CN108542385A - A method of carrying out sense organ flavor substance classification using smell brain wave - Google Patents
A method of carrying out sense organ flavor substance classification using smell brain wave Download PDFInfo
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- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
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Abstract
The invention discloses a kind of methods carrying out sense organ flavor substance classification using smell brain wave, include the following steps：S1, electroencephalogram spectrum information acquisition is carried out to candidate using brain machine interface system, that is, electroencephalograph；S2, the electroencephalogram spectrum information of gained is pre-processed；S3, based on wavelet package transforms feature extraction is carried out to completing pretreated electroencephalogram profile information；S4, using K mean values, random forest (RF) and based on genetic algorithm optimization support vector machines (GA SVM) mode identification method, to characteristic set carry out model prediction.The present invention really restores the physiology and appearance of human brain information process of candidate during judging, this is extremely important in clinical medicine and cognitive science field, can be pervasive in the sensory evaluation of substance, make more succinct sensory evaluation process, more normative, preciseness and science.
Description
Technical field
The present invention relates to sense organ flavor substance sorting technique fields, and in particular to a kind of to carry out sense organ using smell brain wave
The method of flavor substance classification.
Background technology
Sensory evaluation is the intersection for collecting the subjects such as modern physiology, psychology, statistics and gradually developing, growing up
Frontier branch of science, sense organ professional is in the tune to new product development, basic research, dispensing and technique in entire appraisement system
It is whole, reduce in the appraisals such as cost, quality guarantee and products perfection and play decisive role.With the hair of science and technology
Exhibition, more and more precision instruments for analyzing flavor substance come into being, can not be complete but rely solely on Instrumental Analysis
The truly feels of full response human body, therefore human body evaluation still occupies an important position.During sensory evaluation, need
Personnel are participated in person in the sensory utilization such as vision, smell, sense of taste, are easy by daily life custom and diet feelings
The interference of condition, the sensory evaluation result provided pass through the thinking of brain, are doped with personal subjective factor, therefore have centainly
Subjectivity, poor repeatability.
EEG signals are a kind of basic bio signals of human body, are the effective means for recording brain activity, can be objective, true
Reflect human body physiological state on the spot, can be used for the auxiliary diagnosis of mental disease, including Parkinson's disease, Alzheimer disease, Weir
Inferior disease, epilepsy, brain tumor and schizophrenia etc..As the objective indicator of nervous function and physiological evaluation, by observing brain
Electric signal (EEG) can help us directly to understand electrophysiological change related with Xin Li, physiological status, by Novel presentation
Observation come determine may generation lesion.
Invention content
Based on above-mentioned analysis, the present invention provides a kind of sides carrying out sense organ flavor substance classification using smell brain wave
Method.
To achieve the above object, the technical solution that the present invention takes is：
A method of sense organ flavor substance classification being carried out using smell brain wave, is included the following steps：
S1, electroencephalogram spectrum information acquisition is carried out to candidate using brain-computer interface system, that is, electroencephalograph；
S2, the electroencephalogram spectrum information of gained is pre-processed；
S3, based on wavelet package transforms feature extraction is carried out to completing pretreated electroencephalogram profile information；
S4, using K mean values, random forest (RF) and based on genetic algorithm optimization support vector machines (GA-SVM) pattern
Recognition methods carries out model prediction to characteristic set.
Preferably, the pretreatment of the eeg data, which includes at least, deletes bad block processing；Filter out 50Hz Hz noises；Data
Superposed average processing.
Preferably, the position of electrode is according to international standard lead 10-20 system rests, selection and the relevant Fp1 of smell,
Electroencephalogram modal data corresponding to F3, F7, Fz electrode.
The present invention really restores the physiology and appearance of human brain information process of candidate during judging, this is in clinic
Medicine and cognitive science field are extremely important, can be pervasive in the sensory evaluation of substance, make sensory evaluation process
More succinct, more normative, preciseness and science.
Description of the drawings
Fig. 1 is the flow chart of data processing figure in the embodiment of the present invention.
Fig. 2 is K mean cluster result.
Fig. 3 is the influence that decision sets to classification performance in random forest.
Fig. 4 is the parameter optimization of the support vector machines based on genetic algorithm.
