CN109815361A - A kind of intelligent music recommender system based on E.E.G identification - Google Patents
A kind of intelligent music recommender system based on E.E.G identification Download PDFInfo
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- CN109815361A CN109815361A CN201910088836.4A CN201910088836A CN109815361A CN 109815361 A CN109815361 A CN 109815361A CN 201910088836 A CN201910088836 A CN 201910088836A CN 109815361 A CN109815361 A CN 109815361A
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Abstract
The invention discloses a kind of intelligent music recommender system based on E.E.G identification, recommender system is made of wearable Think Gear sensor, mobile terminal and background data base；The wearable Think Gear sensor is worn on human body head and detects for E.E.G, by the frequency analysis to brain wave signal, gained frequency is matched to obtain the mood states of user by pattern-recognition；The frequency that wearable Think Gear sensor detects is sent to mobile terminal by bluetooth module, and data are uploaded to background data base by 4G module by mobile terminal；Background data base scores according to the data of upload, then selects different music according to the different mood of user and score value, passes through mobile terminal playing.Solve the problems, such as that existing music recommender system can not accurately adapt to different user.
Description
Technical field
The invention belongs to the technical fields that music is recommended, and in particular to a kind of intelligent music recommendation system based on E.E.G identification
System.
Background technique
21 century is the information age, and with the development of information technology and network technology, informationization has penetrated into people
The various aspects of daily life establish close contact with daily life already.It is getting faster in rhythm of life
Today, people are also more more and more intense to convenient and fast demand is entertained, so various music recommender systems are also gradually to come into being.From
Since Gu, music is just always along with daily life, and there are guitar, saxophone in Gu Yousheng vertical bamboo flute suona horn, the present, and nowadays people listen
Music passes through mobile phone, music player mostly or directly listens to online on computers, and mp3, mp4 few people use.
The form of music is also constantly being innovated and is being developed.In one's early years, people also pass through tape or CD listens to music, and digital music
It has been extended over the entire globe that, people listen to music by internet mostly, and in other words, internet is instantly most popular digital sound
Happy music medium, the music website combined with information technology with Internet technology, convenience, data storage safety,
Sharing, data capacity etc., hence it is evident that better than traditional tape and CD.
Original music recommender system only provides corresponding music according to user preferences mostly, but excessively extensively, may
Expected effect is not achieved.So needing a kind of more intelligent system, coordinate different users on its basis, according to user's
Musical taste, usually searching requirement, emotional feedback information and other certain features, providing for user some can more empathize
Music.
Summary of the invention
The purpose of the invention is to improve existing music recommender system, for user provide it is a kind of more intelligence just
The intelligent music recommender system based on E.E.G identification of benefit.
To realize the above-mentioned technical purpose, the technical scheme adopted by the invention is as follows:
A kind of intelligent music recommender system based on E.E.G identification, including wearable Think Gear sensor, movement are eventually
End and background devices；Wearable Think Gear sensor is worn on human body head for E.E.G
(electroencephalogram, eeg) signal detection, the frequency that wearable Think Gear sensor detects pass through bluetooth
Module is sent to mobile terminal (such as mobile phone, tablet computer), and data are uploaded to background devices by 4G module by mobile terminal；
Background devices are analyzed according to the calculation method of E.E.G and mood, are scored, and then select according to analysis result and score value
Suitable music, passes through mobile terminal playing.
To optimize above-mentioned technical proposal, the concrete measure taken further include:
Above-mentioned Think Gear sensor includes with the forehead sensor of the position as a reference point of point at ear-lobe, built-in adopts
Collection examines the Think Gear AM chip of correct algorithm and high-sensitivity amplifier, and the Think Gear sensor is with number
The brain wave signal that measures of mode, and brain wave data is transmitted by bluetooth serial ports, passes through setting code key identical with bluetooth and wave
Special rate carries out pairing connection, to realize the transparent transmission of E.E.G serial data, wireless transmission signal is output to background devices.
Above-mentioned background devices include E.E.G collection analysis module, scoring recommending module and music libraries, the E.E.G acquisition
Analysis module collection analysis brain wave data, brain wave data is classified, and the scoring recommending module based on the analysis results broadcast by music libraries
It puts and recommends music and receive user's scoring.
