A method for testing beer preference of consumers based on electroencephalogram analysis and quantification

Abstract

The application is suitable for the technical field of consumer preference research, and provides a beer consumer preference test method based on electroencephalogram analysis quantification, which comprises the following steps: beer drinking consumer recruitment; analyzing and evaluating the beer preference of consumers through electroencephalogram recording analysis and combining with the consumer preference questionnaire evaluation mode; prompting the consumer through the test standard program edited by MATLAB; the consumer performs experimental operation according to the screen prompt, and evaluates the samples one by one; after the electroencephalogram data is processed, the original data of eight kinds of electroencephalogram waves and two emotional dimensions of concentration and relaxation are analyzed and obtained, an integrated emotional experience index model is established by taking the emotions in different evaluation stages and the eight wave bands as characteristics, and the emotions significantly distinguishing different samples and different evaluation stages are researched. The accuracy and usability of the application are good, and the analysis through objective means such as electroencephalogram is more easily accepted in terms of scientificity and reliability.

Description

Technical Field

[0001] This invention belongs to the field of consumer preference research technology, and in particular relates to a method for testing consumer beer preferences based on electroencephalogram (EEG) analysis. Background Technology

[0002] Consumer research plays an increasingly important role in food research and development and promotion. Consumer choices are influenced by factors such as product quality, brand, price, and the purchasing environment. Research on food shopping drivers has found that brand influence is gradually decreasing for today's consumers, while high-quality, cost-effective, and suitable products are more favored. This reflects a shift in consumer philosophy towards product quality and actual value. In today's rapidly changing consumer market, accurately capturing consumer needs and ideas is becoming increasingly important for food research.

[0003] Consumer research methods can be divided into direct and indirect approaches. Direct research methods, such as interviews and questionnaires, have advantages such as ease of operation and low cost. However, the results of direct research methods may be affected by consumer cognitive biases. This means that these methods can only measure consumers' subjective perceptions and emotional responses, but not the emotional responses themselves (whether they are intentional or unintentional, or whether some consumers may not be entirely honest when filling out questionnaires). Direct research methods are based on subjective self-feelings and lack the ability to capture subconscious emotions. Indirect research methods are not affected by consumers' cognitive biases and can assess consumers' subconscious autonomous emotional responses, obtaining more objective emotional responses. Currently, methods such as facial expressions, electroencephalography (EEG), electrodermatology (TEG), and heart rate can measure typical physiological or physical responses representing different emotions.

[0004] Research in neuroscience, psychology, and cognitive science has shown that electroencephalogram (EEG) data can reflect human psychological activities and cognitive behaviors, and is increasingly being used to examine consumers' cognitive processes or emotional responses to pre-designed marketing stimuli. The delta (1–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), and beta (13–30 Hz) bands of EEG are closely related to various physiological and psychological activities and can reflect changes in consumers' emotions. When extracting EEG frequency domain features, many scholars first map EEG signals to these frequency bands, and then extract the corresponding frequency domain features for each band to derive consumers' excitement, relaxation, and emotional valence during food evaluation. This application uses a method combining EEG analysis and consumer questionnaires to establish a consumer preference test model based on EEG analysis to analyze and predict consumers' true preference for beer. Summary of the Invention

[0005] The purpose of this invention is to provide a method for testing consumer beer preferences based on electroencephalogram (EEG) analysis, aiming to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method for testing consumer beer preferences based on electroencephalogram (EEG) analysis includes the following steps:

[0008] Step S1: Recruitment and preference experiment of beer drinking consumers;

[0009] Step S2, Evaluation Method: The experiment analyzes and evaluates consumers' beer preferences by combining EEG recordings with consumer preference questionnaires; a standard test program edited in MATLAB is used to provide consumers with feedback.

[0010] Step S3, Evaluation Process: Consumers perform the experimental operations according to the on-screen prompts and evaluate each sample one by one;

[0011] Step S4: Data Processing and Analysis Modeling: After extraction and artifact removal, the EEG data is analyzed to obtain the raw data of eight types of EEG waves and two emotional dimensions of focus and relaxation. Based on the emotions of different evaluation stages and the eight wave bands, a comprehensive emotional experience index model is established to study the emotions of different samples and different evaluation stages.