Specific implementation mode
In order to make objects and advantages of the present invention be more clearly understood, the present invention is carried out with reference to embodiments further
It is described in detail.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not used to limit this hair
It is bright.
Embodiment
Material and method：The beer of 3 kinds of different brands (Qingdao, blue zone, Harbin) is selected, alcohol concentration and raw material are such as
Shown in the following table 1：
The beer sample of 13 kinds of different brands of table
In terms of smell electroencephalogramsignal signal analyzing, used EEG signals are that data are adopted in laboratory certainly, collect original brain
After electric signal, brain electricity pretreatment is carried out first and (including deletes bad block, filters out 50Hz Hz noises, the averagely equal step of data investigation
Suddenly)；Then it carries out wavelet packet to it and first changes to acquire wavelet packet variance, as the signal characteristic of extraction；In view of the electrode of acquisition
Including therefore different regions has carried out electrode screening；The characteristic value of selection is finally sent to corresponding grader, is obtained most
Whole classification accuracy.
This experiment places brain using NCERP series electroencephalogram and evoked potentuial measuring system, according to the worlds 10-20 brain electric system
Electric frequency acquisition DC-50Hz, sample frequency 256Hz can at most obtain 36 channel datas (this experiment obtains 24 channel datas),
The bioelectrical signals of subject Different brain region are respectively represented.
For the completion experiment that subject can be concentrated one's energy, acquisition EEG signals need stringent experimental situation, such as
It is quiet and to keep faint light.
1. signal acquisition experiment flow
This experiment in, select the age between 23-26 Sui, dextro manuality, without any respiratory disorder, mental disease and
The subject of chronic disease.It allows subject to be in comfortable state, in real time the EEG signals of observation subject, waits for that its EEG signals is in steady
When determining state, olfactory stimulation gesture is sent out, experimenter randomly selects one from 3 kinds of samples and is gently placed on subject underthe nose
At 1-2cm and the stimulation of 2s is kept, so that subject is fully received olfactory stimulation, EEG signals make corresponding response.Once
After experiment terminates, laboratory windowing ventilation is tested abundant rest 1-2min and repeats above-mentioned experimental implementation later.
It should be noted that following points for attention during experiment acquisition：
Before acquisition, subject keeps clearheaded, and cleans hair with neutrality shampoo before starting eeg signal acquisition,
In order to avoid making to make wave distortion due to scalp resistance is excessive because grease is excessively high.Meanwhile experiment content simply is introduced to subject, it gets across
This experiment is not damaged no pain, is eliminated because its state of mind influences the influence of brain wave acquisition result.
In acquisition, subject hyperphoria with fixed eyeballs cover, earplug make it reduce the actions such as blink, eyeball movement, and it is comfortable to be tested holding
Posture, no any limb action occur, prevent from being mixed with excessive myoelectricity in brain electricity.Meanwhile experimenter controls experiment and carries out
Time, prevent subject occur fatigue or boredom.During trial interval is rested, actively linked up with subject, it is ensured that quilt
Examination keeps pleasant state.
2. the pretreatment of eeg data
Since collected EEG signals mix a variety of brains electricity (evoked brain potential, artefact, spontaneous brain electricity, noise etc.), so
Before the identification for carrying out smell brain electricity, needs to pre-process original EEG signals, thus obtain useful EEG signals.
The pretreatment of eeg data filters out 50Hz Hz noises including (1), it is made to reduce pollution of the noise background to signal,
Improve signal-to-noise ratio, retains the authenticity of original signal；(2) data investigation is average, since EEG signals are a kind of non-linear non-flat
Steady complex physiologic EEG signals can be offset irregular spontaneous EEG signals by superposed average, regular to lure
Hair EEG signals are enhanced, and realize the visualization of EEG signals time-domain information.In this experiment, data be not superimposed, 4 groups
Data investigation is average, 5 groups of data investigations are average, 6 groups of data investigations are average and 7 groups of data investigations are average.
3. the selection of electrode
The position of electrode is according to international standard lead 10-20 system rests, as shown in table 2.It, can in smell and sense of taste memory
To observe the interaction between prefrontal and marginal convolution.During carrying out long-term odor identification, socket of the eye frontal lobe region and bilateral
Cortex of temporal lobe is activated.In short term odor identification, right side temporal lobe is activated.In this experiment, it selects relevant with smell
Fp1, F3, F7, Fz electrode are analyzed.
