information (e.g., images, speech, sounds, commands, etc.) some time after the presentation of the stimulus.

[0075] To analyze ERPs, signal-to-noise separation is required. Averaging methods can be primarily used. Specifically, by averaging brain waves measured based on the stimulus onset time, irrelevant brain waves can be removed, and only relevant potentials—that is, brain activity typically associated with stimulus processing—can be selected.

[0076] Because of its high temporal resolution, ERPs are closely related to the study of cognitive function. ERPs are electrical phenomena caused by external stimuli or related to internal states. Based on the type of stimulus, ERPs can be classified into auditory-related potentials, visual-related potentials, somatosensory-related potentials, and olfactory-related potentials. Based on the nature of the stimulus, ERPs can be classified into exogenous ERPs and endogenous ERPs. Exogenous ERPs have waveforms determined by external stimuli, are related to automatic processing, and mainly appear in the initial stages of stimulation. For example, exogenous ERPs are brainstem potentials. On the other hand, endogenous ERPs are determined by internal cognitive processes or psychological processes or states unrelated to stimuli and are related to "controlled processes." For example, endogenous ERPs are P300, N400, P600, CNV (negative correlation variation), etc.

[0077] The names of ERP peaks typically include polarity and the latent period (reaction time difference), and each signal peak has its own definition and meaning. For example, a positive potential is P, a negative potential is N, and P300 represents the positive peak measured approximately 300 milliseconds after the stimulus begins. Additionally, peaks are designated 1, 2, 3, or a, b, c, etc., according to their order of appearance. For example, P3 represents the third positive potential in the waveform after the stimulus begins.

[0078] The following text will describe various ERP systems.

[0079] For example, N100 is associated with responses to unpredictable stimuli.

[0080] MMN (Mismatch Negative Waves) can be generated not only by focused stimuli but also by unfocused stimuli. MMN can be used as an indicator of whether sensory memory (audio-visual memory) is functioning before initial attention is aroused. P300, which will be described below, occurs during attention and judgment, while MMN is analyzed as a process that occurs in the brain before attention.

[0081] For example, N200 (or N2) is primarily generated based on visual and auditory stimuli and is associated with short-term or long-term memory, as well as P300 as described below, which is a type of memory after attention.

[0082] For example, P300 (or P3) primarily reflects attention to stimuli, stimulus cognition, memory retrieval, and uncertainty reduction, and is related to perceptual decisions in distinguishing external stimuli. Because P300 generation is related to cognitive function, it is generated regardless of the type of stimulus encountered. For example, P300 can be generated from auditory, visual, and somatic stimuli. P300 is widely used in brain-computer interface research.

[0083] For example, N400 is related to language processing and is triggered by sentences or auditory stimuli containing semantic errors. Additionally, N400 is related to memory processes and can reflect the process of retrieving or searching for information from long-term memory.

[0084] For example, as an indicator of the reconstruction or recovery process, P600 relates to the process of processing stimuli more accurately based on information stored in long-term memory.

[0085] For example, CNV refers to a potential that appears in a later stage, lasting 200ms to 300ms or even a few seconds. It is also known as a slow potential (SP) and is associated with anticipation, preparation, mental priming, association, attention, and motor activity.

[0086] For example, ERN (Error-Related Negative Potential) or Ne (Error-Related Negative Potential) is an event-related potential (ERP) generated by a mistake or error. This can occur when a subject makes a mistake in a sensorimotor task or similar task. More specifically, ERN is generated when a subject identifies a mistake or error, and its negative peak appears primarily in the frontal and central regions for approximately 50 to 150 ms. In particular, its negative peak may appear in situations where a mistake related to motor response is possible, and it may also be used to indicate a negative self-judgment.

[0087] The main features of ERN will be described in more detail below.

[0088] Figure 1 This is a diagram showing a general waveform of an ERN according to one form of the present disclosure.

[0089] Reference Figure 1 Negative potential values ​​are plotted above the horizontal axis, and positive potential values ​​are plotted below the horizontal axis. Furthermore, it can be confirmed that an ERP with a negative peak is generated within a predetermined time range after the start of the response to any motion. Here, the response can represent a case of error or negligence (erroneous response). The predetermined time range can be approximately 50 ms to 150 ms, or approximately 0 ms to 100 ms. Meanwhile, in the case of a correct response, the negative peak of the generated ERP is smaller than that of the ERN.

[0090] The ERN, acting as an initial negative ERP, is time-locked until a response error occurs. Furthermore, the ERN is known to reflect enhanced activity of the dopaminergic system associated with behavioral monitoring. The ERN includes the frontal striatum loop containing the lateral cingulate cortex. Simultaneously, dopamine is associated with the brain's reward system, which typically forms specific behaviors and motivates individuals, providing feelings of pleasure and enhancement. When a behavior is repeatedly rewarded, it is learned as a habit. Additionally, emotional learning releases more dopamine, and the release of dopamine leads to the attempt of new behaviors. Therefore, reward-driven learning is called reinforcement learning.

