System and method for analyzing changes in emotional state due to food intake

Abstract

This system analyzes changes in emotional state due to food intake, specifically focusing on the degree of stress increase or decrease and emotional changes resulting from the consumption of foods such as beverages and chocolate. [Solution] A system for analyzing changes in emotional state after food intake, which includes a control unit that determines emotional changes after food intake based on data on the user's brainwaves, comprises a system for analyzing changes in emotional state after food intake, in which the control unit determines the degree of the user's emotional state after food intake using an emotional index (EI) derived by combining first data including the ratio between alpha waves and beta waves from the brainwave data and second data regarding the asymmetry of beta waves output from the user's left and right frontal lobes.

Description

[Technical Field]

[0001] This invention relates to a system for analyzing changes in emotional state due to food intake, for analyzing the degree of stress increase or decrease and changes in emotions that occur as a result of consuming foods such as beverages and chocolate. [Background technology]

[0002] Food consumption trends in modern society are shifting beyond physical characteristics such as flavor and texture, towards emphasizing the personal feelings and emotional satisfaction consumers experience when consuming food. In particular, psychological effects such as stress reduction are acting as important criteria when choosing food, suggesting the need for a new method of food evaluation that considers consumers' emotional well-being, going beyond simple information such as nutrients and calories.

[0003] Furthermore, technologies are developing that use electroencephalography (EEG) to evaluate stress changes and emotional responses that occur when consuming food in real time. Such technologies play an important role in satisfying consumers' emotional needs by making it possible to objectively measure an individual's emotional state and psychological responses. These changes in food evaluation technology reflect a societal demand to comprehensively understand the impact that food has on consumers' emotions and health, not simply limited to taste and aroma.

[0004] However, sensory evaluations after consuming food have the problem of being difficult to derive as quantitative data. In particular, it has been difficult to quantitatively evaluate the degree to which people felt positive or negative emotions after consuming food. Therefore, researchers have come to judge changes in emotions before and after food consumption based on sensory evaluations using questionnaires from experimenters, which leads to a problem of reduced reliability of the evaluation. [Overview of the Initiative] [Problems that the invention aims to solve]

[0005] The technical objective of the present invention is to provide a system for analyzing changes in emotional state due to food intake, which analyzes the degree of stress increase or decrease and changes in emotions that occur as a result of consuming foods such as beverages and chocolate.

[0006] The technical problem of the present invention is to provide a food intake-induced emotional state change analysis system that can determine the stage of a user's emotions after consuming food, using an emotional index which is an index that combines various types of brain waves in order to judge the user's emotional changes. [Means for solving the problem]

[0007] The present invention provides an emotional state change analysis system due to food intake according to an embodiment of the present invention. In an emotional state change analysis system due to food intake, which includes a control unit that determines emotional changes after food intake based on data on the user's brainwaves, the control unit determines the degree of the user's emotional state after food intake using an emotional index (EI) derived by combining first data, which includes the ratio between alpha waves and beta waves from the brainwave data, and second data, which includes the asymmetry of beta waves output from the user's left and right frontal lobes.

[0008] For example, the emotion index is derived by analyzing each of the electroencephalogram signals measured from multiple channels arranged symmetrically with respect to the frontal lobe. The control unit compares the average value of the two emotion indices with the highest correlation among the multiple emotion indices with a table storing data for each stage of multiple emotions to determine the degree of the user's emotional state.

[0009] For example, the table stores each reference value for each stage of emotion, and the control unit determines the emotional stage indicated by the reference value that is closest to the average value among the reference values ​​as the user's emotional state.

[0010] For example, the first data is derived using electroencephalogram (EEG) signals acquired from each of several channels arranged symmetrically with respect to the frontal lobe, and the second data is derived using EEG signals acquired from two channels arranged symmetrically.

[0011] For example, the first data is the product of the 1-1 data, which is the ratio of alpha waves to beta waves, and the 1-2 data, which is the ratio of the sum of alpha waves and theta waves to beta waves, where the 1-1 data is a parameter for evaluating arousal and relaxation states, and the 1-2 data is a parameter for evaluating stress response.