Brain wave data is divided into following four class: δ 1 by above-mentioned E.E.G collection analysis module~3Hz, θ be 4~7Hz, α 8
~13Hz, β is 14~30Hz, respectively indicates sleep state, sad state, tranquility and excitatory state.
The scoring algorithm of above-mentioned scoring recommending module are as follows:
In formula, RuiIt scores for user u the prediction of music item i；It is average for scoring of the user u to all music of system
Value；MuIt is user u to the deviation score of a certain music item i；TuFor according to the E.E.G of user u obtain to music item i's
Scoring；K is specific gravity of the E.E.G scoring in whole algorithmic formula.
Above-mentioned MuCalculation formula is as follows:
In formula, σuAnd σvRespectively indicate the deviation of target user u and neighbour user v scoring, Ni(u)It is similar to user u
Neighbour user collection, rviIndicate scoring of the user v to project i,WithIndicate the grade average of user u and user v, sim
(u, v) is the calculating that Pearson correlation coefficient is used to indicate the similarity between user.
Above-mentioned TuCalculation formula is as follows:
In formula, | xu-λj|minIndicate the minimum positive value for the brain wave signal value and music threshold difference that user u is received.Wherein xu
Indicate the user's u E.E.G numerical value x being collected intou=(1,30) Hz；λjIndicate music threshold values --- the music pair under corresponding emotional state
The EEG signal wave number answered, wherein λj={ 2,5.5,10.5,22 }, j value are 1,2,3,4.Research has shown that human body is in positive reason
For the brain wave signal generated under condition in the range of 1~30Hz, different frequencies corresponds to different moods, this formula to be accomplished
Mood is exactly converted to this by the numerical value that can analyze.
The invention has the following advantages:
After design of the invention, by the detection to user's E.E.G, recommend in time and accurately to be suitble to feelings instantly
The music of thread largely realizes the intelligent recommendation of music.
Detailed description of the invention
Fig. 1 is software workflow figure of the invention；
Fig. 2 is present system block diagram；
Fig. 3 is present system work flow diagram；
Fig. 4 is the acquisition of E.E.G of the embodiment of the present invention and transmission flow block diagram；
Fig. 5 is E.E.G band class figure of the present invention.
Specific embodiment
The embodiment of the present invention is described in further detail below in conjunction with attached drawing.
Referring to Fig. 1-3, a kind of intelligent music recommender system based on E.E.G identification of the invention, including wearable Think
Gear sensor, mobile terminal and background devices；Wearable Think Gear sensor is worn on human body head and examines for E.E.G
It surveys, the frequency that wearable Think Gear sensor detects is sent to mobile terminal (such as mobile phone, plate by bluetooth module
Computer), data are uploaded to background devices by 4G module by mobile terminal；Background devices according to E.E.G and mood calculation method
It analyzed, scored, then selected different music according to analysis result and score value, pass through mobile terminal playing.
In embodiment, Think Gear sensor includes with the forehead sensor of the position as a reference point of point at ear-lobe, interior
The Think Gear AM chip that correct algorithm and high-sensitivity amplifier are examined in acquisition is set, Think Gear sensor is with number
The brain wave signal that measures of mode, and brain wave data is transmitted by bluetooth serial ports, passes through setting code key identical with bluetooth and wave
Special rate carries out pairing connection, to realize the transparent transmission of E.E.G serial data, wireless transmission signal is output to background devices.
Referring to fig. 4 with transmission process, wearable device Think Gear brain-wave sensor is for acquiring E.E.G letter for E.E.G acquisition
Number and collected brain wave signal is transferred to background devices.By converting electric signal for brain wave signal, then pass through indigo plant
Tooth is transmitted to backstage with the processing of complete pair signals, is next output to mobile terminal and is matched.Telecommunications is converted by EEG signal
Number substantially process are as follows: by serial data be output in CPLD (programmable logic device) keep in；CPLD converts serial data
After parallel data, it is sent in MCU (microcontroller) by multiplexer；Finally, MCU (microcontroller) conveys data
To bluetooth module, data are wirelessly issued by it.
