CN107479702A - A kind of human emotion's dominance classifying identification method using EEG signals - Google Patents
A kind of human emotion's dominance classifying identification method using EEG signals Download PDFInfo
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- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
- G06F3/015—Input arrangements based on nervous system activity detection, e.g. brain waves [EEG] detection, electromyograms [EMG] detection, electrodermal response detection
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- G06F2203/01—Indexing scheme relating to G06F3/01
- G06F2203/011—Emotion or mood input determined on the basis of sensed human body parameters such as pulse, heart rate or beat, temperature of skin, facial expressions, iris, voice pitch, brain activity patterns
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Abstract
The invention discloses a kind of method that emotion dominance Classification and Identification is carried out using human body EEG signals.The a large amount of subject EEG signals of this method collection are as training data；Carry out denoising, feature extraction and characteristic standardization successively to training data；Classification is carried out to subject emotion dominance level by professional and is used as label data；Train to obtain emotion dominance artificial neural network disaggregated model using the training data after processing and label data.When carrying out emotion dominance Classification and Identification, the emotion dominance artificial neural network disaggregated model input obtained to training gathers in real time and pretreated EEG signals characteristic, uses the model to calculate the emotion dominance classification of individual.
Description
Technical field
The present invention is that a kind of human emotion's dominance knows method for distinguishing.Relate generally to computer science to it is psychologic related
Technical field.
Background technology
PAD emotion models are the important affective state spatial models of psychological educational circles（As shown in Figure 1）.The model thinks feelings
Sense represents pleasant degree with 3 pleasant degree, activity and dominance dimensions, wherein P（Pleasure-displeasure）, represent
The positive negative characteristic of individual affective state；A represents activity (Arousal-nonarousal）, represent the nervous physiology activation of individual
It is horizontal；D represents dominance (Dominance-submissiveness）, represent individual to scene and other people state of a control.Together
When specific emotion can also be represented by the value of this 3 dimensions, such as indignation coordinate be（-0.51,0.59,0.25）.
Research shows, the emotion of the mankind can be effectively explained using tri- dimensions of P, A, D.In the method, we are by feelings
Sense dominance is defined on [- 1,1] mathematical space, and it is that high dominance is horizontal to provide [0.4,1] wherein,（- 0.4,0.4）To be medium
Dominance is horizontal, and [- 1, -0.4] is that low dominance is horizontal, and the purposes of this method is to use human body EEG signals as foundation,
Judge that individual is horizontal in the emotion dominance of particular moment, so as to provided for mankind's affective state and horizontal identification refer to according to
According to.
Emotion recognition is to realize the key technology of harmonious man-machine interaction.Research from society and cognitive psychology shows
Under related environmental stimuli, emotion can rapidly, easily, automatically or even unconsciously arouse.Affection computation initially by
What the Picard professors of Massachusetts Institute Technology proposed in 1997.The target of affection computation is to confer to computer and perceives, manages
The ability for solving and showing emotion, so as to be exchanged more actively, friendly, excellent in voice and affectionly with people.Then, affection computation draws rapidly
Artificial intelligence and the interest of computer realm expert are played, and as a brand-new, full of hope research field in recent years.
The it is proposed of affection computation is on the one hand due to the requirement of man-machine interaction concordance, it is desirable to which computer is as people one with developing rapidly
Sample not only possesses the ability listening, say, seeing, reading, and it will be appreciated that with expressing the moods such as pleasure, anger, sorrow, happiness;On the other hand it is also
Based on the strong psychology for calculating doctrine, it is desirable to calculating is extended to the inner world of people.After affection computation proposes, based on facial table
Feelings, voice, the emotion recognition of posture and physiological signal are being widely studied.
EEG signals（Electroencephalogram, EEG）Be it is a kind of using electrophysiological index record brain activity
Method, it is that the brain postsynaptic potential that a large amount of neurons synchronously occur in activity is formed after summation.It records brain
Electric wave change when movable, it is overall reflection of the bioelectrical activity in cerebral cortex or scalp surface of cranial nerve cell.Brain electricity
Signal simultaneously and most widely used in the world and obtain generally accepted leading psychological test index more.By for EEG signals
Analysis, can be to the activity of human brain（Including affective activity）Effectively follow the trail of and measure.
In existing method, researcher by the analysis for EEG signals, discloses human cognitive activity
A large amount of universal laws.Cognitive psychology and intelligent people will effectively be promoted by carrying out emotion dominance Classification and Identification using EEG signals
The development of machine interaction research.