[0012] For example, the second data is the difference in the log values ​​of the beta waves of two symmetrical channels, and the second data is a parameter for evaluating positive and negative sentiment changes.

[0013] For example, the sentiment index is the value obtained by multiplying the first data and the second data, and the sentiment index is,

number

[0014] For example, the control unit analyzes each electroencephalogram signal measured from each channel to derive the average of two of the emotion indices that have the most similar values ​​to each other, and the control unit determines the user's emotional state based on which of the already stored reference values ​​for distinguishing each stage of emotion the average value is similar to.

[0015] For example, the data for the electroencephalogram is obtained from multiple channels for measuring the user's electroencephalogram, each of which includes Fp1 and Fp2 arranged symmetrically, F3 and F4 arranged symmetrically, and F7 and F8 arranged symmetrically.

[0016] In one example, the control unit derives the emotion index for each of the channels, and the second data derived using the data for the electroencephalogram acquired through the Fp1 channel and the Fp2 channel is the same, and the second data derived using the data for the electroencephalogram acquired through the F3 channel and the F4 channel is the same, and the second data derived using the data for the electroencephalogram acquired through the F7 channel and the F8 channel is the same.

Advantages of the Invention

[0017] According to an embodiment of the present invention, a method for comprehensively and objectively determining stress and emotional changes due to food intake is provided. Through this, the sensory value of food products can be confirmed, which can have a positive impact on consumers' purchases, and thus can be utilized as a basis for evaluating and improving the market success rate of products.

[0018] According to an embodiment of the present invention, the control unit can determine the emotional stage of a user after food intake using the emotion index obtained from the user's electroencephalogram data. At this time, the emotion index is a combination of three parameters for determining stress, arousal-relaxation state, and positive-negative state, and can be a highly reliable parameter when determining the user's complex emotional state.

[0019] According to an embodiment of the present invention, it is possible to quantify the emotional changes and emotional stages of a user that are difficult to quantify through an emotional state change analysis system due to food intake.

Brief Description of the Drawings

[0020] [Figure 1] It is a diagram showing the positions of a plurality of channels constituting an electroencephalogram measuring device according to an embodiment of the present invention. [Figure 2] It is a block diagram showing an emotional state change analysis system due to food intake according to an embodiment of the present invention. [Figure 3]This is a heatmap showing the correlation analysis results for each channel and its respective emotional index after ingestion of sample A according to an embodiment of the present invention. [Figure 4] This is a heatmap showing the results of a correlation analysis of each channel with respect to each emotional index after ingestion of sample B according to an embodiment of the present invention. [Figure 5] This is a flowchart illustrating a method for analyzing changes in emotional state due to food intake according to an embodiment of the present invention. [Modes for carrying out the invention]

[0021] The advantages and features of the present invention, and the methods for achieving them, will become clear with reference to the embodiments described below in detail with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below and can be embodied in a variety of different forms, and these embodiments are provided only to complete the disclosure of the present invention and to fully inform those who are ordinary skill in the art to which the present invention pertains, and the present invention is defined only by the scope of the claims. The same reference numerals throughout the specification refer to the same components.

[0022] The terms "...part," "...unit," and "...module" used in this specification refer to a unit that processes at least one function or operation, which may be embodied in hardware, software, or a combination of hardware and software.

[0023] Furthermore, the reason why the names of the components are distinguished as "First," "Second," etc. in this specification is to differentiate them because their names are the same, and the order is not necessarily limited in the following explanation.

[0024] The detailed description is illustrative of the present invention. Furthermore, the foregoing describes preferred embodiments of the present invention, and the present invention can be used in a variety of other combinations, modifications, and environments. That is, modifications or alterations are possible within the scope of the concept of the invention disclosed herein, within the scope equivalent to the described disclosures, and / or within the scope of the art or knowledge of the art. The described examples illustrate the best-case scenario for embodying the technical idea of ​​the present invention, and various modifications are possible as required in the specific field of application and use of the present invention. Therefore, the above detailed description of the invention is not intended to limit the present invention to the disclosed embodiments. Furthermore, the appended claims should be interpreted as including other embodiments.