[0012] Furthermore, in step S3, the evaluation of each sample is conducted from three aspects: appearance, aroma, and taste. The specific operation is as follows:

[0013] Appearance: Hold the sample up to the light and observe the hue and transparency of the sample in the cup for 30 seconds;

[0014] Aroma: Hold the sample under your nose and gently rotate it, smelling for 30 seconds to appreciate the aroma of the sample;

[0015] Taste: Swallow the sample and savor the overall sensation over the next 20 seconds;

[0016] Consumers took turns rating the samples using a sensory questionnaire.

[0017] Furthermore, in step S4, the comprehensive emotional experience index model is as follows:

[0018] f(x)=ω T +b

[0019] Where f is the Integrated Emotional Experience Index (IAA), x is the feature vector space composed of the emotional dimension and eight band indices, b is the residual, and ω = (ω1, ω2, ..., ω...). d ), is the coefficient matrix.

[0020] Furthermore, in step S4, the specific operation for obtaining the comprehensive preference index at different evaluation stages is as follows:

[0021] The average IAA index of each sample at each evaluation stage is taken as the feature R = (R1, R2, ..., R m Assign values ​​to it according to the weighting method:

[0022] g(x) = A T R+σ

[0023] Where g is the final comprehensive emotional experience score, σ is the residual, and A = (a1, a2, a3, ..., a... m ), is the coefficient matrix.

[0024] Furthermore, in step S1, the recruited beer consumers consist of consumers who have not undergone professional sensory evaluation. These consumers are between 18 and 50 years old, have no taste, smell, or visual impairments, do not smoke, are regular consumers who evaluate products, and have no product dependence or allergic behavior.

[0025] Furthermore, in step S2, the consumer preference questionnaire uses a nine-point preference scale.

[0026] Furthermore, in step S3, during the evaluation of each sample, the recruited beer-drinking consumers clean their mouths to ensure that they do not interfere with the testing of the next sample.

[0027] Compared with the prior art, the beneficial effects of the present invention are:

[0028] This consumer beer preference testing method based on electroencephalogram (EEG) analysis uses an EEG chip to acquire raw brainwave data generated during beer consumption, along with the signal strength of the raw brainwave data, when the consumer's brain is in a relaxed state. Multiple raw brainwave data points temporarily stored during the acquisition period are processed to obtain baseline brainwave data. By combining EEG analysis with consumer questionnaires, a consumer preference testing model based on EEG analysis is established to analyze and predict consumers' true beer preferences. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of a method for testing consumer beer preferences based on electroencephalogram (EEG) analysis. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0031] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0032] An embodiment of the present invention provides a method for testing consumer beer preferences based on electroencephalogram (EEG) analysis, comprising the following steps:

[0033] Step S1: Recruitment and preference experiment of beer drinking consumers;

[0034] Step S2, Evaluation Method: The experiment analyzes and evaluates consumers' beer preferences by combining EEG recordings with consumer preference questionnaires; a standard test program edited in MATLAB is used to provide consumers with feedback.

[0035] Step S3, Evaluation Process: Consumers perform the experimental operations according to the on-screen prompts and evaluate each sample one by one;

[0036] Step S4: Data Processing and Analysis Modeling: After extraction and artifact removal, the EEG data is analyzed to obtain the raw data of eight types of EEG waves and two emotional dimensions of focus and relaxation. Based on the emotions of different evaluation stages and the eight wave bands, a comprehensive emotional experience index model is established to study the emotions of different samples and different evaluation stages.

[0037] In this embodiment of the invention, a standard testing program edited using MATLAB provides consumers with information including the testing process and timing. During the experiment, consumers wear EEG caps to capture and analyze their EEG signals during the evaluation process. The experiment employs a blind sample testing method, selecting a group of 5-6 samples, each placed in a transparent container labeled with a 3-digit random number. The samples are evaluated in a random order to eliminate interference from the evaluation sequence.