[0091] Additionally, an ERN may be generated within 0 to 100 ms after the start of an erroneous response that may be caused by prefrontal cortex guidance during interfering tasks (e.g., Go-noGo tasks, Stroop tasks, Flanker tasks, and Simon tasks).

[0092] Furthermore, it is known that ERN, together with CRN as described below, reflects a conventional behavior monitoring system that can distinguish between correct and incorrect behavior.

[0093] Furthermore, the fact that the ERN reaches its maximum amplitude at the frontal cortex electrode reflects that the intracranial generator is located in the lateral cingulate cortex or the dorsal anterior cingulate cortex (dACC) region.

[0094] In addition, ERN can display changes in the magnitude of negative emotional states.

[0095] Furthermore, even when behavioral monitoring is based on external evaluation feedback processing that differs from the expression of internal motivation, ERN can be reported and can be classified as FRN as described below.

[0096] Furthermore, ERNs can be generated not only when a mistake or error is recognized, but also before a mistake or error is recognized.

[0097] Furthermore, ERNs can be generated not only as a response to one's own faults or mistakes, but also as a response to the faults or mistakes of others.

[0098] Furthermore, ERNs can be generated not only as a response to mistakes or errors, but also as a response to anxiety or stress related to a pre-determined task or object.

[0099] Furthermore, since a larger ERN peak is obtained, it can be assumed that a larger ERN peak reflects a more serious mistake or error.

[0100] Meanwhile, for another example, Pe (positive error potential), as an event-related potential (ERP) generated after ERN, is a positive ERP that is generated primarily at the frontal cortex electrodes approximately 150 ms to 300 ms after a mistake or error. Pe is referred to as the response of becoming aware of the mistake or error and paying more attention. In other words, Pe is associated with an indicator of the conscious error information processing process following error detection. ERN and Pe are collectively referred to as ERPs associated with error detection.

[0101] The main features of Pe will be described in more detail below.

[0102] Figure 2 This is a diagram showing the general waveforms of ERN and Pe according to another form of this disclosure.

[0103] Reference Figure 2 A negative potential value is depicted above the positive potential value. Furthermore, it can be confirmed that an ERP with a negative peak value, i.e., an ERN, is generated within a first predetermined time range after the start of the response to any motion. Here, the response can represent a situation where a mistake or error has occurred (error response). The first predetermined time range can be approximately 50 ms to 150 ms. Alternatively, the first predetermined time range can be approximately 0 ms to 200 ms.

[0104] Additionally, it can be confirmed that an ERP with a positive peak value, Pe, was generated within a second predetermined time range after the ERN occurred. This second predetermined time range can be approximately 150ms to 300ms after the error occurred. Alternatively, the second predetermined time range can mean approximately 200ms to 400ms.

[0105] Figure 3 This is a diagram illustrating the deflection characteristics of one form of Pe according to the present disclosure.

[0106] Reference Figure 3 Like P3, Pe has a wide deflection characteristic, and the plexus generator includes not only the regions of the posterior cingulate cortex and insular cortex, but also the region of the anterior cingulate cortex.

[0107] Additionally, Pe can reflect the emotional evaluation of errors and attention to stimuli, such as P300. Furthermore, ERN represents the conflict between correct and incorrect responses, and Pe is considered the response of recognizing the error and paying more attention. In other words, ERN can be generated during stimulus detection, and Pe can be generated based on attention during stimulus processing. When ERN and / or Pe each have relatively large values, these values ​​are known to be associated with adaptive behavior aimed at responding more slowly and accurately after an error.

[0108] Figure 4A and Figure 4B This is a diagram showing the measurement area of ​​ERN and Pe according to one form of this disclosure.

[0109] ERN and Pe are collectively referred to as ERPs related to error detection. Regarding the measurement areas for ERN and Pe, the maximum negative and maximum positive values ​​are typically measured in the central region. However, there may be some differences depending on the measurement conditions. For example, Figure 4A This is the primary region for measuring ERN, and the maximum negative value of ERN can usually be measured in the midline frontal lobe or central region (i.e., FCZ). Additionally, Figure 4B It is the main area for measuring Pe, and compared to ERN, large positive Pe values ​​can usually be measured in the back midline region.

[0110] Meanwhile, for another example, FRN (Feedback-Related Negative Potential) is an event-related potential (ERP) associated with error detection based on external evaluation feedback. ERN and / or Pe detect errors based on internal monitoring processes. However, in the case of FRN, when FRN is obtained based on external evaluation feedback, it may resemble the process of ERN.