[0025] Figure 1 shows the positions of multiple channels constituting an electroencephalogram (EEG) measuring device according to an embodiment of the present invention.

[0026] Referring to Figure 1, the electroencephalogram (EEG) measuring device 100 may include a plurality of channels 11, 12, 21, 22, 31, and 32 that are arranged symmetrically with respect to the human frontal lobe. For example, the EEG measuring device 100 may be a DSI-24 model from Wearable Sensing, and the EEG measuring device may be a 20-channel dry EEG analyzer. However, in the embodiment of the present invention, the plurality of channels 11, 12, 21, 22, 31, and 32 may be six channels Fp1, Fp2, F3, F4, F7, and F8 that are symmetrically arranged with respect to the frontal lobe. Fp1(11) and Fp2(12) may be arranged symmetrically with respect to each other, F3(21) and F4(22) may be arranged symmetrically with respect to each other, and F7(31) and F8(32) may be arranged symmetrically with respect to each other. Fp1(11) and Fp2(12) may be the channels that are located closest to the user's eyes. During the process of measuring the user's brainwaves using the electroencephalogram (EEG) measuring device 100, the impedance of each channel 11, 12, 21, 22, 31, and 32 was maintained at less than 5 kΩ, and the sampling rate was 500 Hz.

[0027] Figure 2 is a block diagram showing an example of the present invention related to the analysis system for analyzing changes in emotional state due to food intake.

[0028] Referring to Figures 1 and 2, the system for analyzing changes in emotional state due to food intake can be implemented through an electroencephalogram (EEG) measuring device 100, a control unit 200, and a display 300.

[0029] The electroencephalogram (EEG) measuring device 100 can extract alpha waves, beta waves, and theta waves through each of the channels 11, 12, 21, 22, 31, and 32. For example, alpha waves may be brain waves in the 8 Hz to 12.9 Hz band, beta waves in the 13 Hz to 30 Hz band, and theta waves in the 4 Hz to 7.9 Hz band. The EEG measuring device 100 can measure the amplitude and power of each of the alpha waves, beta waves, and theta waves, and can measure the proportion of alpha waves, beta waves, and theta waves in the total EEG signal bandwidth (4 Hz to 30 Hz) over a specific period of time. In other words, during the stage of converting the electroencephalogram (EEG) signal to frequency power, the first 10 seconds and the last 10 seconds of EEG data are removed to eliminate potential noise and emotional disturbance at the beginning and end of the entire minute of EEG signal. Only the middle 50 seconds are acquired, and then the relative theta waves (4-7.9Hz / 4-30Hz), relative alpha waves (8-12.9Hz / 4-30Hz), and relative beta waves (13-30Hz / 4-30Hz) necessary for analysis can be extracted. The data for the alpha waves, beta waves, and theta waves of each channel 11, 12, 21, 22, 31, and 32 measured by the EEG measuring device 100 can be transmitted to the control unit 200.

[0030] The control unit 200 may include a processor and memory for processing and analyzing the measured electroencephalogram (EEG) signals. The processor may consist of one or more cores and may include processors for data analysis and deep learning, such as a central processing unit (CPU), a general-purpose graphics processing unit (GPGPU), a tensor processing unit (TPU), and an application central processing unit (AP). One or more processors may be controlled to process input data according to predefined operating rules or artificial intelligence models stored in memory. If one or more processors are dedicated artificial intelligence processors, they may be designed with hardware structures specialized for processing specific artificial intelligence models.

[0031] The processor can read computer programs or instructions stored in memory and perform the analysis of emotional state changes due to food intake according to this embodiment. The memory can store various information and table 210 required for the emotional state change analysis system due to food intake according to the embodiment of the present invention. The memory can include at least one type of storage medium from among flash memory type, hard disk type, multimedia card micro type, card type memory (e.g., SD or XD memory), RAM (Random Access Memory), SRAM (Static Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory), magnetic memory, magnetic disk, and optical disk. The memory can also include any form of computer-readable recording medium that is well known in the art to which the present invention belongs. The above description of memory is illustrative and the present disclosure is not limited thereto.