The content of the invention
Present disclosure is to provide a kind of human emotion's dominance classifying identification method using EEG signals.
In order to obtain above-mentioned purpose, using following technical scheme.
1, collection human body electroencephalogram's data establish emotion dominance Classification and Identification model, and this method mainly comprises the following steps.
S1：Using brain wave acquisition equipment collection Different Individual in the EEG signals of different emotions dominance state Xia 32,
As shown in Figure 2, wherein the point of grey is brain wave acquisition access points used in this method for the selection of brain wave acquisition point.
S2：Denoising is carried out to the EEG signals collected, removes the noise data brought due to signal interference.
S3：The numerical characteristics of extraction EEG signals are calculated, it is 2 seconds to calculate time window length, and principal character includes：
The EEG signals feature data types of table 1.
S4：All characteristic values are standardized, so as to improve the accuracy of model training, avoided plan
Close.
S5：The emotion dominance level of subject is commented according to the expression and voice messaging of subject by 3 professionals
Valency, it is horizontal that evaluation result is divided into high, medium and low 3 class dominance.
S6：The standardized feature data obtained using in S4 are used as the dominance evaluating data obtained in training data and S5
As label data, artificial neural network disaggregated model is trained, is known so as to obtain the emotion dominance classification based on EEG signals
Other model.And the model is subjected to parametrization preservation.
, after emotion dominance identification model is obtained, when needing to carry out emotion dominance prediction/detection, according to following
Step carries out emotion dominance detection.
S1：Tested 32 EEG signals of personnel as shown in Figure 2 of collection in real time.
S2：Denoising is carried out to EEG signals.
S3：Extract EEG signals characteristic as shown in table 1.
S4：Data normalization processing is carried out to characteristic value.
S4：Characteristic value input after standardization is trained to obtained brain electricity emotion dominance Classification and Identification mould before
Type, it is horizontal that emotion dominance of the tested individual under current state is calculated by the model.
The main feature of the present invention includes.
（1）By early-stage Study, in 89 data characteristicses of EEG signals, Feature Selection is used, it is determined that use
In 21 best signal characteristics of emotion dominance Classification and Identification effect, so as to greatly reduce computation complexity, improve
Computational efficiency.
（2）Using longer time window, so as to reduce the complexity of calculating, the validity of identification is improved well
And accuracy.
（3）Using the sorting technique in pattern-recognition, emotion dominance is divided into 3 classes, only judges the classification of dominance, and
Dominance concrete numerical value is not calculated, more conforms to practical application needs.
Brief description of the drawings
Fig. 1 is PAD affective state spatial model schematic diagrames.
Fig. 2 is eeg signal acquisition point schematic diagram.
Fig. 3 is the EEG signals schematic diagram data after denoising（10 passages）.
Fig. 4 is EEG signals characteristic schematic diagram（10 features）.
Embodiment
The present invention is further elaborated with specific embodiment below in conjunction with the accompanying drawings.
1. emotion dominance Classification and Identification method for establishing model, this method is mainly by advance before classification is predicted
The EEG signals being largely tested are gathered, carry out denoising and data prediction, and use the data training emotion dominance people of collection
Artificial neural networks disaggregated model, used with providing follow-up real-time estimate identification.
（1-1）Eeg signal acquisition
Being tested is needed to be recalled according to itself early stage, and oneself is told about in environment is gathered and remembers experience the most deep, it is proposed that subject
Telling about includes happiness, sad, indignation, the typical affective state event such as fear.It is public using U.S. Biopac during telling about
The polygraph MP150 collection subject EEG signals of department's production.Using 32 signalling channels in brain electricity cap during collection,
Be divided into ' Fp1', ' AF3', ' F3', ' F7', ' FC5', ' FC1', ' C3', ' T7', ' CP5', ' CP1', ' P3', '
P7', 'PO3', 'O1', 'Oz', 'Pz', 'Fp2', 'AF4', 'Fz', 'F4', 'F8', 'FC6', 'FC2', '
Cz', 'C4', 'T8', 'CP6', 'CP2', 'P4', 'P8', 'PO4', 'O2’.Particular location is as shown in Figure 2.Instruction
Practice data acquisition amount to be no less than 120 minutes.Accumulative collection subject quantity is no less than 20 person-times.