[0111] In addition, FRN and ERN can share many electrophysiological properties. For example, FRN has a negative peak at the frontal cortical electrode about 250 ms to 300 ms after the onset of negative feedback, and may be generated in the dorsal anterior cingulate cortex (dACC) area like ERN.

[0112] Furthermore, similar to ERN, FRN can reflect the reinforcement learning activity of the dopaminergic system. Additionally, FRN typically has a larger negative value than positive feedback and may have a larger value for unpredictable situations than for predictable outcomes.

[0113] For example, CRN (Correctly Related Negative Potential) is the ERP generated by a correct test and is a negative value less than ERN. Similar to ERN, CRN may be generated during the initial waiting period (e.g., 0ms to 100ms). Figure 5 This is a diagram illustrating the general waveforms of one form of ERN and CRN of this disclosure.

[0114] For example, Pc (correct positive potential) is an event-related potential that occurs after the CRN. It is an event-related potential that occurs approximately 150 ms to 300 ms after the correct response occurs. The relationship between CRN and Pc can be similar to the relationship between ERN and Pe.

[0115] Furthermore, ERPs can be categorized into stimulus-locked ERPs and response-locked ERPs. These distinctions can be based on criteria such as the cause of the ERP and the response time. For example, an ERP triggered from the moment a word or image is presented to the user can be considered a stimulus-locked ERP. Conversely, an ERP triggered from the moment the user speaks or presses a button can be considered a response-locked ERP. Therefore, based on these criteria, stimulus-locked ERPs are typically categorized as N100, N200, P2, P3, etc., while response-locked ERPs are typically categorized as ERN, Pe, CRN, Pc, FRN, etc.

[0116] Furthermore, brainwaves can be categorized based on the motivation they exhibit. Brainwaves can be classified into spontaneous brainwaves (spontaneous potentials) that manifest according to the user's will, and evoked brainwaves (evoked potentials) that manifest naturally according to external stimuli unrelated to the user's will. Spontaneous brainwaves may be displayed when the user moves or imagines movement, while evoked brainwaves may be manifested through stimuli such as visual, auditory, olfactory, and tactile stimuli.

[0117] Simultaneously, brainwave signals can be measured using the International 10-20 system. The International 10-20 system determines the measurement points for brainwave signals based on the relationship between electrode locations and cortical regions.

[0118] Figure 6This is a diagram illustrating an EEG measurement channel corresponding to a region of the cerebral cortex according to one form of the present disclosure.

[0119] Reference Figure 6 The brain regions (prefrontal 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 EEG measurement channels. For each channel, data can be obtained and used to analyze each cortical region.

[0120] Figure 7 This is a block diagram illustrating the configuration of a device for selecting goods based on a passenger's brainwave signals, according to one form of this disclosure.

[0121] Drive-through (DT) is a service that allows customers to order, pay for, and pick up goods within their own mobile device, without having to park it. DT is praised as an efficient and convenient service because customers don't need to park their mobile devices or wait in line. Recently, DT services have become increasingly common. For example, mobile device passengers can easily use the convenient DT services offered by fast food restaurants and coffee shops in urban areas or along highways in their daily lives. "Mobile device" can encompass vehicles, mobile / transportation equipment, etc.

[0122] Meanwhile, current DT services typically require passengers to arrive at the location or venue offering the DT service (hereinafter referred to as a "DT point") and then order goods. This disclosure provides a goods selection device and method that allows passengers to select and order goods (hereinafter referred to as "DT goods") offered by the DT point before arriving at the DT point.

[0123] Here, a DT point can be a location within a predetermined range of the mobile vehicle that provides DT services.

[0124] Furthermore, the DT points of this disclosure can include not only locations providing DT services, but also outlets capable of providing DT goods (though not DT services) to mobile devices and receiving information about goods selected and ordered within the mobile device. For example, a DT point of this disclosure can be an outlet where a customer in a mobile device can select and order goods offered by the DT point, but must park / pull the mobile device at a separate location to pick up the ordered goods. In other words, the DT points of this disclosure can include drive-in outlets without a drive-through route.

[0125] The product selection device disclosed herein can display DT products offered by DT points on a predetermined area of ​​a mobile body, and select the displayed product by using the passenger's brainwave signals. Furthermore, the product selection device of this disclosure can provide information about the selected DT product to the DT point.