It control logic part in CPLD (programmable logic device) can be according to the data and ARM reading data that transmission comes
State, judge ARM whether continue read current data.Meanwhile in order to facilitate data processing, CPLD and ARM interface are reduced
Each ADC serial data exported is first converted to parallel data, is then output to data by multiplexer by number
In ARM, finally by UARTI port transmission to bluetooth module.
After the completion of the processing of signal, transmitted followed by wireless blue tooth.Bluetooth transmission, that is, bluetooth module only and ARM into
Row communication, ARM transfer data to bluetooth module by UART1 interface, then wireless to issue.UART1 interface is that bluetooth is embedded
The interface that the fast BTM0704C2P chip of mould provides.Bluetooth module is communicated by UART with LPC2144, and data are received
After be wirelessly transmitted to embedded processing terminal.Wherein LPC2144 is the main control chip as CPLD.
In embodiment, background devices include that E.E.G collection analysis module, scoring recommending module and music libraries, the E.E.G are adopted
Set analysis module collection analysis brain wave data, brain wave data is classified, and the scoring recommending module recommends sound based on the analysis results
Music storehouse plays music and receives user's scoring.
Research shows that E.E.G be it is a kind of using electrophysiological index record brain activity obtain method, brain is in activity, largely
The synchronous postsynaptic potential occurred of neuron is formed after summation.It record brain activity when electric wave variation, be it is some from
The rhythmic neural electrical activity of hair, its frequency variation range can be divided into four between 1-30 times per second under normal conditions
A wave band is δ wave, θ wave, α wave and β wave respectively.δ wave: frequency is 1~3Hz, and amplitude is 20~200 μ V.When people in infancy or
Intellectual development is immature, adult is under extremely tired and lethargic sleep or narcosis, this wave can be recorded in temporal lobe and top
Section；θ wave: frequency is 4~7Hz, and amplitude is 5~20 μ V.Adult's wish baffle or depression and mental patient in this
Kind wave is extremely significant.But this wave is the main component in the electroencephalogram of juvenile (10-17 years old)；α wave: frequency is 8~13Hz (flat
Mean is 10Hz), amplitude is 20~100 μ V.It is the basilic rhythm of normal person's E.E.G, if not additional stimulation, frequency
Rate is fairly constant.People is awake, quiet and the rhythm and pace of moving things is the most obvious when closing one's eyes, because this waveform is closest to the brain of right brain
Electric biological rhythm, then the inspiration state of people there have been；β wave: frequency is 14~30Hz, and amplitude is 100~150 μ V.Work as essence
There is this wave when refreshing nervous and excited or excited, when people wakes from a nightmare with a start, the slow wave rhythm and pace of moving things originally can be immediately by this
The rhythm and pace of moving things is substituted.In addition to this, in sleep it may also occur that the more special E.E.G of other waveforms, such as hump wave, σ wave, λ
Wave, κ-complex wave, μ wave etc..
So brain wave data is divided into following four class by E.E.G collection analysis module: δ is 1~3Hz, θ in the embodiment of the present invention
For 4~7Hz, α be 8~13Hz, β is 14~30Hz, respectively indicate the sleep state of natural person, sad state, tranquility and
Excitatory state is divided referring to Fig. 5.
In embodiment, the scoring algorithm for the recommending module that scores are as follows:
In formula, wherein RuiIndicate that user u scores to the prediction of music item i, the higher music item that scores more preferentially pushes away
It recommends.Indicate user u to the grade average of all music of system；MuIndicate user u to the deviation score of a certain music item i；
TuIndicate the scoring to music item i obtained according to the E.E.G of user u, the EEG obtained in conjunction with E.E.G collection analysis module
The analysis of signal is as a result, choose λ value, λ on the basis of corresponding moodjIndicate music threshold values (λj={ 2,5.5,10.5,22 }, j
Value 1,2,3,4), music threshold values indicates to correspond to the music type of corresponding emotional state under some EEG signal wave number.