[0038] In a preferred embodiment of the present invention, in step S3, the evaluation of each sample is conducted from three aspects: appearance, aroma, and taste. Specifically, the operation is as follows:

[0039] Appearance: Hold the sample up to the light and observe the hue and transparency of the sample in the cup for 30 seconds;

[0040] Aroma: Hold the sample under your nose and gently rotate it, smelling for 30 seconds to appreciate the aroma of the sample;

[0041] Taste: Swallow the sample and savor the overall sensation over the next 20 seconds;

[0042] Consumers took turns rating the samples using a sensory questionnaire.

[0043] In this embodiment of the invention, the following steps are taken: Appearance (30 seconds): Pick up the sample, hold the cup up to the light, and observe the hue, transparency, etc. of the sample in the cup for 30 seconds; Aroma (30 seconds): Pick up the sample, place it under your nose, and gently rotate it to smell the aroma for 30 seconds; Taste and Texture (5 seconds): Pick up the sample, take a bite (do not swallow), and feel the texture and taste of the sample for 5 seconds; Aftertaste (20 seconds): Swallow the sample, and in the next 20 seconds, quietly savor the overall feeling brought by the sample; A questionnaire is used to score the taste, texture, aftertaste, overall feeling, and preference of the sample.

[0044] In a preferred embodiment of the present invention, the comprehensive emotional experience index model in step S4 is as follows:

[0045] f(x)=ω T +b

[0046] Where f is the Integrated Emotional Experience Index (IAA), x is the feature vector space composed of the emotional dimension and eight band indices, b is the residual, and ω = (ω1, ω2, ..., ω...). d ), is the coefficient matrix.

[0047] In this embodiment of the invention, ω∈[-1,1].

[0048] In a preferred embodiment of the present invention, the specific operation of obtaining the comprehensive preference index at different evaluation stages in step S4 is as follows:

[0049] The average IAA index of each sample at each evaluation stage is taken as the feature R = (R1, R2, ..., R m Assign values ​​to it according to the weighting method:

[0050] g(x) = A T R+σ

[0051] Where g is the final comprehensive emotional experience score, σ is the residual, and A = (a1, a2, a3, ..., a... m ), is the coefficient matrix.

[0052] In this embodiment of the invention, A∈[0,1].

[0053] In a preferred embodiment of the present invention, in step S1, the recruited beer consumers consist of consumers who have not undergone professional sensory evaluation, are between 18 and 50 years old, have no taste, smell, or visual impairments, do not smoke, are regular consumers who evaluate products, and have no product dependence or allergic behavior.

[0054] In this embodiment of the invention, the participants were consumers who had not undergone professional sensory evaluation, with an average age of 18-50, non-smokers, no history of disease, and regular beer consumers (consuming at least once a month), with no product dependence or allergic behavior. Participants were allowed to participate in the experiment only after understanding the experimental content, potential risks, and signing an informed consent form.

[0055] In a preferred embodiment of the present invention, in step S2, the consumer preference questionnaire uses a nine-point preference scale.

[0056] In this embodiment of the invention, the numbers 1 to 9 represent the following meanings in sequence: 1 - extreme aversion, 2 - strong aversion, 3 - moderate aversion, 4 - slight aversion, 5 - neither like nor dislike, 6 - slight liking, 7 - moderate liking, 8 - strong liking, and 9 - extreme liking.

[0057] In a preferred embodiment of the present invention, in step S3, during the evaluation of each sample, the recruited beer-drinking consumers clean their mouths to ensure that they do not interfere with the testing of the next sample.

[0058] In this embodiment of the invention, between evaluations of each sample, the subject needs to drink water and rinse their mouth to clean their oral cavity, ensuring that it does not interfere with the testing of the next sample.

[0059] Example 1

[0060] EEG analysis was used to analyze consumer preferences for four types of beer.

[0061] This experiment evaluated four beer products and recruited 10 volunteers, including 3 women and 7 men, with consumers aged between 27 and 46 (average age 33.6 years).