[0032] The control unit 200 can analyze changes in the user's emotional state before and after consuming food. The control unit 200 can determine the degree of the user's emotional state after consuming food using an emotional indicator (EI) derived by combining first data, which includes the ratio between alpha waves and beta waves from the electroencephalogram (EEG) data measured from the EEG measuring device 100, and second data, which includes the asymmetry of beta waves output from the user's left and right frontal lobes. The control unit 200 may include a table 210 that stores reference values ​​for each stage of emotion, a data processing unit 230 that derives the emotional indicator, and an emotion discrimination unit 250 that determines the stage of the user's emotion using the emotional indicator.

[0033] The data processing unit 230 processes electroencephalogram (EEG) signals acquired from channels 11, 12, 21, 22, 31, and 32, which are arranged symmetrically with respect to the frontal lobe, and can derive an emotional index. The emotional index can be derived by combining first data, which includes the ratio between alpha waves and beta waves, and second data, which includes the asymmetry of beta waves output from the user's left and right frontal lobes.

[0034] The first data can be derived using electroencephalogram (EEG) signals acquired from each of several channels 11, 12, 21, 22, 31, and 32, which are arranged symmetrically with respect to the frontal lobe. The first data may be the product of the 1-1 data, which is the ratio of alpha waves to beta waves, and the 1-2 data, which is the ratio of the sum of alpha and theta waves to beta waves. Specifically, the 1-1 data can represent the ratio of the frequency power of alpha waves to the frequency power of beta waves, and the 1-2 data can represent the ratio of the sum of the frequency power of alpha waves and theta waves to the frequency power of beta waves. The 1-1 data may be an ABR (Alpha / Beta Ratio) parameter for evaluating arousal and relaxation states, and the 1-2 data may be an ATBR (Alpha-Theta / Beta Ratio) parameter for evaluating stress response.

[0035]

number

[0036]

number

[0037] The second data can be derived using electroencephalogram (EEG) signals acquired from two channels arranged symmetrically with respect to the frontal lobe. The second data may be the difference in the logarithmic values ​​of the beta waves from the two symmetrical channels. Specifically, the second data may be the difference between the logarithmic value of the beta wave frequency power acquired from one of the two symmetrical channels and the logarithmic value of the beta wave frequency power acquired from the other channel. The second data may be the FBA (Frontal Beta Asymmetry) parameter for evaluating positive and negative emotional changes.

[0038]

number

[0039] The second data cannot be derived solely from electroencephalogram (EEG) signals acquired through a single channel. However, the second data can be derived through EEG signals acquired from two channels arranged symmetrically to each other. For example, the second data can be derived using EEG signals acquired by F3(21) and F4(22), and the second data can be derived using EEG signals acquired by F7(31) and F8(32). Therefore, the second data derived using data for EEGs acquired through Fp1(11) and Fp2(12) may be identical, the second data derived using data for EEGs acquired through F3(21) and F4(22) may be identical, and the second data derived using data for EEGs acquired through F7(31) and F8(32) may be identical.

[0040] As for the three parameters mentioned above, an increase in the change before and after food intake indicates emotional relaxation, positive emotions, and stress reduction, all of which are judged as positive responses, while a decrease in the change indicates emotional arousal, negative emotions, and increased stress, all of which may be judged as negative responses. In other words, for the sake of ease of judgment of the results, the same index was used for all three parameters, indicating the same trend in increase / decrease. The parameter derived by fusing the three parameters may be an emotional index. The emotional index can be derived as the product of the 1-1 parameter, the 1-2 parameter, and the 2 parameter.

[0041]

number

[0042] Table 210 can include reference values ​​for classifying emotional stages using experimentally acquired emotional indices. To construct Table 210, first, multiple experimenters were asked to smell fruit scents that could induce positive emotional states and strange odors that could induce negative emotional states. Second, auditory stimuli were provided to amplify positive emotional states and auditory stimuli to amplify negative emotional states. The specific experimental methods are shown in the table below.