[0126] Here, steady-state visual evoked potentials (SSVEPs) can be used as the brainwave signals of passengers. SSVEPs are signals of a natural response to visual stimuli at a specific frequency. Typically, when given visual stimuli in the frequency range of 3.5 Hz to 75 Hz, the human brain (primarily the occipital lobe) is electrically activated at the same frequency as the visual stimulus. SSVEP-based research utilizes the physiological mechanisms of brainwave responses. When a user gazes at light flashing at a specific frequency, this technique utilizes the principle that a synchronized signal with the flashing light frequency is detected from brainwaves originating from the cerebral cortex. In other words, this technique utilizes the principle that gazing at a specific visual stimulus at its flashing frequency will increase brainwave power at the same frequency and / or integer octaves. Furthermore, SSVEPs have a good signal-to-noise ratio.

[0127] Reference Figure 7 The product selection 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 are shown for illustrating this form, and the components included in the product selection device 700 are not limited to the examples described above. For instance, more than one component unit may be implemented in one component unit, and operations performed in one component unit may be divided into and performed in more than two component units. Additionally, some component units may be omitted, or additional component units may be added.

[0128] The product selection device 700 disclosed herein can receive information about at least one product from a service point. Additionally, the receiver 710 can perform this operation.

[0129] Here, a service point can be represented as a DT point.

[0130] For example, a service point could be a location that provides DT (Delivery and Delivery) services within a predetermined range of the mobile device. Alternatively, when the mobile device is within a predetermined range of the service point, it can receive information about the goods. The quantity and type of this service may vary depending on the location of the mobile device.

[0131] Figure 8 This is a diagram used to explain the range of transmission and reception between a mobile body and a service point according to one form of this disclosure.

[0132] Reference Figure 8In the case of the first service point 800, information about the goods provided by the first service point 800 can be sent from the first service point 800 to the mobile body 820 within the first range 802. On the other hand, in the case of the second service point 810, there is no mobile body within the second range 812 from the second service point 810 to which information about the goods can be sent. Optionally, the mobile body 820 can receive information about the goods from the first service point 800 and the second service point 810 located within the third range 822 from the mobile body 820.

[0133] In another example, a service point can represent a location entered by the user or a preset location of the mobile entity. Alternatively, a service point can represent a location automatically detected based on predetermined conditions in the mobile entity's navigation system. Service points can be set differently for each user in the mobile entity. For example, each user can set a service point based on his / her own preferences.

[0134] In another example, service points can be grouped based on the characteristics of the location where the service is provided. For instance, service points can be grouped into categories such as fast food restaurants, coffee shops, bakeries, convenience stores, banks, and ticket offices based on the characteristics or type of goods and services offered. At least one grouped list can be selected based on the choices of passengers on the mobile device. Alternatively, the list of groups can be presented on the mobile device's display, and in response to this presentation, at least one grouped list can be selected based on the choices of passengers on the mobile device.

[0135] Here, the predetermined range can represent a distance of several kilometers or tens of kilometers in radius from the mobile unit and / or service point. Alternatively, the predetermined range can be set based on a communication network. For example, when transmission and reception between the mobile unit and service point are performed based on a local area network (LAN), the predetermined range can be the area within which the LAN can achieve transmission and reception. In this case, a beacon can be used for the LAN.

[0136] Here, "goods" can refer to DT goods. For example, "goods" might refer to each item such as hamburgers, coffee, and bread. In other words, it might refer to the goods that a passenger on the moving vehicle wants to purchase.

[0137] Additionally, the product may include services provided by service points.

[0138] Additionally, the product may include the name, image, and logo of the service point.

[0139] Here, information about the product can refer to information about the DT product.

[0140] For example, information about DT products can include information about the product's image, type, price, quantity, and name.

[0141] In another example, information about DT products can include reservation information provided by the service point. For example, it could include information about new menus, events, and discounts offered by the service point.

[0142] For example, information about DT (Dedicated Travel) products can be tailored based on passenger preferences. In this regard, the information about DT products can be based on pre-learning of passenger preferences. Furthermore, the information about DT products can be updated in real time.

[0143] The product selection device 700 disclosed herein can display information about at least one product received from a service point on a predetermined area of ​​a mobile body. Additionally, a display 720 can perform this operation.

[0144] Here, the predetermined area of ​​the mobile body can refer to the area used to display information about the goods in the mobile body.

[0145] For example, the predetermined area can be a predetermined area in the display that can be projected onto the moving body. For example, the predetermined area can be a predetermined area on the windshield, side windshields, rear windshield, and a projection display different from the windshield. Alternatively, it can be a predetermined area on a separate head-up display (HUD).

[0146] In another example, the predetermined area can be variable rather than fixed. For instance, it could be an area input by the user or a preset area of ​​the moving vehicle. Alternatively, it can be adaptively set by taking into account the location of each passenger. In other words, the predetermined area can be set based on the location of each passenger. Or, the predetermined area can be set by considering whether the passenger's line of sight would typically remain in that area while the moving vehicle is in motion.