（1-2）EEG signals denoising
In the detection and processing procedure of EEG signals, the influence of the interference signals such as power frequency noise is seriously received, in order to effective
Extraction and signal Analysis in active ingredient, using the currently independent component analysis of relative maturity（ICA）Method, to original
Beginning EEG signals are pre-processed, so as to obtain that the signal of true brain electrical feature can be reflected.EEG signals after denoising
As shown in Figure 3（Because picture scope limits, the EEG signals schematic diagram of 10 passages is only depicted in schematic diagram 3, it is actual
Gather as 32 passages）.
（1-3）Extraction subject signal characteristic
Signal characteristic value as shown in table 1 is calculated for the EEG signals collected.The EEG signals characteristic being calculated
As shown in Figure 4（Because picture scope limits, 10 brain electrical characteristic data schematic diagrames, actual meter are only depicted in schematic diagram 4
Obtained signal characteristic is 21）.
（1-4）Data normalization is carried out to the EEG signals characteristic value being calculated
In order to avoid due to caused by characteristic value difference in size the problems such as over-fitting, using normal data method for normalizing
It is right（1-3）In the characteristic that is calculated be standardized.Initial data is normalized into average as 0, variance 1 by this method
Data, normalization formula are as follows：
Wherein, μ and σ is respectively the average and variance of characteristic.
（1-5）Emotion dominance evaluating data gathers
By 3 trained personnel（Psychology specialty）Viewing subject expression video, listen to subject language expression, to it is different when
Carve subject emotion dominance to be scored, scoring scope is [- 1,1], wherein -1 represents negative sense sharpest edges degree, 1 represents positive
Sharpest edges degree.3 people are taken to score average mark as the moment dominance evaluation of estimate.Emotion dominance evaluation of estimate is classified,
It is that high dominance is horizontal wherein to provide [0.4,1],（- 0.4,0.4）Horizontal for medium dominance, [- 1, -0.4] is low dominance
It is horizontal.
（1-6）Train personal emotion dominance identification model
Use（1-4）In the standardized feature value that is calculated as training data,（1-5）In emotion dominance evaluation of estimate make
For label data, standard intraocular's neural network classification model training is carried out.In a model, it is 21 to set input layer number, hidden
Node layer number is 28, and output layer nodes are 3, learning rate 0.01, and final training obtains emotion dominance Classification and Identification model.
All model parameters trained are preserved, as follow-up real-time grading computation model.
2, carry out real-time emotion dominance classified calculating method.This method mainly by gathering and calculating dominance phase in real time
EEG signals characteristic value is closed, the emotion dominance classified calculating model established before use calculates the emotion dominance at the moment
Type.
（2-1）Using with（1-1）Identical equipment and real-time 32 EEG signals of frequency collection individual.Acquisition channel is for example attached
Shown in Fig. 2.
（2-2）Using with（1-2）Identical method carries out EEG signals denoising.
（2-3）Calculate and extract EEG signals feature as shown in table 1.
（2-4）Using（1-4）In method to signal characteristic carry out data normalization processing, the signal after being standardized
Characteristic value.
（2-5）Read（1-6）It is middle to train obtained emotion dominance Classification and Identification model, by the signal characteristic after standardization
Moment individual's emotion dominance classification value is calculated using disaggregated model as input in value.
In existing experiment, the classifying quality of this method has reached preferable level, by verifying, this method
Compressive classification accuracy is 73.8%, preferably can judge human emotion's dominance-types using EEG signals.
Claims (2)
1. a kind of human emotion's dominance Classification and Identification method for establishing model using EEG signals, the method is characterized in that its
Comprise the following steps；
S1：The multi-path physiology signal picker produced using Biopac companies of the U.S., multiple subjects are gathered with 512Hz sample frequencys
The 32 passage EEG signals under multiple affective states；
S2：The eeg data collected is pre-processed using independent component analysis method, removes noise jamming；
S3：Using pretreated EEG signals, 21 related baseband signal characteristics of dominance identification, brain telecommunications are calculated
It is 2 seconds, it is necessary to which the signal characteristic calculated includes number to calculate time window length：EEG signals kurtosis, EEG signals second differnce
Maximum, the EEG signals coefficient of variation, the EEG signals degree of bias, EEG signals first-order difference maximum, EEG signals small echo are accurate
Average, EEG signals small echo Precision criterion is poor, AF3, F7, FC5, FC1, C3, T7, PO3, O1, F4, F8, FC6,
FC2, P4, PO4 brain electric channel autoregressive coefficient；
S4：Data normalization processing is carried out to 21 features being calculated, as model training data；
S5：It is continuous to subject progress emotion dominance according to the voice and facial expression state of subject by 3 professional and technical personnel
Evaluation, dominance evaluation of estimate corresponding to eeg data is obtained, according to evaluating data, dominance-types are divided into high, medium and low totally three
Class, label data is used as using classification results；
S6：Artificial neural network disaggregated model training is carried out using training data and label data, it is artificial to obtain emotion dominance
Neural network classification identification model.