[0043] [Table 1]

[0044] In the experiment, auditory stimuli to amplify positive emotional states may include comments that provide mental stability, comments that induce physical stability, and comments that can induce positive imagination in the experimenter. That is, the auditory stimuli provided to the experimenter can be varied to distinguish between stages of positive emotion. Auditory stimuli to amplify negative emotional states may include repetitive sounds through a metronome. To distinguish between stages of negative emotion, silence may be provided, or the number of repetitions per second of the metronome may be varied. The stages of emotion can be divided into three negative stages, two negative stages, one negative stage, one positive stage, two positive stages, and three positive stages. Two positive stages may represent a more positive state than one positive stage, and three positive stages may represent a more positive state than two positive stages. Two negative stages may represent a more negative state than one negative stage, and three negative stages may represent a more positive state than two negative stages.

[0045] Through the experiment, average values ​​for emotional indices categorized by emotional stages can be derived. These average values ​​for emotional indices can serve as baseline values ​​for classifying emotional stages. The specific experimental data for the emotional indices are shown in the table below, however, the specific numerical values ​​of the experimental data and baseline values ​​may be subject to change by the designer.

[0046] [Table 2]

[0047] The emotion discrimination unit 250 can analyze the user's emotion indicators derived by the data processing unit 230 and determine the user's emotional stage. The emotion discrimination unit 250 can determine the two emotion indicators with the highest correlation among multiple emotion indicators for the electroencephalogram signals acquired in each channel 11, 12, 21, 22, 31, and 32. In other words, the emotion discrimination unit 250 can determine the two channels with a high degree of similarity between the emotion indicators. The correlation can mean the result of analyzing what kind of linear relationship exists between two variables by correlation analysis, and can also be called the correlation coefficient. For example, the correlation can be the Pearson correlation coefficient. The two variables can be in an independent relationship or a correlated relationship, in which case the strength of the relationship between the two variables is called the correlation. The two variables can mean the emotion indicators obtained by processing the electroencephalogram signals acquired in two channels. If the two variables are completely identical, the correlation can be derived as +1. If two variables are completely different, the correlation can be derived as 0, and if two variables are completely identical in opposite directions, the correlation can be derived as -1. The correlation (correlation coefficient) can be derived by the following formula.

[0048]

number

[0049] At this time, x i and y i This can be the value of two variables,

number

[0050] The emotion discrimination unit 250 can determine the average value of each emotion index derived as electroencephalogram (EEG) signals measured by the two channels with the highest correlation. In terms of the reliability of the EEG signals, the average value of the emotion index for the two channels with the highest correlation between each emotion index can be used. The emotion discrimination unit 250 can compare the average value with a table 210 that stores data for each stage of multiple emotions and determine the degree of the user's emotional state. Specifically, the emotion discrimination unit 250 can derive the reference value that has the smallest absolute difference between each reference value derived for each stage of emotion and the average value. That is, the stages of emotion can be distinguished by the reference value that shows the value most similar to the average value.

[0051] For example, if the average value of the sentiment index for the two channels with the highest correlation is 0.2, then the value of 0.2 will fall between positive level 1 and positive level 2 in Table 2. However, the absolute difference between the baseline value for positive level 2 and the average value is 0.104, and the absolute difference between the baseline value for positive level 1 and the average value is 0.193. Therefore, the sentiment discrimination unit 250 can determine that positive level 2 is the user's emotional level.

[0052] For example, the emotion discrimination unit 250 can distinguish between the degree of emotion, going beyond simply determining the emotional stages among multiple users. The emotion discrimination unit 250 can distinguish between the degree of emotion based on how close the average value of the emotion index for the two channels with the highest correlation is to a specific baseline value, or how far it is from a specific baseline value. If the average value for the first user is 0.2 and the average value for the second user is 0.22, the absolute difference between the average value for the first user and the baseline value indicating positive stage 2 is 0.104, and the absolute difference between the average value for the second user and the baseline value indicating positive stage 2 is 0.084. The difference between the average value for the second user and the baseline value indicating positive stage 2 is smaller than the difference between the average value for the first user and the baseline value indicating positive stage 2. Therefore, the emotion discrimination unit 250 can determine that both the first and second users are in a positive stage 2 emotional state, but the emotional state of the second user is even more positive than that of the first user.

