CN113208633A - Emotion recognition method and system based on EEG brain waves - Google Patents
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Abstract
The invention discloses an emotion recognition method and system based on EEG brain waves, wherein the method comprises the steps that EEG signal acquisition equipment acquires EEG signals of a user, and the EEG signals are transmitted to a data analysis system after being preprocessed; the data analysis system extracts the features of the EEG signal and transmits the features to the judgment and identification system; the judgment and identification system identifies the emotional state of the user through a Light gbm algorithm. The method realizes the intellectualization of the brainwave emotional state detection, improves the detection accuracy, reduces the detection time and widens the application scene of the detection.
Description
Technical Field
The invention relates to the technical field of EEG signal identification, in particular to an emotion identification method and system based on EEG brain waves.
Background
At present, automatic equipment and a method for intelligently analyzing electroencephalogram signals to judge individual emotional states do not exist in the market and clinically. Besides the traditional manual questionnaire survey form, the commonly used emotion detection method also acquires electroencephalogram physiological signals by wearing electroencephalogram acquisition equipment with large volume and complicated operation, and performs emotion detection by assisting with an image detection algorithm. The methods are complex to operate, low in efficiency and large in error, and meanwhile, the application scene is limited due to the fact that the size of the detection equipment is large.
Therefore, how to reduce the recognition time and improve the recognition accuracy is an urgent problem to be solved.
Disclosure of Invention
The invention aims to provide an EEG brain wave-based emotion recognition method and system to intelligently analyze an EEG signal so as to judge an emotion state, improve recognition accuracy and reduce recognition time.
In order to solve the technical problem, the invention provides an emotion recognition method based on an EEG brain wave, which comprises the following steps:
the EEG signal acquisition equipment acquires an EEG signal of a user, and the EEG signal is transmitted to the data analysis system after being preprocessed;
the data analysis system extracts the features of the EEG signal and transmits the features to the judgment and identification system;
the judgment and identification system identifies the emotional state of the user through a Light gbm algorithm.
Preferably, the preprocessing comprises power frequency filtering and band-pass filtering.
Preferably, after the preprocessing step, the method further comprises performing a data normalization operation on the EEG signals.
Preferably, the features of the EEG signal include time domain features, frequency domain features, time-frequency features, and non-linear features of the EEGB signal.
Preferably, the time domain features include mean, variance, standard deviation and first order difference features; the frequency domain features include the sum of the band power spectral energies, the band power spectral energy maximum.
Preferably, the time-frequency characteristics are obtained by short-time Fourier transform (STFT) and wavelet transform.
Preferably, the nonlinear features are correlation dimension, approximate entropy, fractal dimension and lyapunov exponent feature.
Preferably, the EEG signal acquisition device acquires the EEG signals of the user by using five channels, and the data analysis system performs feature extraction on the EEG signals of each channel respectively.
Preferably, the Light gbm algorithm is used for training a classification model and outputting emotion classification recognition results.
The invention also provides an emotion recognition system based on the EEG brain waves, which is used for realizing the method and comprises the following steps:
the EEG signal acquisition equipment is used for acquiring an EEG signal of a user, preprocessing the EEG signal and transmitting the processed EEG signal to the data analysis system;
the data analysis system is used for extracting the features of the EEG signal and transmitting the features to the judgment and identification system;
and the judgment and identification system is used for identifying the emotional state of the user through the Light gbm algorithm.
According to the emotion recognition method and system based on the EEG brain waves, provided by the invention, EEG signals of frontal lobe electrodes of users are collected through EEG signal collection equipment, characteristic values are extracted, and emotions are classified by adopting a Lightgbm machine learning model so as to automatically detect emotion states. The method and the device can intelligently judge the emotional state of the user, improve the analysis efficiency and accuracy, widen the application scene of detection, improve the identification accuracy and reduce the identification time.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a flowchart of an embodiment of an emotion recognition method based on EEG brainwaves according to the present invention;
fig. 2 is a schematic structural diagram of an emotion recognition system based on an EEG brainwave provided by the present invention.
Detailed Description
The core of the invention is to provide an emotion recognition method and system based on EEG brain waves, so as to realize intelligent analysis of EEG signals to judge emotion states, improve recognition accuracy and reduce recognition time.