[0147] In another example, the predetermined area can have different shapes and / or sizes depending on the information about the product. For example, when the information about the product is an image, the predetermined area can be circular. Alternatively, when the information about the product is the name of the product, the predetermined area can be rectangular. Alternatively, the area displaying information about the first product can be larger than the area displaying information about the second product.

[0148] Meanwhile, the product selection device 700 of this disclosure can display information about the receipt of products on a predetermined area of ​​a mobile body based on a predetermined frequency.

[0149] Here, the predetermined frequency may include information about the output frequency, frequency output mode, output duration, etc. For example, the product selection device 700 of this disclosure may display information about the receipt of a product on a predetermined area of ​​a moving body at a predetermined output frequency based on a predetermined frequency output mode and / or a predetermined output duration.

[0150] For example, based on the dynamic range of SSVEP, the output frequency can be any frequency in the range of 3.5Hz to 75Hz.

[0151] In another example, the output frequency can be set differently for each piece of information on the products displayed in a predetermined area. For instance, information about the first product can be set to a first frequency, while information about the second product can be set to a second frequency, which is different from the first frequency.

[0152] In another example, the output frequency can be set based on a preset frequency difference. For instance, information about the first product can be displayed in the first area using the first frequency as the output frequency, while information about the second product can be displayed in the second area by adding the frequency difference to the first frequency used as the output frequency.

[0153] In another example, the output frequency can be set based on the entire frequency band. For instance, when the entire frequency band is 70Hz (i.e., the range from 5Hz to 75Hz), the output frequency for information about the product to be displayed can be set to maximize the difference between candidate frequencies within the entire 70Hz band. This process allows for maximum utilization of the entire available frequency band.

[0154] For another example, the output frequency can be determined based on the quantity and type of information about the goods displayed in the predetermined area.

[0155] Here, for example, the frequency output pattern can represent a pattern of a set frequency displayed on a predetermined area of ​​the moving body, and the frequency output pattern can include a grid, diagonal lines, multiple lines radiating from a center point, or multiple lines passing through a single point.

[0156] For example, information about a product can be displayed in a designated area using a grid pattern.

[0157] In another example, information about a product can be displayed on a designated area using a pattern of diagonal lines and intersecting lines.

[0158] Here, the frequency output duration can represent the duration for which information about a product is displayed on a predetermined area according to a set output frequency. For example, the frequency output duration can be set based on the type of information about the product, the output frequency, and the output mode.

[0159] Furthermore, when multiple pieces of information about a product are displayed on a predetermined area of ​​a moving object, this information can be of different types. For example, the information displayed in the first area could be an image of the first product, while the information displayed in the second area could be the price of the second product.

[0160] The above information regarding the product, display area, output frequency (Hz), output mode, and / or output duration can be stored in the form of a list as shown in Table 1 below.

[0161] Table 1

[0162]

[0163] Referring to Table 1, the product selection device 700 of this disclosure can display product information on a predetermined area of ​​a moving body based on the type of information about the product, display area, output frequency, output mode, and output duration.

[0164] At the same time, when there are more than two passengers, the list shown in Table 1 may differ for each passenger.

[0165] Figure 9 It is a diagram illustrating a form of displaying information about goods on a predetermined area of ​​a moving object.

[0166] refer to Figure 9 When displaying three pieces of information about products, information about the first product 912 can be displayed in the first area 910, information about the second product 922 can be displayed in the second area 920, and information about the third product 932 can be displayed in the third area 930. Figure 9 The first region 910 to the third region 930 are shown in a circular form, but are not limited to this, and may include regions of a certain form to display predetermined information.

[0167] In addition, Figure 9 In this context, the entire area 900, including the first area 910 to the third area 930, is shown as a two-dimensional plane, but is not limited thereto, and may include curves or a three-dimensional form. In other words, the distance (or depth) of each display area from the passenger may not be equal, and at least one area may be at different distances.

[0168] In response to information displayed on a predetermined area of ​​the mobile body, the product selection device 700 of this disclosure can collect brainwave signals from at least one passenger of the mobile body within a predetermined time period. Additionally, sensor 730 can perform this operation.

[0169] Here, brainwave signals can represent SSVEP.

[0170] Additionally, in this paper, collecting brainwave signals within a predetermined time period may include: measuring the brainwave signals of at least one passenger in the moving body and detecting SSVEP from the measured brainwave signals.

[0171] For example, SSVEPs can be collected from passengers who are staring at a predetermined area of ​​a moving vehicle and displaying information about the goods.

[0172] For example, the passenger's SSVEP can be collected only at frequencies corresponding to the output frequencies displayed in a predetermined area. In other words, when the output frequencies are 5Hz and 17Hz, frequencies within the corresponding ranges of 5Hz and 17Hz (e.g., + / - 2Hz) can be collected. Here, frequency can represent the power of a frequency band or SSVEP within a predetermined range.

