Apparatus and method for providing customized mobile body driving path using brainwave signal

Abstract

An apparatus and method for providing a customized mobile body driving path using a brain wave signal, the apparatus including a sensor configured to collect a brain wave signal of a driver of a mobile body in a predetermined passage area, an analyzer configured to determine information about a planned path to be provided by analyzing the brain wave signal collected in the predetermined passage area, and a controller configured to control an operation of the mobile body based on the information to be provided.

Description

[0001] Cross-reference to related applications

[0002] This application claims priority to Korean Patent Application No. 10-2019-0158707, filed on December 3, 2019, which is 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] Vehicles (or mobile bodies), as a means of transportation, are a very important means and tool for living in the modern world. Furthermore, a mobile body itself can be considered a special thing that is meaningful to someone.

[0006] With technological advancements, the functions offered by mobile devices have also evolved. For example, in recent years, mobile devices have not only transported passengers to their destinations but have also met the demand for faster and safer arrival. Furthermore, new features have been added to mobile device systems to satisfy passengers' aesthetic tastes and comfort needs. Additionally, existing features such as steering wheels, transmissions, and accelerator / decelerator mechanisms 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 relates to methods and apparatus for controlling mobile bodies. Specific embodiments relate to methods and apparatus for controlling mobile bodies based on error monitoring.

[0009] Embodiments of the present invention provide an apparatus and method for providing customized driving paths for mobile bodies based on the driver's brainwave signals.

[0010] Another embodiment of the invention provides a customized mobile driving path providing device and method, which adjusts the amount of information about the planned path to be provided by using brainwave signals obtained from a predetermined area of ​​the driver.

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

[0012] According to embodiments of the present invention, a customized mobile vehicle driving path providing device using brainwave signals can be provided, the device comprising: a sensor configured to collect brainwave signals of a driver of the mobile vehicle in a predetermined channel area; an analyzer configured to determine information about a planned path to be provided by analyzing the brainwave signals collected in the predetermined channel area; and a controller configured to control the operation of the mobile vehicle based on the information to be provided.

[0013] The predetermined channel region may include at least one of a first region and a second region, wherein the first region includes the hippocampus and the second region includes the retrosplenial cortex.

[0014] Brainwave signals can be brainwave signals in the time series plane.

[0015] The analysis may include comparing the amplitude of brainwave signals collected in a predetermined channel region with a predetermined threshold.

[0016] The amplitude of a brainwave signal can be the power spectrum of a brainwave signal at a specific frequency.

[0017] The analysis may include comparing the magnitude of oxygen saturation collected in a predetermined channel region with a predetermined threshold.

[0018] The analyzer can classify the type of planned path by analyzing brainwave signals collected in a predetermined channel area, and determine the information about the planned path to be provided based on the classified type.

[0019] The analyzer can classify the type of planned path by analyzing the amplitude of brainwave signals collected in a predetermined channel region, and adjust the amount of information about the planned path to be provided based on the classified type.

[0020] The analyzer can classify the type of planned route by analyzing the magnitude of oxygen saturation collected in a predetermined channel area, and adjust the amount of information about the planned route to be provided based on the classified type.

[0021] When the predetermined channel area includes a first area and a first signal and a second signal greater than the first signal are collected in the first area, the amount of information to be provided in response to the second signal may be greater than the amount of information to be provided in response to the first signal.

[0022] When the predetermined channel area includes a second area and a first signal and a second signal greater than the first signal are collected in the second area, the amount of information to be provided in response to the second signal may be less than the amount of information to be provided in response to the first signal.

[0023] When the predetermined channel area includes a first area and a second area, the analyzer can ultimately determine the information about the planned path to be provided by combining the analysis results of the EEG signals collected in the first area with the analysis results of the EEG signals collected in the second area.

[0024] The controller can adjust the amount of information provided by predetermined devices included in the moving body, and the predetermined devices may include at least one of steering devices, pedal devices, transmissions, video systems, audio systems, navigation systems, and other moving body manipulation devices.

[0025] According to embodiments of the present invention, a method for providing a customized driving path for a mobile vehicle using brainwave signals can be provided, the method comprising: collecting brainwave signals of a driver of the mobile vehicle in a predetermined channel area; determining information about a planned path to be provided by analyzing the brainwave signals collected from the predetermined channel area; and controlling the operation of the mobile vehicle based on the information to be provided.

[0026] The predetermined channel region may include at least one of a first region and a second region, the first region including the hippocampus and the second region including the posterior cortex.

[0027] Brainwave signals can be brainwave signals in the time series plane.

[0028] The analysis may include comparing the amplitude of brainwave signals collected in a predetermined channel region with a predetermined threshold.

[0029] The amplitude of a brainwave signal can be the power spectrum of a brainwave signal at a specific frequency.

[0030] The analysis may include comparing the magnitude of oxygen saturation collected in a predetermined channel region with a predetermined threshold.

[0031] Determining the information to be provided about the planned pathway may include classifying the type of planned pathway by analyzing brainwave signals collected in a predetermined channel region, and determining the information to be provided about the planned pathway based on the classified type.