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a flowchart illustrating an embodiment of an emotion recognition method based on EEG brainwaves according to the present invention. The equipment adopted in the method is preferably high-precision EEG signal acquisition equipment, the electrodes are dry electrodes and are mainly symmetrically distributed on the forehead or the frontal lobe, the electrode points are symmetrically distributed in the left and right direction, the single-channel electrodes have high sampling rate and can meet the requirement of accurate depiction of EEG signals, and the EEG signal acquisition equipment is particularly portable BCI equipment. The EEG signal acquisition equipment acquires the EEG signals of the user watching videos in the standard emotion library, and the acquisition time is preferably 60 minutes.
The emotion detection method adopted in the embodiment specifically includes the following steps:
EEG signal acquisition equipment acquires an EEG signal of a user, namely an EEG signal, and the EEG signal is transmitted to a data analysis system after being preprocessed.
Wherein, in the preprocessing step, the power frequency filtering and the band-pass filtering are carried out on the original signal. Because a large amount of power frequency interference noise exists in the original EEG signal, a 50Hz notch filter can be designed in the embodiment to remove power frequency interference, the signal is subjected to 0.5-40Hz band-pass filtering, and the eye electrical artifact is removed by using independent component analysis. By the above pre-processing method irrelevant useless information in the EEG signal is removed, leaving a clean signal relevant to the experimental task.
Due to the fact that the EEG signals have individual difference, the data normalization operation can be additionally adopted in the embodiment to guarantee the consistency of the signals.
2. And the data analysis system extracts the features of the EEG signal according to task requirements and transmits the features to the judgment and identification system.
Wherein the features extracted from the EEG signal include: the time domain characteristic, the frequency domain characteristic, the time frequency characteristic and the nonlinear characteristic are 11 characteristics in total. The time domain characteristics adopt four characteristics of mean value, variance, standard deviation and first-order difference; the frequency domain features adopt two features of the sum of the energy of the frequency band power spectrum and the maximum value of the energy of the frequency band power spectrum; the time-frequency characteristic is one, and the time-frequency characteristic is calculated by adopting a short-time Fourier transform (STFT) algorithm and a wavelet transform method. The nonlinear features comprise four features of correlation dimension, approximate entropy, fractal dimension and Lyapunov exponent. In the frequency characteristic extraction process, power spectrum values in different frequency bands, power spectrum ratios and differences of the different frequency bands are obtained through calculation after Fourier change.
In this embodiment, five-channel devices are specifically used to acquire electroencephalogram signals, that is, electroencephalogram signals of five channels are extracted, feature extraction is performed on the electroencephalogram signal of each channel, and 55 features are obtained through calculation.
3. The judgment and recognition system trains the classification model through the Light gbm algorithm to recognize various emotional states of the user.
The classification result output by the algorithm comprises four categories of neutrality, happiness, sadness and difficulty. And the Light gbm training classification model is used, so that various emotional states can be accurately distinguished.
The Light gbm algorithm is a lifting machine learning algorithm, is a rapid, distributed and high-performance gradient lifting framework based on a decision tree algorithm, can be used for sequencing, classifying, regressing and many other machine learning tasks, has higher training speed, lower memory consumption and better accuracy, is supported in a distributed manner, and can rapidly process mass data.
According to the emotion recognition method and system based on the EEG brain waves, provided by the invention, EEG signals of frontal lobe electrodes of users are collected through EEG signal collection equipment, characteristic values are extracted, and emotions are classified by adopting a Light gbm machine learning model so as to automatically detect emotion states. The method and the device can intelligently judge the emotional state of the user, improve the analysis efficiency and accuracy, widen the application scene of detection, improve the identification accuracy and reduce the identification time.
In detail, EEG signals are specific discharge activities of the human brain, and EEG electroencephalography is a commonly used analysis method. EEG has the advantages of convenience in acquisition mode, stability of signals, lower cost and the like. EEG signals have non-stationary characteristics and are subject to individual variability. Changes in the mood of the person are present on the EEG signal and can be obtained by analysis of the frequency distribution of the signal. The emotion recognition method adopts 11 groups of characteristics to recognize emotion, adopts a Light gbm machine learning model, and ensures the accuracy of classification of multi-class emotion data.
The frequency distribution of the brain electrical signals is as follows: delat wave: 0.-5-4hz, theta wave: 4-8hz, alpha wave: 8-13hz, beta wave: 13-35hz, gamma wave: greater than 35 hz. The EEG frequency distribution of a person in different emotional states is mainly located in alpha and beta frequency bands. By using the portable EEG acquisition equipment, the EEG signals of a person can be acquired in real time, the ratio of the power value of the EEG signals in different frequency bands to the energy of different frequency bands is calculated, the frequency spectrum energy is used as a characteristic, and the emotional state is accurately detected through a machine learning algorithm.