[0173] In another example, a predetermined frequency in which power increases in a frequency band can be detected, and visual stimuli corresponding to (or matching) that predetermined frequency can be collected.

[0174] For yet another example, a passenger's SSVEP can be collected by determining the similarity to an output frequency displayed in a predetermined area. In other words, a passenger's SSVEP can be collected based on characteristics of the output frequency, such as amplitude, shape, and duration. Here, the amplitude of the frequency represents, for example, the amplitude of the power obtained by transforming the measured signal into a frequency band in the frequency domain using a Fourier transform. For example, a passenger's SSVEP can be collected by considering the amplitude of the power (or the amplitude of the SSVEP) and / or the duration of a specific frequency band (e.g., approximately 5 Hz, 17 Hz, etc.).

[0175] Here, the scheduled time can represent sufficient time for collecting SSVEP. For example, as shown in Table 1, the scheduled time can be set based on the type of information about the product, display area, output frequency, output mode, and output duration.

[0176] For example, the predetermined time can be proportional to the amplitude variation of the smallest of multiple output frequencies.

[0177] In another example, the scheduled time can be proportional to the output duration.

[0178] The product selection device 700 disclosed herein can determine a passenger's selection by analyzing collected brainwave signals. Additionally, the controller 740 can perform this operation.

[0179] Here, the analysis may include a process of comparing the magnitude of SSVEP collected within a predetermined time period with a predetermined threshold.

[0180] Meanwhile, the threshold can be a preset value or a user-input value. Furthermore, the threshold can have different amplitudes for each passenger from whom SSVEP is collected. For example, it can be a value reflecting the characteristics of each passenger's brainwave signal. To reflect the analysis results of the brainwave signal characteristics, a predetermined learning process can be performed in advance for the SSVEP characteristics displayed in the passenger's brainwave signal.

[0181] Furthermore, the analysis may include a process for determining whether the amplitude of SSVEP within a predetermined time interval is equal to or greater than a predetermined threshold (i.e., exceeds a predetermined threshold range). For example, in the case of a first frequency, the amplitude of SSVEP may be compared with a first threshold to determine whether the amplitude of SSVEP within a first time range is equal to or greater than the first threshold. In the case of a second frequency, the amplitude of SSVEP may be compared with a second threshold to determine whether the amplitude of SSVEP within a second time range is equal to or greater than the second threshold.

[0182] Furthermore, the threshold can be varied according to the output frequency of SSVEP. For example, at a first frequency, the amplitude of SSVEP can be compared with a first threshold, while at a second frequency, the amplitude of SSVEP can be compared with a second threshold.

[0183] Furthermore, this analysis can derive the passenger preference order by determining the similarity between the collected passenger SSVEP and each of at least one output frequency. In other words, the preference order of passenger choices can be determined by considering the characteristics of the output frequencies (e.g., amplitude, shape, and duration).

[0184] For example, information about goods that corresponds to a frequency equal to or greater than (or exceeding) a predetermined threshold can be used to determine a passenger's choice.

[0185] For example, information about goods that corresponds to the frequency that has the greatest difference from a predetermined threshold can be used to determine a passenger's choice.

[0186] For example, information about a product that corresponds to the frequency most similar to the output frequency in terms of characteristics such as amplitude, shape, and duration can be used to determine a passenger's choice.

[0187] Simultaneously, prior to analysis, the following processing can be performed: identifying the occurrence of SSVEPs by using the timing of characteristic occurrences of brainwave signals and / or by using patterns of brainwave signals. Optionally, prior to analysis, the following processing can be performed: identifying the occurrence of SSVEPs based on the timing of characteristic occurrences of brainwave signals and / or through prior learning of brainwave signals. Additionally, the analysis may include a process for extracting SSVEPs.

[0188] Additionally, the ERP used for analysis can be a statistical value of SSVEP collected over a predetermined period. For example, the statistical value can represent the mean, weighted average, maximum, or minimum value.

[0189] As described above, when using SSVEP, it is possible to determine which piece of information the passenger selected from the information displayed on the mobile device. Furthermore, based on this determination, the mobile device can be controlled for a specific purpose, or a particular piece of information can be sent to a service point.

[0190] For example, the route can be changed to a service point corresponding to the selected passenger's choice. Information about the changed route can then be provided to the passenger.

[0191] For example, an additional process can be performed to check whether the determined passenger's choice satisfies the passenger's intention. In other words, when the passenger's choice is determined, words such as "yes" and "no" can be displayed at different output frequencies on a predetermined area, and the corresponding brainwave signals of the passenger can be analyzed. Therefore, the checking process using SSVEP can be performed a second time.

