CN108836326A - A method of organoleptic substances classification is carried out based on smell brain wave and wavelet packet - Google Patents
A method of organoleptic substances classification is carried out based on smell brain wave and wavelet packet Download PDFInfo
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- CN108836326A CN108836326A CN201810320415.5A CN201810320415A CN108836326A CN 108836326 A CN108836326 A CN 108836326A CN 201810320415 A CN201810320415 A CN 201810320415A CN 108836326 A CN108836326 A CN 108836326A
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- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/369—Electroencephalography [EEG]
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Abstract
The invention discloses a kind of methods for carrying out organoleptic substances classification based on smell brain wave and wavelet packet, include the following steps：S1, the acquisition of electroencephalogram spectrum information is carried out to candidate using brain-computer interface system, that is, electroencephalograph；S2, acquired electroencephalogram modal data is pre-processed；S3, feature extraction is carried out to pretreated spectrum data is completed based on wavelet package transforms, using resulting wavelet packet variance as characteristic value；S4, pattern-recognition is carried out using random forest (RF), based on genetic algorithm optimization support vector machines (GA-SVM).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 organoleptic substances sorting technique fields, and in particular to one kind is carried out based on smell brain wave and wavelet packet
The method of organoleptic substances classification.
Background technique
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, ingredient and technique in entire appraisement system
It is whole, reduce in the appraisals such as cost, quality guarantee and product optimization 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 instrument 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 habit 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.
Summary of the invention
Based on above-mentioned analysis, organoleptic substances classification is carried out based on smell brain wave and wavelet packet the present invention provides a kind of
Method.
To achieve the above object, the technical scheme adopted by the invention is as follows：
A method of organoleptic substances classification is carried out based on smell brain wave and wavelet packet, is included the following steps：
S1, the acquisition of electroencephalogram spectrum information is carried out to candidate using brain-computer interface system, that is, electroencephalograph；
S2, acquired electroencephalogram modal data is pre-processed；
S3, feature extraction is carried out to pretreated spectrum data is completed based on wavelet package transforms, by resulting wavelet packet side
Difference is used as characteristic value；
S4, pattern-recognition is carried out using random forest (RF), based on genetic algorithm optimization support vector machines (GA-SVM).
Preferably, the pretreatment of the eeg data, which includes at least, deletes bad block processing；Filter out 50Hz Hz noise；Data
Superposed average processing.
Preferably, the position of electrode is according to international standard lead 10-20 system rest, select Fp1 relevant to smell,
Electroencephalogram modal data corresponding to F3, F7, Fz electrode.
Preferably, the step S4 be based on random forest classify, when in random forest include 20,55,64-70,75,
When 85-100 decision tree, for accuracy rate up to 100%, classifying quality is ideal.
Preferably, when the support vector machines in the step S4 based on genetic algorithm finds optimal parameter, as penalty factor c
It is 6.2466, when kernel functional parameter g is 0.1111,5 times of cross validation accuracys rate are up to 92.86%.
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.
Detailed description of the invention
Fig. 1 is the flow chart of data processing figure in the embodiment of the present invention.
Fig. 2 is 9 groups of data investigation effect pictures.
Fig. 3 is the influence schematic diagram that decision sets to classification performance in random forest.
Fig. 4 is the parameter optimization schematic diagram of the support vector machines based on genetic algorithm.
Specific embodiment
In order to which objects and advantages of the present invention are 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：Select 4 kinds of different samples (fruit juice, white wine, mature vinegar, beer), concentration and raw material such as the following table 1
It is shown：
14 kinds of table different samples
In terms of smell electroencephalogramsignal signal analyzing, used EEG signals are laboratories from data are adopted, and collect original brain
After electric signal, progress brain electricity pretreatment (including deletes bad block, filters out 50Hz Hz noise, the averagely equal step of data investigation first
Suddenly)；Then wavelet packet is carried out to it first changes the signal characteristic for acquiring wavelet packet variance as extraction；In view of the electrode packet of acquisition
Electrode screening has therefore been carried out containing different regions；The characteristic value of selection is finally sent to corresponding classifier, is obtained final
Classification accuracy.Flow chart of data processing figure of the present invention is as shown in Figure 1.