[0032] Determining the information to be provided about the planned pathway may include classifying the type of planned pathway by analyzing the amplitude of brainwave signals collected in a predetermined channel region, and adjusting the amount of information to be provided about the planned pathway based on the classified type.

[0033] Determining the information to be provided about the planned route may include classifying the type of planned route by analyzing the magnitude of oxygen saturation collected in the predetermined channel area, and adjusting the amount of information to be provided about the planned route based on the classified type.

[0034] When the predetermined channel area includes a first area and a first signal and a second signal greater than the first signal are collected in the first area, the amount of information to be provided in response to the second signal may be greater than the amount of information to be provided in response to the first signal.

[0035] When the predetermined channel area includes a second area and a first signal and a second signal greater than the first signal are collected in the second area, the amount of information to be provided in response to the second signal may be less than the amount of information to be provided in response to the first signal.

[0036] When the predetermined channel area includes a first area and a second area, determining the information about the planned path to be provided may include combining the analysis results of brainwave signals collected in the first area with the analysis results of brainwave signals collected in the second area to ultimately determine the information about the planned path to be provided.

[0037] The operation of the mobile body can be controlled by adjusting the amount of information provided by predetermined devices included in the mobile body, and the predetermined devices may include at least one of steering devices, pedal devices, transmissions, video systems, audio systems, navigation systems, and other mobile body manipulation devices.

[0038] The features briefly summarized above regarding embodiments of this disclosure are merely exemplary aspects of the following detailed description of embodiments of this disclosure and do not limit the scope of this disclosure.

[0039] According to embodiments of the present invention, devices and methods can be provided to offer customized driving paths for mobile bodies based on the driver's brainwave signals.

[0040] Additionally, according to embodiments of the present invention, a customized mobile driving path providing device and method can be provided, which adjusts the amount of information about the planned path to be provided by using brainwave signals obtained from a predetermined area of ​​the driver.

[0041] The effects obtained in the embodiments of 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. Attached Figure Description

[0042] To better understand this disclosure, various embodiments of the disclosure, given by way of example, will now be described with reference to the accompanying drawings, in which:

[0043] Figure 1 This is a diagram illustrating the general waveform of an ERN according to an embodiment of the present disclosure;

[0044] Figure 2 This is a diagram illustrating the general waveforms of ERN and Pe according to an embodiment of the present disclosure;

[0045] Figure 3 This is a diagram illustrating the deflection characteristics of Pe according to another embodiment of the present disclosure;

[0046] Figure 4A and Figure 4B This is a diagram showing the measurement areas of ERP and Pe, respectively, according to an embodiment of this disclosure;

[0047] Figure 5 This is a diagram illustrating the general waveforms of ERN and CRN according to an embodiment of the present disclosure;

[0048] Figure 6 This is a diagram illustrating EEG measurement channels corresponding to cerebral cortex regions according to an embodiment of the present disclosure;

[0049] Figure 7 This is a block diagram illustrating the configuration of a device for determining the amount of information about a planned route to be provided to a driver in a moving vehicle based on the driver's brainwave signals according to an embodiment of the present invention; and

[0050] Figure 8 This is a flowchart illustrating a method for operating a customized mobile driving path providing device according to an embodiment of the present invention. Detailed Implementation

[0051] 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 indicate the same or corresponding parts and features.

[0052] Exemplary embodiments of this disclosure will be described in detail so that those skilled in the art will readily understand and implement the devices and methods provided by the embodiments of this disclosure in conjunction with the accompanying drawings. However, this disclosure may be embodied in various forms, and its scope should not be construed as limited to the exemplary embodiments.

[0053] In describing embodiments of this disclosure, well-known functions or structures that may obscure the spirit of this disclosure will not be described in detail.

[0054] In embodiments of this disclosure, it will be understood that when an element is referred to as being "connected to," "coupled to," or "combined with" another element, it may be directly connected to or coupled to another element or combined with another element, or there may be intermediate elements between them. It will be further understood that, when used in embodiments of this disclosure, the terms "comprises," "includes," "have," etc., specify the presence of the stated features, integers, steps, operations, elements, components, and / or combinations thereof, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or combinations thereof.

[0055] It will be understood that although the terms “first,” “second,” etc., 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 any 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.

[0056] In embodiments of 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 distinct elements may be combined into a single hardware unit or a single software unit, and conversely, a single element may 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 may 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 can be embodied in hardware (e.g., a processor), software, or a combination of hardware and software.

[0057] In the embodiments of this disclosure, all constituent elements described in various forms should not be construed as essential elements, but rather some constituent elements may be optional elements. Therefore, embodiments configured in some form by corresponding subsets of constituent elements may also fall within the scope of this disclosure. Additionally, embodiments configured by adding one or more elements to various elements may also fall within the scope of this disclosure.