[0192] For example, an order signal for a product corresponding to a passenger's chosen item can be sent to the appropriate service point. In other words, when a passenger's choice is determined to be "coffee," an order signal for coffee can be sent to the corresponding coffee shop.

[0193] Figure 10 This is a flowchart illustrating a method of operating a commodity selection device according to one form of the present disclosure.

[0194] In step S1001, information about at least one item can be received from the service point.

[0195] In this document, "information about a product" can refer to information about a DT (Demand-Based) product. Information about a DT product may include details about its image, type, price, quantity, and name. Information about a DT product may also include reservation information provided by the service point. For example, information about a DT product may include information about new menus, events, and discounts offered by the service point.

[0196] In step S1002, the received information can be displayed on a predetermined area of ​​the moving body.

[0197] Here, the predetermined area can be a predetermined area on the display that can be projected onto the moving body. For example, the predetermined area can be a predetermined area on the windshield, side windshields, rear windshield, and a projection display different from the windshield. Alternatively, it can be a predetermined area on a separate head-up display (HUD).

[0198] Here, the received information can represent information about the product. For example, the received information can be displayed on a predetermined area of ​​the mobile device based on user input, a preset order of the mobile device, and / or the order in which the information about the product is received.

[0199] Additionally, the received information about the product can be displayed on a predetermined area of ​​the mobile device based on a predetermined frequency. Here, the predetermined frequency may include information about the output frequency, frequency output mode, output duration, etc.

[0200] In step S1003, in response to the displayed information, brainwave signals of at least one passenger in the mobile body can be collected within a predetermined time.

[0201] Here, brainwave signals can represent SSVEP. The SSVEP of a passenger who is gazing at information about a product displayed on a predetermined area of ​​a moving body can be collected. Here, the passenger's SSVEP can be collected only at frequencies corresponding to the output frequency displayed on the predetermined area.

[0202] In step S1004, the passenger's choice can be determined by analyzing the collected brainwave signals.

[0203] Here, the analysis may include a process of comparing the amplitude of SSVEPs collected within a predetermined time period with a predetermined threshold. Additionally, the analysis may include a process of determining whether the amplitude of SSVEPs within a predetermined time interval is equal to or greater than a predetermined threshold (i.e., exceeds a predetermined threshold range). Furthermore, the analysis can derive a passenger selection priority by determining the similarity between the collected passenger SSVEPs and each of at least one output frequency.

[0204] Figure 11 This is a flowchart illustrating a method of operating a commodity selection device according to another form of this disclosure.

[0205] When there are multiple service points around a mobile vehicle, it is desirable to select a service point before ordering the desired goods from the mobile vehicle.

[0206] In step S1101, information about a service point can be received from each of the multiple service points.

[0207] Here, information about service points may include the name, image, and logo of the relevant service point, and may also be included in information about the goods.

[0208] In step S1102, the received information can be displayed on a predetermined area of ​​the mobile body. In step S1103, in response to the displayed information, brainwave signals of at least one passenger within the mobile body can be collected within a predetermined time. In step S1104, the passenger's selection can be determined by analyzing the collected brainwave signals. Since steps S1102 to S1104 can respectively correspond to... Figure 10 Steps S1002 to S1004, therefore the detailed process is the same as Figure 10 The same as described in [the text].

[0209] In step S1105, a product request signal can be sent to the service point corresponding to the selected passenger's choice.

[0210] In step S1104, the determined passenger's selection may correspond to a predetermined service point. Therefore, in order to receive information about the goods provided by the corresponding service point, a goods request signal can be sent to the service point.

[0211] In step S1106, information about at least one item can be received from the service point.

[0212] The subsequent process will be with Figure 10 The same as described in [the text].

[0213] Figure 12 This is a flowchart illustrating a method of operating a goods selection device according to yet another form of this disclosure.

[0214] Passenger preferences, purchase history, and other information can be used to provide customized product information.

[0215] In step S1201, the mobile body identifier (ID) information can be sent to the service point.

[0216] Here, the mobile body ID information can represent information that identifies the mobile body or the passenger of that mobile body. Alternatively, the mobile body ID information can represent identification information of the mobile body assigned by the corresponding service point or the passenger of the mobile body.

[0217] For example, the mobile body ID information may include information such as passenger order details, order frequency, most recently ordered items, and items ordered based on specific weather / season. Alternatively, information such as order details can be retrieved from the corresponding service point using the mobile body ID information.

[0218] In step S1202, information about the goods can be received from the service point. In this context, the information about the goods may be information processed based on the sent mobile ID information. In other words, it may not be the information that the service point typically sends to any mobile device, but rather information reflecting the passenger's preferences, recent purchase history, etc.

[0219] The subsequent process will be with Figure 10 The same as described in [the text].