Such as: λ1=2, i.e., when music threshold values value is 2, selection is suitble to dormant music；Work as λ2=5.5, λ3=
10.5 λ4=22 when i.e. music threshold values value is 5.5,10.5,22, chooses and is suitble to sad state, tranquility or excited shape
The music of state；When the brain wave signal of background analysis the result is that when sleep state, λ is taken1Value is 2；When the brain wave signal of background analysis
The result is that taking λ respectively when sad state, tranquility or excitatory statejValue is 5.5,10.5,22；Believe then in conjunction with E.E.G
Number value with music threshold values calculate so that user E.E.G value (xu) and music threshold values λjAbsolute value of the difference obtains minimum value, and difference is got over
It is small to illustrate that the emotional state of user instantly and music threshold values are closer, recommend the music accuracy rate for being suitble to user's mood instantly to get over
Height, because of the T calculated in this caseuIt is larger.K is used to adjust specific gravity of the E.E.G scoring in whole algorithmic formula, and k is got over
Big to indicate that E.E.G scoring accounting is bigger, the advantage that E.E.G is recommended is more prominent, finally integrates whole algorithm and calculates final scoring,
Background system selects the happy preferential recommendation of higher assessment partial to user.
In embodiment, MuCalculation formula is as follows:
In formula, σuAnd σvRespectively indicate the deviation of target user u and neighbour user v scoring, Ni(u)It is similar to user u
Neighbour user collection, rviIndicate scoring of the user v to project i,WithIndicate the grade average of user u and user v, sim
(u, v) is the calculating that Pearson correlation coefficient is used to indicate the similarity between user.
About Pearson correlation coefficient,Wherein rui、rvi
Practical scoring of the respectively user u and v to project i,WithIndicate the grade average of user u and user v, IuAnd IvRespectively
User u and user v comment excessive project set, their similarity, Mei Geyong are measured using the common scoring between user
The scoring at family vector Rn×mIt indicates, wherein n, m are respectively number of users and item number.The distance calculated between vector can obtain
The calculating of similarity between user.
In embodiment, TuCalculation formula is as follows:
In formula, | xu-λj|minIndicate the minimum positive value for the brain wave signal value and music threshold difference that user u is received.Wherein xu
Indicate the user's u E.E.G numerical value x being collected intou=(1,30) Hz；λjIndicate music threshold values --- the music pair under corresponding emotional state
The EEG signal wave number answered, wherein λj={ 2,5.5,10.5,22 }.The brain wave signal that human body generates under normal circumstances 1~
In the range of 30hz, different frequencies corresponds to different moods, and the scoring algorithm for the recommending module that scores to be accomplished being exactly to incite somebody to action
This mood is converted to scoring.A user is calculated for the comprehensive score of music libraries music through this algorithmic formula, according to
The shared specific gravity of E.E.G scoring is adjusted, adjustable E.E.G acts in this recommender system, keeps information matches more accurate, comprehensive
All scorings select high scoring according to the scoring height surveyed and carry out preferential recommendation.The higher recommendation effect that scores is better.
The above is only the preferred embodiment of the present invention, protection scope of the present invention is not limited merely to above-described embodiment, all
The technical solution belonged under thinking of the present invention all belongs to the scope of protection of the present invention.It should be pointed out that for the common of the art
For technical staff, several improvements and modifications without departing from the principles of the present invention should be regarded as protection scope of the present invention.
Claims (7)
1. a kind of intelligent music recommender system based on E.E.G identification, it is characterised in that: sensed including wearable Think Gear
Device, mobile terminal and background devices；The wearable Think Gear sensor is worn on human body head and detects for E.E.G；Institute
It states the electric signal that wearable Think Gear sensor detects and rate-adaptive pacemaker is converted by frequency discrimination link, which passes through indigo plant
Tooth module is sent to the mobile terminal, and data are uploaded to background devices by 4G module by mobile terminal；The background devices
It analyzed, scored according to the calculation method of E.E.G and mood, then select different sounds according to analysis result and score value
It is happy, pass through mobile terminal playing.
2. a kind of intelligent music recommender system based on E.E.G identification according to claim 1, it is characterised in that: described
Think Gear sensor includes examining correct algorithm with the forehead sensor of the position as a reference point of point at ear-lobe, built-in acquisition
The brain measured in a manner of number with the Think Gear AM chip of high-sensitivity amplifier, the Think Gear sensor
Wave signal, and brain wave data is transmitted by bluetooth serial ports, pairing company is carried out by the way that code key identical with bluetooth and baud rate is arranged
It connects, to realize the transparent transmission of E.E.G serial data, wireless transmission signal is output to background devices.