[0058] 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 at or below 30 Hz and potential differences of a few microvolts. Various waveforms may appear depending on brain activity and state. Research is underway using brainwave signals for interface control based on human intent. 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 (functional near-infrared spectroscopy), which utilizes changes in blood oxygen saturation. While fMRI and fNIRS are useful techniques for measuring brain activity, fMRI typically has lower temporal resolution, and fNIRS generally has lower spatial resolution. Due to these limitations, EEG signals are widely used due to their excellent portability and temporal resolution.

[0059] 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.

[0060] 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 each frequency component and indicating the corresponding amplitude. 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).

[0061] 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 of 3.5 Hz or below and amplitudes ranging from 20 μV to 200 μV, primarily observed in normal deep sleep or in newborns. Additionally, 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.

[0062] 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 between 8 Hz and 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 between 13 Hz and 30 Hz, primarily occurring in tolerable states of stress or when attracting a certain level of attention. Beta waves are primarily 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 anxiety and stress, they are sometimes called stress waves. Gamma waves are brain waves that typically have a frequency of 30 Hz to 50 Hz and mainly appear in states of intense excitement or during higher cognitive information processing. Additionally, gamma waves may occur during conscious wakefulness and REM sleep, and may overlap with beta waves.

[0063] 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 parts 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.

[0064] Furthermore, for 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.

[0065] To analyze ERPs, a process is needed to separate the signal from the noise. Averaging methods can be primarily used. Specifically, by averaging brain waves measured based on the stimulus onset time, brain waves irrelevant to the stimulus can be removed, and only relevant potentials—that is, brain activity typically associated with stimulus processing—can be selected.

[0066] 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.

[0067] The names of ERP peaks typically include polarity and the latent period (response 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 ms after the stimulus begins. Additionally, peaks are applied in the order of appearance, such as 1, 2, 3, or a, b, c. For example, P3 represents the third positive potential in the waveform after the stimulus begins.

[0068] The following text will describe various ERP systems.

[0069] For example, N100 is associated with responses to unpredictable stimuli.

[0070] MMN (Mismatch Negative Potential) can be generated by both focused and unfocused stimuli. MMN can be used as an indicator of whether a sensory memory (audio-visual memory) is operating before initial attention. The P300, described below, occurs during attention and judgment, while MMN is analyzed as a process that occurs in the brain prior to attention.

[0071] 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, which is a type of memory that occurs after attention.

[0072] 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 physical stimuli. P300 is widely used in brain-computer interface research.

[0073] 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.

[0074] 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.

[0075] For example, CNV refers to a potential that appears for 200ms to 300ms or even a few seconds in a later stage. It is also known as a slow potential (SP) and is associated with anticipation, preparation, mental activating, association, attention, and motor activity.

[0076] 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.

[0077] The main features of ERN will be described in more detail below.

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

[0079] refer to 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. Alternatively, the predetermined time range can be 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.

[0080] As an initial negative ERP, the ERN 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. Dopamine is also 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.

[0081] Additionally, ERNs may be generated within 0 to 100 ms after the start of an erroneous response guided by the frontal cortex during interfering tasks (e.g., Go-noGo tasks, Stroop tasks, Flanker tasks, and Simon tasks).

[0082] In addition, 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.

[0083] 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).

[0084] In addition, ERN can display changes in the magnitude of negative emotional states.

[0085] Furthermore, even when behavioral monitoring is conducted 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.

[0086] Furthermore, ERNs can be generated not only when a mistake or error is recognized, but also before a mistake or error is recognized.

[0087] 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.

[0088] In addition, ERNs can be generated not only as a response to mistakes or errors, but also as a response to anxiety or stress about a pre-determined task or object.

[0089] Furthermore, since a larger ERN peak is obtained, it can be assumed that a larger ERN peak reflects a more serious mistake or error.

[0090] Meanwhile, for example, Pe (positive error potential), an event-related potential (ERP) generated after the ERN, is a positive ERP that is generated primarily at the frontal cortex electrodes approximately 150 ms to 300 ms after the error or mistake occurs. Pe is referred to as the response of becoming aware of the error or mistake 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.

[0091] The main features of Pe will be described in more detail below.

[0092] Figure 2 This is a diagram illustrating the general waveforms of ERN and Pe according to another embodiment of the present disclosure.

[0093] refer to 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.

[0094] 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.

[0095] Figure 3 This is a diagram illustrating the deflection characteristics of Pe according to an embodiment of the present disclosure.

[0096] refer to Figure 3 Like P3, Pe has a wide deflection characteristic, and the cluster generator includes not only the regions of the posterior cingulate cortex and insular cortex, but also the region of the anterior cingulate cortex.

[0097] 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 that recognizes the error and elicits 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.

[0098] Figure 4A and Figure 4B This is a diagram showing the measurement areas of ERP and Pe according to an embodiment of the present disclosure.

[0099] 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.

[0100] For 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 be similar to the process of ERN.

[0101] 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.

[0102] 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.

[0103] 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 ERN and CRN according to an embodiment of the present disclosure.

[0104] 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 may be similar to that between ERN and Pe.

[0105] 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.