[0220] According to this disclosure, there are devices and methods for selecting goods based on passengers' brainwave signals.

[0221] Additionally, according to this disclosure, devices and methods for selecting goods based on passenger SSVEP can be provided.

[0222] The effects obtained in this disclosure are not limited to those described above, and other effects not mentioned above will be clearly understood by those skilled in the art based on the following description.

[0223] Although the exemplary methods of this disclosure are described as a series of operational steps for clarity, this disclosure is not limited to the order or sequence of the described operational steps. The operational steps may be performed simultaneously or sequentially in different orders. To implement the methods of this disclosure, additional operational steps may be added and / or existing operational steps may be eliminated or replaced.

[0224] The various forms disclosed herein are not presented to describe all available combinations, but rather to describe only representative combinations. The various forms of steps or elements may be used individually or in combination.

[0225] Furthermore, this disclosure can be implemented in various forms, including hardware, firmware, software, or combinations thereof. When this disclosure is implemented in a hardware component, 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.

[0226] The scope of this disclosure includes: software or machine-executable instructions (e.g., operating system (OS), application, firmware, program) that enable various forms of methods 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.

[0227] The description in this disclosure is exemplary in nature only, and therefore, modifications that do not depart from the essence of this disclosure are intended to be within its scope. Such modifications should not be considered as departing from the spirit and scope of this disclosure.

Claims

1. A device for selecting goods using brainwave signals, the device comprising: The receiver is configured to receive information about at least one item from a service point; A display is configured to display received information on a predetermined area of ​​a mobile body, wherein the display is configured to display received information about the at least one item on the predetermined area of ​​the mobile body based on a predetermined frequency including information about the output frequency, wherein the output frequency is set differently based on the received information about the at least one item displayed on the predetermined area; Sensors, configured to collect brainwave signals of at least one passenger in the moving body within a predetermined time period in response to displayed information; and The controller is configured to analyze the collected brainwave signals and determine the selection of the at least one passenger.

2. The device according to claim 1, in, The collected brainwave signals include steady-state visual evoked potentials.

3. The device according to claim 1, in, The service point is located within a predetermined range of the mobile vehicle and provides drive-through service.

4. The device according to claim 1, in, The information received regarding the at least one item includes at least one of the following: an image of the item provided by the service point, the type of the item, the price of the item, the quantity of the item, the name of the item, a new menu item, an event, and a discount.

5. The device according to claim 1, in, The predetermined area is a predetermined area in the display that can be projected onto the moving body.

6. The device according to claim 1, in, Based on the received information about the at least one item, determine at least one of the shape and size of the predetermined area.

7. The device according to claim 1, in, The predetermined area is determined based on at least one of the positions of the at least one passenger and the gaze positions of the at least one passenger while the mobile body is in motion.

8. The device according to claim 1, in, The controller is configured to control the mobile body or send predetermined information to the service point based on the selection of the determined at least one passenger.

9. The device according to claim 1, in, The controller is also configured to determine whether the selection of the at least one passenger satisfies the intention of the at least one passenger.

10. A method for selecting goods using brainwave signals, the method comprising: The receiver receives information about at least one item from the service point; The received information is displayed on a predetermined area of ​​the mobile body by a display, wherein displaying the received information on the predetermined area includes: displaying the received information about the at least one item on the predetermined area of ​​the mobile body based on a predetermined frequency including information about the output frequency, wherein the output frequency is set differently based on the received information about the at least one item displayed on the predetermined area; In response to the displayed information, sensors collect brainwave signals from at least one passenger in the moving body within a predetermined time period; and The controller determines the selection of the at least one passenger by analyzing the collected brainwave signals.

11. The method according to claim 10, in, The collected brainwave signals include steady-state visual evoked potentials.

12. The method according to claim 10, in, The service point is located within a predetermined range of the mobile vehicle and provides drive-through service.

13. The method according to claim 10, in, The information received regarding the at least one item includes at least one of the following: an image of the item provided by the service point, the type of the item, the price of the item, the quantity of the item, the name of the item, a new menu item, an event, and a discount.

14. The method according to claim 10, in, The predetermined area is a predetermined area in the display that can be projected onto the moving body.

15. The method according to claim 10, in, Based on the received information about the at least one item, determine at least one of the shape and size of the predetermined area.

16. The method according to claim 10, in, The predetermined area is determined based on at least one of the positions of the at least one passenger and the gaze positions of the at least one passenger while the mobile body is in motion.

17. The method of claim 10, further comprising: Based on the selection of the determined at least one passenger, the mobile body is controlled or the predetermined information is sent to the service point.

18. The method of claim 10, further comprising: Determine whether the selection of the at least one passenger satisfies the intention of the at least one passenger.

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