3. a kind of intelligent music recommender system based on E.E.G identification according to claim 2, it is characterised in that: after described
Platform equipment includes E.E.G collection analysis module, scoring recommending module and music libraries, the E.E.G collection analysis module collection analysis
Brain wave data classifies brain wave data, and music libraries play recommendation music and receive the scoring recommending module based on the analysis results
User's scoring.
4. a kind of intelligent music recommender system based on E.E.G identification according to claim 3, it is characterised in that: the brain
Brain wave data is divided into following four class by wave collection analysis module: δ is 1~3Hz, θ is 4~7Hz, α is 8~13Hz, β be 14~
30Hz respectively indicates sleep state, sad state, tranquility and excitatory state.
5. a kind of intelligent music recommender system based on E.E.G identification according to claim 3, it is characterised in that: institute's commentary
Divide the scoring algorithm of recommending module are as follows:
In formula, RuiIt scores for user u the prediction of music item i；It is user u to the grade average of all music of system；Mu
It is user u to the deviation score of a certain music item i；TuMusic item i is commented for what is obtained according to the E.E.G of user u
Point；K is specific gravity of the E.E.G scoring in whole algorithmic formula.
6. a kind of intelligent music recommender system based on E.E.G identification according to claim 4, it is characterised in that: the Mu
Calculation formula is as follows:
In formula, σuAnd σvRespectively indicate the deviation of target user u and neighbour user v scoring, Ni(u)It is similar with user u close
Adjacent user's collection, rviIndicate scoring of the user v to project i,WithIndicate the grade average of user u and user v, sim (u, v)
It is the calculating that Pearson correlation coefficient is used to indicate the similarity between user.
7. a kind of intelligent music recommender system based on E.E.G identification according to claim 4, it is characterised in that: the Tu
Calculation formula is as follows:
In formula, | xu-λj|minFor the minimum positive value of the user u brain wave signal value received and music threshold difference；xuIt is collected into
User's u E.E.G numerical value xu=(1,30) Hz；λjFor music threshold values --- the corresponding brain wave signal wave number of different emotional state music,
Wherein λj={ 2,5.5,10.5,22 }, j value are 1,2,3,4.
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| CN110801568A (en) * | 2019-06-28 | 2020-02-18 | 林万佳 | Device for making and outputting pulsating music and method for forming pulsating music |
| CN113220122A (en) * | 2021-05-06 | 2021-08-06 | 西安慧脑智能科技有限公司 | Brain wave audio processing method, equipment and system |
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| CN103412646A (en) * | 2013-08-07 | 2013-11-27 | 南京师范大学 | Emotional music recommendation method based on brain-computer interaction |
| CN106991122A (en) * | 2017-02-27 | 2017-07-28 | 四川大学 | A kind of film based on particle cluster algorithm recommends method |
| CN108073284A (en) * | 2017-12-15 | 2018-05-25 | 南京信息工程大学 | Purchase system based on brain wave identification mood |
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| CN103412646A (en) * | 2013-08-07 | 2013-11-27 | 南京师范大学 | Emotional music recommendation method based on brain-computer interaction |
| CN106991122A (en) * | 2017-02-27 | 2017-07-28 | 四川大学 | A kind of film based on particle cluster algorithm recommends method |
| CN108073284A (en) * | 2017-12-15 | 2018-05-25 | 南京信息工程大学 | Purchase system based on brain wave identification mood |
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| CN110801568A (en) * | 2019-06-28 | 2020-02-18 | 林万佳 | Device for making and outputting pulsating music and method for forming pulsating music |
| CN110801568B (en) * | 2019-06-28 | 2022-07-08 | 林万佳 | Device for making and outputting pulsating music and method for forming pulsating music |
| CN113220122A (en) * | 2021-05-06 | 2021-08-06 | 西安慧脑智能科技有限公司 | Brain wave audio processing method, equipment and system |
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