[0106] 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.

[0107] 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 regions of the cerebral cortex.

[0108] Figure 6This is a diagram illustrating an EEG measurement channel corresponding to a region of the cerebral cortex according to an embodiment of the present disclosure.

[0109] refer to 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 analysis can be performed on each cortical region using this data.

[0110] Figure 7 This is a block diagram illustrating the configuration of a device for determining the amount of information about a planned route to be provided to a driver in a moving vehicle based on the driver's brainwave signals according to an embodiment of the present invention.

[0111] The fundamental purpose of mobile vehicles is to transport passengers to their destinations. Recent technological advancements have tended to create a variety of convenient features that can be used on mobile vehicles, and an increasing number of systems (such as navigation and autonomous driving systems) appear to help drivers reach their destinations more easily.

[0112] At the same time, navigation and autonomous driving systems provide unified information independent of the driver and / or the route.

[0113] For example, predetermined devices within a moving vehicle typically provide a constant amount of information, regardless of the driver's skill level. In other words, they provide a constant amount of information regardless of whether the driver is a novice or an experienced driver.

[0114] For example, a predetermined device in a moving body provides constant information, regardless of the type of planned path. In other words, it provides constant information regardless of whether the planned path is familiar or unfamiliar.

[0115] In addition, to modify the amount of information provided in the mobile device, all items in the settings of the corresponding device should be changed manually.

[0116] Furthermore, recent research has shown that different brain regions are involved depending on whether a pathway is familiar or unfamiliar. Specifically, it is known that the familiarity or unfamiliarity of a pathway can be determined based on brainwave signals measured in the hippocampus and / or the posterior flexor cortex.

[0117] In this paper, the hippocampus is a region located in the temporal lobe of the human brain and is known to be involved in learning new things. Additionally, the hippocampus is known to be involved in driving to new, known destinations or along unfamiliar paths.

[0118] Furthermore, since the corpus callosum is a bundle of nerve fibers (white matter bundles) connecting the right and left hemispheres, and the splenial refers to the posterior part of the corpus callosum, the postsplenial cortex is known to be involved in learning about familiar things. Additionally, the postsplenial cortex is known to be involved in driving in familiar places or along familiar paths.

[0119] Therefore, familiarity with a driving path can be determined by analyzing the amplitude or activity of brainwave signals from the hippocampus and / or the posterior ventricular cortex.

[0120] Embodiments of this disclosure may provide apparatus and methods for determining information about a planned route to be provided to a driver in a mobile body based on brainwave signals generated from a predetermined area of ​​the driver's brain. Additionally, embodiments of this disclosure may provide apparatus and methods for controlling the operation of a mobile body based on the information to be provided.

[0121] refer to Figure 7 The customized mobile vehicle driving path providing device 700 may include sensors 710, analyzers 720, and / or controllers 730. However, it should be noted that only some components necessary for illustrating this embodiment are shown, and the components included in the customized mobile vehicle driving path providing device 700 are not limited to the examples described above. For example, two or more component units may be implemented in one component unit, and operations performed in one component unit may be divided and executed in two or more component units. Additionally, some component units may be omitted, or additional component units may be added.

[0122] According to embodiments of this disclosure, a customized mobile vehicle driving path providing device and / or method using brainwave signals can collect brainwave signals of the driver of the mobile vehicle in a predetermined channel area. Furthermore, the customized mobile vehicle driving path providing device of embodiments of this disclosure can determine the information about the planned path to be provided by analyzing the brainwave signals collected in the predetermined channel area. Additionally, the customized mobile vehicle driving path providing device of embodiments of this disclosure can control the operation of the mobile vehicle based on the information to be provided.

[0123] Specifically, the customized mobile vehicle driving path providing device 700 of the embodiments of this disclosure can collect brainwave signals of at least one passenger of a mobile vehicle in a predetermined channel area. Additionally, sensor 710 can perform operations.

[0124] Here, brainwave signals can represent brainwave signals in the time series plane.

[0125] Here, passengers may include the driver of the moving vehicle.

[0126] Furthermore, brainwave signals can represent brainwave signals at each frequency. Additionally, brainwave signals can represent the amplitude of brainwave signals at each frequency. Furthermore, the amplitude of brainwave signals at each frequency can represent the power of a frequency band within a predetermined range. In other words, the amplitude of brainwave signals at each frequency can represent the power obtained by converting a signal measured, for example, through Fourier transform, into a frequency band in the frequency domain.

[0127] In addition, brainwave signals can include oxygen saturation in a predetermined region.

[0128] Here, the predetermined channel region may include the region containing the hippocampus and the region containing the posterior cortex.

[0129] Furthermore, the customized mobile driving path providing device disclosed herein can determine the information about the planned path to be provided by analyzing the collected brainwave signals. Additionally, the analyzer 720 can perform operations.

[0130] In this paper, the analysis may include comparing the amplitude of brainwave signals collected in a predetermined channel region with a predetermined threshold. Alternatively, the analysis may include comparing the amplitude of brainwave signals collected in the predetermined channel region over a predetermined time period with a predetermined threshold. Here, the amplitude of the brainwave signal may represent the power spectrum of the brainwave signal at a specific frequency.