[0062] 1. Experimental Procedure: Participants wore EEG caps containing TGAM chips to capture and transmit EEG data; the questionnaire was conducted online. Each participant was provided with a computer for experimental procedure prompts and questionnaire completion; a blinded test was used (codes: Sample 1, Sample 2, Sample 3, Sample 4). Participants evaluated each wine sample in four steps: observing the wine, smelling the aroma, tasting, and swallowing. EEG information was recorded during the experiment; there was a break of at least 3 minutes between each sample evaluation, during which participants were asked to drink water, eat unsalted soda crackers to cleanse their palates, and complete a preference questionnaire.

[0063] 2. Data Processing and Analysis

[0064] (1) Sensory evaluation data: Consumers rated the sensory characteristics of the four samples based on appearance, aroma, and taste, and gave an overall preference score. The scores were weighted and averaged according to the scores given by the participants, as shown in Table 1;

[0065] (2) EEG data: By capturing EEG data of different frequency bands and performing preprocessing, eight types of EEG waves and two emotional dimensions were obtained during the evaluation process. These features were used to construct a preference model, and the final preference score was given. The scores are shown in Table 1.

[0066] Table 1

[0067] Beer sample name Sensory questionnaire score EEG Preference Score Sample 1 7.6 65 Sample 2 8.2 73 Sample 3 9.5 89 Sample 4 8.7 82

[0068] Sensory questionnaires and EEG preferences are two different scoring methods, but the final results show that the rankings for different beer samples are generally consistent, indicating that the accuracy and usability of this application are good. Moreover, the scientific validity and credibility of the analysis conducted through objective means such as EEG are more easily accepted.

[0069] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present invention, and these should also be considered within the scope of protection of the present invention. These modifications and improvements will not affect the effectiveness of the implementation of the present invention or the practicality of the patent.

Claims

1. A method for testing beer preference of a consumer based on electroencephalogram analysis quantification, characterized by, Comprise the following steps: Step S1, beer drinking consumer recruitment and preference experiment; Step S2, evaluation method: the experiment analyzes and evaluates the beer preference of consumers through electroencephalogram recording analysis combined with consumer preference questionnaire evaluation; the consumer is prompted by the test standard program edited by MATLAB; Step S3, evaluation process: the consumer operates the experiment according to the screen prompt, and evaluates the samples one by one; Step S4, data processing and analysis modeling: after the electroencephalogram data is extracted and artifact is removed, the original data of eight kinds of brain waves and two emotional dimensions of concentration and relaxation are analyzed, the comprehensive emotional experience index model is established with the emotions and eight wave bands in different evaluation stages as characteristics, and the emotions that significantly distinguish different samples and different evaluation stages are studied; In the step S4, the comprehensive emotional experience index model is as follows: ； wherein f is the integrated affective experience index IAA, x is a feature vector space consisting of the emotional dimensions and the eight band indicators, b is a residual, is a coefficient matrix; In the step S4, the specific operation for obtaining the comprehensive preference index of different evaluation stages is as follows: The average of the IAA indices for each sample for each taste panel session is taken as the characteristic , which is assigned a value according to the weighting method: ； wherein g is the final overall emotional experience score, is the residual, is a coefficient matrix; In the step S3, the evaluation of each sample is from three parts of appearance, aroma and taste, and the specific operation is as follows: Appearance: hold the sample, shine light on it, and observe the color and transparency of the sample in the cup for 30 seconds; Aroma: hold the sample under the nose and rotate it gently, and smell the aroma for 30 seconds; Taste: swallow the wine sample, and taste the overall feeling brought by the sample in the next 20 seconds; The consumer scores the sensory questionnaire for the samples in turn; In the step S1, the recruited beer drinking consumers are composed of consumers who have not undergone professional sensory evaluation, the age of the consumers is between 18-50 years old, there is no taste, smell and vision obstacle, the consumer does not smoke, is a regular consumer for evaluating the product, and has no product dependence or allergic behavior; In the step S2, the consumer preference questionnaire adopts nine-point preference mark; During the evaluation of each sample, the recruited beer drinking consumer ensures that the next sample test will not be disturbed by cleaning the mouth.

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