This experiment places brain using NCERP series electroencephalogram and evoked potentuial measuring system, according to the world 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 years old, dextro manuality, without any respiratory disorder, mental disease and
The subject of chronic disease.Allow subject in comfortable state, the EEG signals of observation subject, are in steady to its EEG signals in real time
When determining state, olfactory stimulation gesture is issued, experimenter randomly selects one from 4 kinds of samples and is gently placed on subject underthe nose
At 1-2cm and the stimulation of 2s is kept, makes subject that can sufficiently receive 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, it is tested hyperphoria with fixed eyeballs cover, earplug, so that it is reduced the movements such as blink, eyeball movement, and it is comfortable to be tested holding
Posture, no any limb action occur, and 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 rest, 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 includes that (1) filters out 50Hz Hz noise, reduces it by 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, by 9 groups of superposed averages of data.
3. the selection of electrode
The position of electrode is according to international standard lead 10-20 system rest, 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 to 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
Use wavelet packet variance as characteristic value after the data obtained after aforesaid operations are carried out wavelet package transforms.In this experiment
It selects db6 small echo to carry out 3 layers of WAVELET PACKET DECOMPOSITION, obtains 8 wavelet packet coefficients, wavelet packet variance is as in EEG signals research
Brain electrical feature.
5. mode identification method
In this experiment, using RF random forest and the mode identification method pair based on genetic algorithm optimization support vector machines
The data obtained after aforesaid operations carry out Classification and Identification, and classification results are respectively 96.16,92.31%.
Be illustrated in figure 3 decision in random forest and set influence to classification performance, when include 20 in random forest, 55,
When 64-70,75,85-100 decision tree, for accuracy rate up to 100%, classifying quality is ideal.It is illustrated in figure 4 and is calculated based on heredity
The support vector machines of method finds the process of optimal parameter.When penalty factor c is 6.2466, and kernel functional parameter g is 0.1111,5
Times cross validation accuracy rate is up to 92.86%.
Conclusion：It can thus be seen that 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, ingredient and technique
The appraisals such as cost, quality guarantee and product optimization play a significant role
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 (5)
1. a kind of method for carrying out organoleptic substances classification based on smell brain wave and wavelet packet, which is characterized in that including walking as follows
Suddenly：
S1, the acquisition of electroencephalogram spectrum information is carried out to candidate using brain-computer interface system, that is, electroencephalograph；
S2, acquired electroencephalogram modal data is pre-processed；
S3, feature extraction is carried out to pretreated spectrum data is completed based on wavelet package transforms, resulting wavelet packet variance is made
It is characterized value；
S4, pattern-recognition is carried out using random forest (RF), based on genetic algorithm optimization support vector machines (GA-SVM).
2. a kind of method for carrying out organoleptic substances classification based on smell brain wave and wavelet packet as described in claim 1, special
Sign is that the pretreatment of the eeg data, which includes at least, deletes bad block processing；Filter out 50Hz Hz noise；Data investigation is average
Processing.
3. a kind of method for carrying out organoleptic substances classification based on smell brain wave and wavelet packet as described in claim 1, special
Sign is that the position of electrode selects Fp1, F3, F7, Fz electricity relevant to smell according to international standard lead 10-20 system rest
Extremely corresponding electroencephalogram modal data.
4. a kind of method for carrying out organoleptic substances classification based on smell brain wave and wavelet packet as described in claim 1, special
Sign is, the step S4 is based on random forest and classifies, when including 20,55,64-70,75,85-100 in random forest
When decision tree, for accuracy rate up to 100%, classifying quality is ideal.
5. a kind of method for carrying out organoleptic substances classification based on smell brain wave and wavelet packet as described in claim 1, special
Sign is, when the support vector machines in the step S4 based on genetic algorithm finds optimal parameter, when penalty factor c is
When 6.2466, kernel functional parameter g are 0.1111,5 times of cross validation accuracys rate are up to 92.86%.
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