[0131] Additionally, the analysis may include comparing the magnitude of oxygen saturation collected in a predetermined channel region with a predetermined threshold.

[0132] Here, the threshold can be a preset value or a value input by the user. Furthermore, the threshold can be different for each driver from whom brainwave signals are collected. For example, the threshold could be a value reflecting the characteristics of each driver's brainwave signals. To reflect the analysis results of the brainwave signal characteristics, predetermined learning processing can be performed in advance for the characteristics displayed in the driver's brainwave signals. Additionally, the threshold can have multiple values.

[0133] Here, the threshold can be a statistical value based on the brainwave signals that the driver has previously learned.

[0134] Additionally, the analysis may include extracting brainwave signals for each frequency.

[0135] In addition, the brainwave signals used for analysis at each frequency can be statistical values ​​of brainwave signals collected over a predetermined period of time. For example, statistical values ​​can represent the average, weighted average, maximum, and minimum values.

[0136] In addition, this analysis can determine the driver's state from the point where the amplitude of the brainwave signal is equal to or greater than a predetermined threshold.

[0137] In addition, this analysis can determine the driver's condition from points in a predetermined region where the oxygen saturation level is equal to or greater than a predetermined threshold.

[0138] In this paper, the analysis may include comparing the amplitude of brainwave signals at each frequency collected over a predetermined time period with a predetermined threshold.

[0139] In this paper, a planned route can represent the path to the driver's desired destination, and the planned route can be set within the moving body. For example, the destination can be stored in the navigation system via passenger input.

[0140] In this document, the information to be provided regarding the planned route may include map information, voice guidance, and video guidance. Additionally, the amount of information to be provided may include the quantity of information intended for the driver.

[0141] For example, the information to be provided may include the size (scale) of the map provided in the navigation system, voice guidance information, guidance information about accident hazard areas, and guidance information about speed control areas.

[0142] For example, the information to be provided may include detailed guidance information given in complex routes. Here, complex routes may include left-turn routes, right-turn routes, detailed routes to complex intersections, and detailed routes to highways / highway ramps and exits.

[0143] For example, the information to be provided may include voice information given in the moving body. Voice information may include volume and direction.

[0144] Therefore, the customized mobile driving path providing device of the embodiments of this disclosure can adjust the amount of map information, voice guidance information and video guidance information of the planned path by analyzing the collected brainwave signals.

[0145] For example, the size of the planned path map can be adjusted when the collected brainwave signals are equal to or greater than a predetermined threshold.

[0146] For example, when the collected brainwave signals are equal to or greater than a predetermined threshold, the predetermined volume and direction of the speech provided in the moving body can be adjusted.

[0147] For example, when the collected brainwave signals are equal to or greater than a predetermined threshold, the amount of guidance information about the accident hazard area provided by the navigation system can be adjusted.

[0148] For example, when the oxygen saturation collected in a predetermined area is equal to or greater than a predetermined threshold, the amount of map information, voice guidance information, and video guidance information for the planned route can be adjusted.

[0149] Furthermore, a customized mobile driving path providing device can adjust the amount of information about the planned path to be provided by analyzing brainwave signals collected in regions including the hippocampus and / or regions including the subspinal cortex. Additionally, the customized mobile driving path providing device can adjust the amount of information about the planned path to be provided by analyzing oxygen saturation collected in regions including the hippocampus and / or regions including the subspinal cortex.

[0150] Since brainwave signals collected in areas including the hippocampus relate to driving on unfamiliar roads, a larger amplitude of brainwave signals collected in that area may indicate a more unfamiliar and less experienced route. Optionally, the driver may be less experienced.

[0151] When brainwave signals collected in the channel region including the hippocampus include a first signal and a second signal greater than the first signal, the amount of information to be provided in response to the second signal can be greater than the amount of information to be provided in response to the first signal. In other words, it can be determined that the path is less familiar in the case of the second signal than in the case of the first signal. Optionally, it can be determined that the driver is less experienced.

[0152] Here, brainwave signals can represent the amplitude or power spectrum of brainwave signals. Additionally, the amplitude of a brainwave signal can represent the power spectrum of a brainwave signal at a specific frequency.

[0153] Here, the first signal and the second signal can represent the amplitude or power spectrum of the first signal and the second signal, respectively. Alternatively, the amplitude of the first signal or the amplitude of the second signal can represent the power spectrum of the first signal and the power spectrum of the second signal at a specific frequency, respectively.

[0154] In this paper, when it is determined that the first signal is equal to or greater than a predetermined threshold, the information to be provided in response to the first signal can represent the information provided in the moving body based on the determination result.

[0155] For example, the proportion of the planned route map displayed on the navigation system monitor in response to the second signal can be greater than the proportion of the planned route map displayed in response to the first signal.