[0053] According to an embodiment of the present invention, the control unit 200 can determine the emotional stage of the user after consuming food using an emotional index acquired from the user's electroencephalogram (EEG) data. In this case, the emotional index is a combination of three parameters for determining stress, arousal-relaxation, and positive-negative states, and can be a reliable parameter for determining the user's complex emotional state.

[0054] According to an embodiment of the present invention, it is possible to quantify the emotional changes and emotional stages of users, which are difficult to quantify, through a system for analyzing changes in emotional state due to food intake.

[0055] Figure 3 is a heatmap showing the results of correlation analysis of each channel with respect to each emotional index after ingestion of sample A according to an embodiment of the present invention.

[0056] Figure 3 shows a correlation analysis heat map by channel of an electroencephalogram measuring device for emotional indicators before and after the intake of Sample A (chocolate) for the subject. The degree of correlation of the emotional indicators due to the intake of Sample A appears highest in the two channels of F4 and F8, and the average value is 0.0301. The results of calculating the difference in the distance between the average value and each reference value for each stage of emotion are as follows.

[0057] d -3 =|0.0301 - (-14.522)| = 14.552

[0058] d -2 =|0.0301 - (-3.726)| = 3.757

[0059] d -1 =|0.0301 - (-0.014)| = 0.044

[0060] d +1 =|0.0301 - (0.007)| = 0.023

[0061] d +2 =|0.0301 - (0.304)| = 0.274

[0062] d +3 =|0.0301 - (0.574)| = 0.543

[0063] As a result of calculating the difference in the distance between the average value and each reference value, the distance to the first stage of affirmation (d +1 ) is the closest, so the control unit can determine that the stage of the user's emotion is the first stage of affirmation.

[0064] Figure 4 is a heat map showing the correlation analysis results for each channel with respect to each emotional indicator after the intake of Sample B according to an embodiment of the present invention.

[0065] Figure 4 shows a heatmap of the correlation analysis of different electroencephalogram (EEG) channels for emotional indices before and after consumption of sample B (carbonated beverage) in the subjects. The correlation between consumption of sample B and emotional indices was highest in two channels, Fp1 and F8, with an average value of -0.0072. The difference in distance between the average value and each baseline value for each emotional stage was calculated and the results are as follows.

[0066] d -3 =|-0.0072-(-14.522)|=14.525

[0067] d -2 =|-0.0072-(-3.726)|=3.719

[0068] d -1 =|-0.0072-(-0.014)|=0.006

[0069] d +1 =|-0.0072-(0.007)|=0.014

[0070] d +2 =|-0.0072-(0.304)|=0.311

[0071] d +3 =|-0.0072-(0.574)|=0.581

[0072] The result of calculating the difference in distance between the mean and each reference value was a negative 1 stage (d -1 Because it is closest to the control unit, the control unit can determine that the user's emotional state is at negative level 1.

[0073] Figure 5 is a flowchart illustrating the method for analyzing changes in emotional state due to food intake according to an embodiment of the present invention. For the sake of simplicity, redundant information has been omitted.

[0074] Referring to Figure 5, the electroencephalogram (EEG) measuring device can measure EEG signals through each of multiple channels. The measured EEG data can be transmitted to the control unit (S100).

[0075] The control unit can convert the electroencephalogram (EEG) data for each channel into frequency power. Specifically, the control unit can calculate the proportion of alpha waves, beta waves, and theta waves within the 4Hz to 30Hz range of the EEG signal. The control unit can derive the 1-1 data, 1-2 data, and 2 data using the alpha wave power, beta wave power, and theta wave power. The control unit can derive the 1-1 data, 1-2 data, and 2 data for each channel and derive the emotional index (EI), which is the product of these (S200).

[0076] The control unit can perform correlation analysis between each emotion index. Based on the results of the correlation analysis between multiple emotion indexes, the control unit can determine the two channels with the highest correlation. That is, the control unit can use the electroencephalogram data acquired through the two channels with the highest correlation out of the six channels, or the emotion index associated with the two channels, to determine the change in the user's emotions before and after consuming food (S300).