2. the emotion dominance artificial neural network Classification and Identification model of the requirement of usage right 1 is carried out when emotion dominance is classified
Method, the method is characterized in that it mainly comprises the following steps：
S1：Need the individual brain for carrying out dominance classification electric using with identical brain electricity sample frequency in right 1 and equipment collection
Signal；
S2：EEG signals denoising is carried out using independent component analysis method；
S3：Extraction and 21 character numerical values of identical EEG signals in right 1；
S4：The characteristic obtained to extraction is standardized；
S5：Characteristic is inputted and requires that method trains obtained emotion dominance artificial neural network Classification and Identification by power 1
Model, the corresponding emotion dominance classification value for being detected individual is calculated by the model.
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Cited By (9)
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| CN109009096A (en) * | 2018-07-17 | 2018-12-18 | 泉州装备制造研究所 | The system and method that a kind of pair of films and television programs objectively evaluate online |
| CN109656366A (en) * | 2018-12-19 | 2019-04-19 | 电子科技大学中山学院 | Emotional state identification method and device, computer equipment and storage medium |
| CN109800804A (en) * | 2019-01-10 | 2019-05-24 | 华南理工大学 | A kind of method and system realizing the susceptible sense of image and independently converting |
| CN111134666A (en) * | 2020-01-09 | 2020-05-12 | 中国科学院软件研究所 | Emotion recognition method of multi-channel electroencephalogram data and electronic device |
| CN111310783A (en) * | 2020-01-05 | 2020-06-19 | 天津大学 | Speech state detection method based on electroencephalogram micro-state features and neural network model |
| CN111860463A (en) * | 2020-08-07 | 2020-10-30 | 北京师范大学 | Emotion identification method based on joint norm |
| CN112806995A (en) * | 2021-02-01 | 2021-05-18 | 首都师范大学 | Psychological stress classification and assessment method and device |
| CN113749656A (en) * | 2021-08-20 | 2021-12-07 | 杭州回车电子科技有限公司 | Emotion identification method and device based on multi-dimensional physiological signals |
| CN118303845A (en) * | 2024-04-30 | 2024-07-09 | 四川新源生物电子科技有限公司 | Anesthesia depth evaluation method, anesthesia depth evaluation system and storage medium |
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| CN109009096A (en) * | 2018-07-17 | 2018-12-18 | 泉州装备制造研究所 | The system and method that a kind of pair of films and television programs objectively evaluate online |
| CN109656366A (en) * | 2018-12-19 | 2019-04-19 | 电子科技大学中山学院 | Emotional state identification method and device, computer equipment and storage medium |
| CN109800804A (en) * | 2019-01-10 | 2019-05-24 | 华南理工大学 | A kind of method and system realizing the susceptible sense of image and independently converting |
| CN111310783A (en) * | 2020-01-05 | 2020-06-19 | 天津大学 | Speech state detection method based on electroencephalogram micro-state features and neural network model |
| CN111134666A (en) * | 2020-01-09 | 2020-05-12 | 中国科学院软件研究所 | Emotion recognition method of multi-channel electroencephalogram data and electronic device |
| CN111860463A (en) * | 2020-08-07 | 2020-10-30 | 北京师范大学 | Emotion identification method based on joint norm |
| CN111860463B (en) * | 2020-08-07 | 2024-02-02 | 北京师范大学 | Emotion recognition method based on joint norm |
| CN112806995A (en) * | 2021-02-01 | 2021-05-18 | 首都师范大学 | Psychological stress classification and assessment method and device |
| CN112806995B (en) * | 2021-02-01 | 2023-02-17 | 首都师范大学 | Psychological stress classification and assessment method and device |
| CN113749656A (en) * | 2021-08-20 | 2021-12-07 | 杭州回车电子科技有限公司 | Emotion identification method and device based on multi-dimensional physiological signals |
| CN113749656B (en) * | 2021-08-20 | 2023-12-26 | 杭州回车电子科技有限公司 | Emotion recognition method and device based on multidimensional physiological signals |
| CN118303845A (en) * | 2024-04-30 | 2024-07-09 | 四川新源生物电子科技有限公司 | Anesthesia depth evaluation method, anesthesia depth evaluation system and storage medium |
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