[0156] For example, the amount of guidance information about accident hazard areas provided in the navigation system and the vehicle's black box in response to the second signal can be greater than the amount of guidance information about accident hazard areas displayed on the navigation system monitor in response to the first signal. Here, guidance information about accident hazard areas may include speed bumps, accident-prone areas, child protection zones, rockfalls, smog areas, wildlife signs, school zones, narrow roads, downhill roads, highways under construction, curves, and reminders to fasten seat belts.

[0157] For example, the volume of music provided in the mobile body in response to the second signal can be lower than the volume of music provided in the mobile body in response to the first signal. In other words, the volume of sounds other than voice information on the planned path can be reduced to make the voice information more audible.

[0158] Here, the first signal and the second signal can be equal to or greater than a predetermined threshold, respectively.

[0159] On the other hand, since brainwave signals collected in areas including the posterior cortex involve driving along familiar roads, a planned route may be more familiar and comfortable when the amplitude of brainwave signals collected in that area is measured to be larger. Optionally, the driver may be more experienced.

[0160] When brainwave signals collected in a channel region including the posterior cortex include a first signal and a second signal greater than the first signal, the amount of information to be provided in response to the second signal may be less than the amount of information to be provided in response to the first signal. In other words, it can be determined that the path is more familiar in the case of the second signal than in the case of the first signal. Optionally, it can be determined that the driver is more experienced.

[0161] In this paper, when it is determined that the first signal is equal to or greater than a predetermined threshold, the information to be provided in response to the first signal can represent the information provided in the moving body based on the determination result.

[0162] For example, the proportion of the planned route map displayed on the navigation system monitor in response to the second signal can be smaller than the proportion of the planned route map displayed in response to the first signal.

[0163] For example, the amount of guidance information about accident hazard areas provided in the navigation system and the vehicle's black box in response to the second signal may be less than the amount of guidance information about accident hazard areas displayed on the navigation system monitor in response to the first signal. Here, guidance information about accident hazard areas may include speed bumps, accident-prone areas, child protection zones, rockfalls, smog areas, wildlife signs, school zones, narrow roads, downhill roads, highways under construction, curves, and reminders to fasten seat belts.

[0164] For example, the volume of music provided in the mobile body in response to the first signal can be lower than the volume of music provided in the mobile body in response to the second signal. In other words, the volume of sounds other than voice information on the planned path can be reduced to make the voice information more audible.

[0165] Here, the first signal and the second signal can be equal to or greater than a predetermined threshold, respectively.

[0166] Furthermore, the customized mobile driving path providing device of the embodiments of this disclosure can ultimately adjust the amount of information about the planned path to be provided by combining the analysis results of brain wave signals collected in regions including the hippocampus with the analysis results of brain wave signals collected in regions including the posterior flexor cortex.

[0167] For example, when the amplitude of brainwave signals collected in a region including the hippocampus is equal to or greater than the amplitude of brainwave signals collected in a region including the posterior ventricular cortex, the customized mobile driving path providing device of the embodiments of this disclosure can provide a greater amount of information than the amount of information about the planned path in the navigation system to be provided.

[0168] For example, when the amplitude of brainwave signals collected in regions including the hippocampus is less than the amplitude of brainwave signals collected in regions including the posterior ventricular cortex, the customized mobile driving path providing device of the present disclosure can provide a smaller amount of information than the amount of information about the planned path in the navigation system to be provided.

[0169] In this article, the information to be provided in the navigation system can be either user input or preset values ​​of the moving body.

[0170] Furthermore, the customized mobile driving path providing device of the embodiments of this disclosure can classify the type of planned path by analyzing brainwave signals collected in a predetermined channel area, and determine the information about the planned path to be provided based on the classified type.

[0171] For example, the customized mobile driving path providing device of embodiments of this disclosure can classify the type of planned path by analyzing the amplitude of brainwave signals collected in a predetermined channel area, and determine the amount of information about the planned path to be provided based on the classified type.

[0172] Here, the type of the planned path can be represented by multiple stages. For example, the type can include type 1, type 2, ..., type n (where n is an integer greater than 0).

[0173] Table 1 is an example showing the types of planned paths and the amount of information to be provided based on that type.

[0174] Table 1

[0175] First threshold Type 1 First Information Second threshold Type II Second Information Third threshold Type 3 Third Information … … …

[0176] Referring to Table 1, multiple thresholds can be used to classify the types. For example, brainwave signals equal to or greater than the first threshold can be classified as type 1. Additionally, brainwave signals equal to or greater than the second threshold and less than the first threshold can be classified as type 2. Here, the first threshold can be the highest, followed by the second and third thresholds (first threshold > second threshold > third threshold).

[0177] Additionally, the information to be provided can be determined based on the type. For example, in the case of the first type, the first information can be provided.

[0178] Furthermore, the amount of information to be provided can be sequential. For example, the first piece of information can have the largest quantity, followed by the second and third pieces of information (first information > second information > third information). Alternatively, the third piece of information can have the largest quantity, followed by the second and first pieces of information (third information > second information > first information).