[0077] The control unit can derive the average value of each emotion index corresponding to the two channels with the highest correlation (S400).

[0078] The control unit can compare the average value with a previously saved table to determine the emotional stage. The previously saved table can store each reference value used to distinguish each stage of emotion, which is divided into multiple stages. The control unit can determine the reference value that most closely approximates the average value, and can determine the emotional stage indicated by the determined reference value as the user's emotional state after consuming food (S500).

[0079] Although embodiments of the present invention have been described above with reference to the attached drawings, a person with ordinary skill in the art to which the present invention pertains will understand that the present invention can be implemented in other specific forms without changing its technical idea or essential features. Therefore, it should be understood that the embodiments described above are illustrative in all respects and not limiting.

Claims

1. In a food intake-induced emotional state change analysis system, which includes a control unit that determines emotional changes after food intake based on data on the user's brainwaves, The control unit determines the degree of the user's emotional state after food intake using an emotional index (EI) derived by combining first data, which includes the ratio between alpha waves and beta waves among the brainwave data, and second data, which includes the asymmetry of beta waves output from the user's left and right frontal lobes, as part of the emotional state analysis system due to food intake.

2. The aforementioned emotional index is derived by analyzing each of the electroencephalogram signals measured from multiple channels arranged symmetrically with respect to the frontal lobe. The control unit compares the average value of the two emotion indicators with the highest correlation among the plurality of emotion indicators with a table storing data for each stage of the plurality of emotions, and determines the degree of the user's emotional state, as described in claim 1, for the emotional state change analysis system due to food intake.

3. The aforementioned table stores each baseline value for each stage of emotion, The control unit determines the emotional stage indicated by the reference value that is closest to the average value among the reference values ​​as the user's emotional state, in the food intake-induced emotional state change analysis system according to claim 2.

4. The aforementioned first data was derived using electroencephalogram (EEG) signals acquired from each of several channels arranged symmetrically with respect to the frontal lobe. The system for analyzing changes in emotional state due to food intake, according to claim 1, wherein the second data is derived using electroencephalogram signals acquired from two channels arranged symmetrically on the left and right sides.

5. The first data mentioned above is the value obtained by multiplying the 1-1 data, which is the ratio of alpha waves to beta waves, by the 1-2 data, which is the ratio of the sum of alpha waves and theta waves to beta waves. The data described above (1-1) are parameters for evaluating the state of wakefulness and relaxation. The first and second data sets are parameters for evaluating stress responses, as described in claim 4, for the system of analyzing changes in emotional state due to food intake.

6. The second data is the difference between the log values ​​of the beta waves of two mutually symmetrical channels. The food intake-induced emotional state change analysis system according to claim 5, wherein the second data is a parameter for evaluating positive and negative emotional changes.

7. The aforementioned sentiment index is the value obtained by multiplying the first data and the second data. The aforementioned emotion indicators are, [Math 1] The system for analyzing changes in emotional state due to food intake, as described in claim 6.

8. The control unit analyzes each electroencephalogram signal measured from each channel and derives the average value of two of the emotion indices that have the most similar values ​​to each other from among a plurality of emotion indices. The control unit determines the user's emotional state based on which of the already stored reference values ​​for classifying each stage of emotion the average value is similar to, according to claim 7, for the emotional state change analysis system due to food intake.

9. The data for the aforementioned brainwaves is acquired from multiple channels for measuring the user's brainwaves. The system for analyzing changes in emotional state due to food intake according to claim 1, wherein each channel includes Fp1 and Fp2 arranged symmetrically with respect to each other, F3 and F4 arranged symmetrically with respect to each other, and F7 and F8 arranged symmetrically with respect to each other.

10. The control unit derives the emotion index for each of the channels, The second data derived using the data for the electroencephalogram acquired through the Fp1 channel and the Fp2 channel is identical. The second data derived using the data for the electroencephalogram acquired through the F3 channel and the F4 channel is identical, The system for analyzing changes in emotional state due to food intake according to claim 1, wherein the second data derived using the data for the electroencephalogram acquired through the F7 channel and the F8 channel is the same.

Images