2 ten-twenty electrode system electrode of table matches table name
4. the feature extraction of EEG signals
Either signal detection classification or identifying processing, are all based on the feature extraction to signal.There is representation signal special
The characteristic quantity of sign could be classified and identified to a large amount of signal.The EEG signals of research are also such.After aforesaid operations
Obtained data use wavelet packet variance as characteristic value after carrying out wavelet package transforms.Db6 small echos are selected to carry out in this experiment 3 layers small
Wave packet decomposes, and obtains 8 wavelet packet coefficients, wavelet packet variance is as the brain electrical feature in EEG signals research.
5. mode identification method
In this experiment, using K mean cluster, RF random forests and pattern based on genetic algorithm optimization support vector machines
Recognition methods carries out Classification and Identification to the data obtained after aforesaid operations, and classification results are as shown in table 3.
The pattern-recognition accuracy rate of 33 kinds of brand beers of table
Since K mean cluster depends on the selection of initial value, each cluster result is different, and accuracy is not yet
Together.Therefore it carries out 20 clusterings and is averaged.It is illustrated in figure 2 the classification accuracy of 6 groups of data investigation mean times, wherein
Red represents the first kind, and blue represents the second class, and green represents third class.
It is illustrated in figure 3 decision in random forest and sets influence to classification performance, when including 50-70 in random forest
Decision tree, for accuracy rate up to 100%, classifying quality is ideal.
It is illustrated in figure 4 the process that the support vector machines based on genetic algorithm finds optimal parameter.When penalty factor c is
When 6.6561, kernel functional parameter g are 0.057507,5 times of cross validation rate of accuracy reached 83.33%.
Conclusion：(1) by after data investigation average treatment, the classification accuracy of three kinds of algorithms significantly improves, and 7 groups of numbers
After superposed average, using random forests algorithm accuracy up to 100%；
(2) as can be seen from Table 3, the method based on smell brain wave can classify the beer of different brands, because
This this method can be pervasive in the sensory evaluation of substance, adjustment, reduction to new product development, basic research, dispensing and technique
The appraisals such as cost, quality guarantee and products perfection play an important roll.
The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, without departing from the principle of the present invention, it can also make several improvements and retouch, these improvements and modifications are also answered
It is considered as protection scope of the present invention.
Claims (3)
1. a kind of method carrying out sense organ flavor substance classification using smell brain wave, which is characterized in that include the following steps：
S1, electroencephalogram spectrum information acquisition is carried out to candidate using brain-computer interface system, that is, electroencephalograph；
S2, the electroencephalogram spectrum information of gained is pre-processed；
S3, based on wavelet package transforms feature extraction is carried out to completing pretreated electroencephalogram profile information；
S4, using K mean values, random forest (RF) and based on genetic algorithm optimization support vector machines (GA-SVM) pattern-recognition
Method carries out model prediction to characteristic set.
2. a kind of method carrying out sense organ flavor substance classification using smell brain wave as described in claim 1, feature exist
In the pretreatment of the eeg data, which includes at least, deletes bad block processing；Filter out 50Hz Hz noises；Data investigation is averagely located
Reason.
3. a kind of method carrying out sense organ flavor substance classification using smell brain wave as described in claim 1, feature exist
In the position of electrode is according to international standard lead 10-20 system rests, selection and relevant Fp1, F3, F7, Fz electrode institute of smell
Corresponding electroencephalogram modal data.
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| CN110411955A (en) * | 2019-07-15 | 2019-11-05 | 中山大学中山眼科中心 | A kind of artificial intelligence training system based on characterization of molecules predicting of substance color smell |
| CN111475936A (en) * | 2020-04-03 | 2020-07-31 | 东北电力大学 | Taste perception model-based taste recognition method |
| CN112493998A (en) * | 2020-12-09 | 2021-03-16 | 北京意图科技有限公司 | Olfactory sensory evaluation method and system |
| CN112587136A (en) * | 2020-12-09 | 2021-04-02 | 北京意图科技有限公司 | Taste sensory evaluation method and system |
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Application publication date: 20180918 |