[0179] The customized mobile driving path providing device 700 of the embodiments of this disclosure can control the operation of the mobile body based on the information to be provided. Additionally, the controller 730 can perform this operation.

[0180] Here, the moving body may include predetermined devices. For example, predetermined devices may include steering devices, pedal devices (accelerator pedal, brake pedal), transmissions, video systems, audio systems, navigation systems, and other moving body control devices.

[0181] As described above, each device used to provide the information to a mobile body can be controlled according to the amount of information. In other words, controlling the operation of a mobile body can mean adjusting the amount of information provided in predetermined devices included in the mobile body.

[0182] For example, the operation of navigation displays and voice devices can be changed to differ from existing settings.

[0183] For example, the operation of the moving black box can be changed to be different from the existing settings.

[0184] For example, the operation of the voice device provided in the mobile body can be changed to be different from the existing settings.

[0185] Here, the existing settings can be values ​​entered by the user or preset by the moving body.

[0186] Simultaneously, the customized mobile vehicle driving path providing device of this disclosure can determine the information about the planned path to be provided by analyzing the driver's brainwave signals. Therefore, while controlling the mobile vehicle, the customized mobile vehicle driving path providing device can update the predetermined thresholds used for analysis. In other words, the driver's brainwave signals, the amount of information to be provided, the mobile vehicle control operations, and / or preset thresholds can be added as learning data for setting the predetermined thresholds.

[0187] Figure 8 This is a flowchart illustrating a method for operating a customized mobile driving path providing device according to an embodiment of the present invention.

[0188] In step S801, the brainwave signals of the driver of the moving body can be collected in the predetermined channel area.

[0189] Here, the predetermined channel region may include at least one of a first region and a second region, the first region including the hippocampus and the second region including the posterior cortex.

[0190] Additionally, brainwave signals can be brainwave signals in the time-series plane.

[0191] In step S802, the information about the planned path to be provided can be determined by analyzing the brainwave signals collected in the predetermined channel area.

[0192] In this paper, the analysis may include comparing the amplitude of brainwave signals collected in a predetermined channel region with a predetermined threshold. Additionally, the analysis may include comparing the amplitude of oxygen saturation collected in a predetermined channel region with a predetermined threshold.

[0193] Determining the information to be provided about the planned path can be represented by classifying the type of planned path by analyzing brainwave signals collected in a predetermined channel area, and determining the information to be provided about the planned path based on the classified type.

[0194] In addition, determining the information to be provided about the planned path can involve classifying the type of planned path by analyzing the amplitude of brainwave signals collected in a predetermined channel region, and adjusting the amount of information to be provided about the planned path based on the classified type.

[0195] In addition, determining the information to be provided about the planned route can be represented by classifying the type of planned route by analyzing the magnitude of oxygen saturation collected in the predetermined channel area, and adjusting the amount of information to be provided about the planned route based on the classified type.

[0196] In addition, when the predetermined channel area includes a first area and a second area, determining the information about the planned path to be provided can mean that the information about the planned path to be provided is ultimately determined by combining the analysis results of the brainwave signals collected in the first area with the analysis results of the brainwave signals collected in the second area.

[0197] Meanwhile, when the predetermined channel area includes a first area and a first signal and a second signal greater than the first signal are collected in the first area, the amount of information to be provided in response to the second signal can be greater than the amount of information to be provided in response to the first signal.

[0198] Furthermore, when the predetermined channel area includes a second area and a first signal and a second signal greater than the first signal are collected in the second area, the amount of information to be provided in response to the second signal may be less than the amount of information to be provided in response to the first signal.

[0199] In step S803, the operation of the moving body can be controlled based on the information to be provided.

[0200] In this document, controlling the operation of a mobile body can refer to adjusting the amount of information provided in a predetermined device included in the mobile body.

[0201] Additionally, the predetermined equipment may include at least one of a steering device, a pedal device, a transmission, a video system, an audio system, a navigation system, and other moving body control devices.

[0202] The effects obtained in the embodiments of this disclosure are not limited to the effects described above, and other effects not mentioned above can be clearly understood by those skilled in the art based on the above description.

[0203] Although exemplary methods of embodiments of this disclosure are described as a series of operational steps for clarity of description, this disclosure is not limited to the order or sequence of the described operational steps. Operational steps may be performed simultaneously, or sequentially but in a different order. To implement the methods of embodiments of this disclosure, additional operational steps may be added and / or existing operational steps may be eliminated or replaced.

[0204] The various embodiments disclosed herein are not presented to describe all available combinations, but rather to describe only representative combinations. Various forms of steps or elements may be used individually or in combination.

[0205] Furthermore, various embodiments of this disclosure can be embodied in hardware, firmware, software, or a combination thereof. When embodiments of this disclosure are embodied in 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.

[0206] 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 executed on a device or computer, and non-transitory computer-readable media storing such software or machine-executable instructions, enabling the software or instructions to be executed on a device or computer.