The emotion state of the user can be automatically detected in real time through an artificial intelligence algorithm, emotion recognition is carried out by extracting characteristic values from frontal lobe electroencephalogram activity based on portable EEG equipment, the user can use the emotion detection method conveniently, the emotion state of the user is automatically recognized and classified by the artificial intelligence algorithm based on portable BCI equipment, detection efficiency is improved, and application scenes are widened. In addition, the invention is based on the attention detection of the user resting EEG signal, and classifies the emotion by adopting the Lightgbm machine learning model, so that the time cost and the labor cost of detection can be reduced.
Referring to fig. 2, fig. 2 is a schematic structural diagram of an EEG brainwave-based emotion recognition system according to the present invention, which is used for implementing the above method, and includes:
the EEG signal acquisition equipment 101 is used for acquiring an EEG signal of a user, preprocessing the EEG signal and transmitting the processed EEG signal to a data analysis system;
the data analysis system 102 is used for extracting features of the EEG signal and transmitting the features to the judgment and identification system;
a judgment and recognition system 103 for recognizing the emotional state of the user by the Light gbm algorithm
Therefore, the system collects the frontal lobe electrode electroencephalogram signals of the user through EEG signal collecting equipment, extracts characteristic values, and classifies emotions by adopting a Lightgbm machine learning model so as to automatically detect the emotion states. The method and the device can intelligently judge the emotional state of the user, improve the analysis efficiency and accuracy and widen the application scene of detection.
For the introduction of the emotion recognition system based on the EEG brainwaves provided by the present invention, please refer to the above-mentioned embodiment of the emotion recognition method based on the EEG brainwaves, which is not described herein again. The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The emotion recognition method and system based on the EEG brainwaves provided by the invention are described in detail above. The principles and embodiments of the present invention are explained herein using specific examples, which are presented only to assist in understanding the method and its core concepts. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.
Claims (10)
1. An emotion recognition method based on EEG brainwaves, comprising:
the EEG signal acquisition equipment acquires an EEG signal of a user, and the EEG signal is transmitted to the data analysis system after being preprocessed;
the data analysis system extracts the features of the EEG signal and transmits the features to the judgment and identification system;
the judgment and identification system identifies the emotional state of the user through a Light gbm algorithm.
2. The EEG brainwave based emotion recognition method of claim 1, wherein said preprocessing includes power frequency filtering and band pass filtering.
3. The EEG brainwave based emotion recognition method of claim 1, further comprising, after said preprocessing step, performing a data normalization operation on the EEG signal.
4. The EEG brainwave based emotion recognition method of claim 1, wherein said EEG signal features include time domain features, frequency domain features, time-frequency features and non-linear features of the EEGB signal.
5. The EEG brainwave based emotion recognition method of claim 4, wherein said time domain features include mean, variance, standard deviation and first order difference features; the frequency domain features include the sum of the band power spectral energies, the band power spectral energy maximum.
6. The EEG brainwave based emotion recognition method of claim 4, wherein said time-frequency features are obtained using a short-time Fourier transform (STFT) and a wavelet transform.
7. The EEG brainwave based emotion recognition method of claim 4, wherein said non-linear features are correlation dimension, approximate entropy, fractal dimension and Lyapunov exponent feature.
8. The EEG brainwave based emotion recognition method of any one of claims 4 to 8, wherein the EEG signal acquisition device acquires the EEG signal of the user by using five channels, and the data analysis system performs feature extraction on the EEG signal of each channel respectively.
9. The EEG brainwave based emotion recognition method of claim 1, wherein said Light gbm algorithm is used for training a classification model and outputting an emotion classification recognition result.
10. An EEG brain wave based emotion recognition system for implementing the method of any of claims 1 to 9, comprising:
the EEG signal acquisition equipment is used for acquiring an EEG signal of a user, preprocessing the EEG signal and transmitting the processed EEG signal to the data analysis system;
the data analysis system is used for extracting the features of the EEG signal and transmitting the features to the judgment and identification system;
and the judgment and identification system is used for identifying the emotional state of the user through the Light gbm algorithm.
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| CN114699078A (en) * | 2022-03-08 | 2022-07-05 | 重庆邮电大学 | Emotion recognition method and system based on small number of channel EEG signals |
| CN115035608A (en) * | 2022-05-26 | 2022-09-09 | 支付宝(杭州)信息技术有限公司 | Living body detection method, device, equipment and system |
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