[0207] The description of the embodiments in this disclosure is exemplary in nature only, and therefore, variations that do not depart from the spirit and scope of this disclosure are intended to be within its scope. Such variations should not be considered as departing from the spirit and scope of this disclosure.

Claims

1. A device for providing customized driving paths for a mobile body using brainwave signals, the device comprising: Sensors are configured to collect brainwave signals from the driver of the moving body in a predetermined channel area; The analyzer is configured to determine the information about the planned path to be provided by analyzing the brainwave signals collected in the predetermined channel area; as well as The controller is configured to control the operation of the moving body based on the information to be provided. The analyzer is configured to compare the amplitude of the brainwave signals collected in the predetermined channel region with a predetermined threshold to determine the information to be provided. The analyzer is configured to classify the type of the planned path by analyzing the brainwave signals collected in the predetermined channel region, and to determine the information about the planned path to be provided based on the classified type. The predetermined channel region is a region that includes at least one of a first region and a second region, wherein the first region includes the hippocampus and the second region includes the posterior flexor cortex. Wherein, when the predetermined channel area includes the first area and a first signal and a second signal greater than the first signal are collected in the first area, the analyzer is configured to determine that the driver of the moving body is less familiar with the driving route in the case of the second signal than in the case of the first signal, and that the amount of information to be provided in response to the second signal is greater than the amount of information to be provided in response to the first signal; Wherein, when the predetermined channel area includes the second area and a third signal and a fourth signal greater than the third signal are collected in the second area, the analyzer is configured to determine that the driver of the moving body is more familiar with the driving route in the case of the fourth signal than in the case of the third signal, and that the amount of information to be provided in response to the fourth signal is less than the amount of information to be provided in response to the third signal.

2. The device according to claim 1, wherein, When the predetermined channel region includes the first region and the second region, the analyzer is configured to ultimately determine the information about the planned path to be provided by combining the analysis results of the brainwave signals collected in the first region with the analysis results of the brainwave signals collected in the second region.

3. The device according to claim 1, wherein, The analyzer is configured to classify the type of the planned path by analyzing the amplitude of the brainwave signals collected in the predetermined channel region, and to adjust the amount of information about the planned path to be provided based on the classified type.

4. The device according to claim 1, wherein, The analyzer is configured to classify the type of the planned path by analyzing the magnitude of oxygen saturation collected in the predetermined channel region, and to adjust the amount of information about the planned path to be provided based on the classified type.

5. The device according to claim 1, wherein: The controller is configured to adjust the amount of information provided in a predetermined device within the mobile body; and The predetermined equipment includes at least one of a steering device, a pedal device, a transmission, a video system, an audio system, a navigation system, and other moving body control devices.

6. A method for providing a customized driving path for a mobile body using brainwave signals, the method comprising: Collect brainwave signals from the driver of the mobile vehicle within the designated channel area; The information about the planned path to be provided is determined by analyzing the brainwave signals collected in the predetermined channel area; as well as The operation of the moving body is controlled based on the information to be provided. The information to be provided includes: The amplitude of the brainwave signal collected in the predetermined channel region is compared with a predetermined threshold. The planned path type is classified by analyzing the brainwave signals collected in the predetermined channel area; and The information to be provided regarding the planned route is determined based on the classified type. The predetermined channel region is a region that includes at least one of a first region and a second region, wherein the first region includes the hippocampus and the second region includes the posterior flexor cortex. Wherein, when the predetermined channel area includes the first area and a first signal and a second signal greater than the first signal are collected in the first area, it is determined that the driver of the moving body is less familiar with the driving route in the case of the second signal than in the case of the first signal, and the amount of information to be provided in response to the second signal is greater than the amount of information to be provided in response to the first signal; Wherein, when the predetermined channel area includes the second area and a third signal and a fourth signal greater than the third signal are collected in the second area, it is determined that the driver of the moving body is more familiar with the driving route in the case of the fourth signal than in the case of the third signal, and the amount of information to be provided in response to the fourth signal is less than the amount of information to be provided in response to the third signal.

7. The method according to claim 6, wherein, When the predetermined channel area includes the first area and the second area, determining the information to be provided about the planned path includes combining the analysis results of brainwave signals collected in the first area with the analysis results of brainwave signals collected in the second area to ultimately determine the information to be provided about the planned path.

8. The method according to claim 6, wherein, The information to be provided regarding the planned route includes: The type of the planned path is classified by analyzing the amplitude of the brainwave signals collected in the predetermined channel region; and The amount of information about the planned path to be provided is adjusted based on the type of classification.

9. The method according to claim 6, wherein, The information to be provided regarding the planned route includes: The planned path type is classified by analyzing the magnitude of oxygen saturation collected in the predetermined channel area; and The amount of information about the planned path to be provided is adjusted based on the type of classification.

10. The method according to claim 6, wherein: The operation of controlling the mobile body includes: adjusting the amount of information provided in a predetermined device within the mobile body; and The predetermined equipment includes at least one of a steering device, a pedal device, a transmission, a video system, an audio system, a navigation system, and other moving body control devices.

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