CN103976740A - Network environment-oriented electroencephalogram identification system and network environment-oriented electroencephalogram identification method - Google Patents
Network environment-oriented electroencephalogram identification system and network environment-oriented electroencephalogram identification method Download PDFInfo
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Abstract
The invention relates to a network environment-oriented electroencephalogram identification system and a network environment-oriented electroencephalogram identification method. The identification system comprises a plurality of wearable electroencephalogram measuring devices, more than one mobile network terminal and an electroencephalogram data processing device; the electroencephalogram measuring devices are used for processing measured electroencephalograms into network electroencephalogram data and transmitting the network electroencephalogram data to the electroencephalogram data processing device for processing; the electroencephalogram data processing device is used for outputting an identification result and transmitting the identification result to a mobile network terminal for displaying. The identification method comprises the steps of measuring the electroencephalograms of a tested individual by using the electroencephalogram measuring devices and processing the electroencephalograms into the network electroencephalogram data, transmitting the electroencephalogram data to the electroencephalogram data processing device by virtue of a wireless network card of the mobile network terminal, performing segment treatment on the electroencephalograms and extracting electroencephalogram characteristic information by use of the electroencephalogram data processing device, screening electroencephalogram characteristic vector training samples based on reputation computing, and determining the input sample and the degree of membership of a fuzzy support vector machine, thereby completing the identification of the tested individual and outputting the result.
Description
Technical field
The present invention relates to a kind of identification system and recognition methods, particularly about a kind of EEG signals identification system and recognition methods of network-oriented environment.
Background technology
Informationization and the networking process of society have proposed new requirement to identity recognizing technology.The a lot of attacks for network identity data base that occur in recent years, the privacy on user, the rights and interests of enterprise and even society stable caused serious impact.Therefore, new identity recognizing technology not only will meet safe and reliable basic demand, more needs to adapt to network environment.Common identity recognizing technology can be divided three classes at present: identity recognizing technology, the identity recognizing technology based on the specific having such as identity card, passport and the identity recognizing technology based on the biological character such as fingerprint, iris based on the specific knowledge such as password, password.The above two are comparatively common, and technology is also relatively ripe, but is more easily usurped by the illegal steal information in the external world, malice.Identity recognizing technology based on biological character becomes study hotspot in recent years with its higher specificity and safety, and the measurement, appearance recognition technology that such as fingerprint identification technology is widely used in the driving school in all parts of the country training time is for booting computer user identification etc.Biological character recognition technology have be difficult for stolen, availability by force, the feature such as safe ready more.In theory, any physiology of people or behavior characteristics all can be used for identification on the basis meeting the following conditions: 1) availability, is easy to gather and measure; 2) universality, each possess per capita; 3) uniqueness, everyone feature difference; 4) stability, the not variation in place in time and changing.Although by using novel electricity, optics, acoustics biosensor in conjunction with the means such as computer science, statistics, the dependable with function of the identity recognizing technology based on biological character progressively improves, for example, but still there is insurmountable problem in the identity recognizing technology based on the traditional biological such as fingerprint, appearance character: appearance identification cannot be distinguished the similar twins of appearance and be subject to the impacts such as ambient lighting; Fingerprint recognition is subject to the impact of finger injuries, and sample fingerprint is easily maliciously stolen; Voice recognition can be copied waveform etc. by Technologies of Handling Voice in Computer.
The identification of brain electricity (Electroencephalograph, EEG) signal is a kind of new identity recognizing technology based on biological character.The identification of brain electricity is distinguished Different Individual by the characteristic information of measuring, extract, compare Different Individual EEG signals.Compared with identity recognizing technology based on other biological character, EEG signals identification is due to specificity and the disguise of the cognitive signal of human thinking, is more difficultly cracked and usurps, and having started to attempt application at safety-security area.The system description at first such as Poulos EEG signals identification mechanism.The uses such as Huang 64 passage medical science EEG measuring equipment and complicated EEG measuring task have reached 100% recognition accuracy, have verified the feasibility in theory of EEG signals identification.Matthias etc. cut down brain wave acquisition port number, use homemade simple measurement device to complete authentication and identifying.Marcel etc. simplify the task of EEG measuring.Along with the development of wearable measurement computing technique, taking health as physical support, be worn on tested individuality EEG measuring with it by supporters such as headband, the helmet, earphones and become study hotspot.Corey etc. start to attempt using the wearable EEG measuring helmet of consumer level radio communication to realize the identity identifying and authenticating for safety-security area with Chuang etc.Actual application background and technology trends require the identification of brain electricity not re-use traditional large-scale wet electrode EEG measuring equipment, but in realizing wearable EEG measuring system structure, simplify EEG measuring task, and adopt highly reliable feature extraction, algorithm for pattern recognition to ensure accuracy of identification.Complex forms, the length consuming time of the EEG measuring task adopting in existing wearable brain electricity identification research, cannot meet the following demand of network environment hypencephalon electricity identification: 1) reliability: reliability is the basis of identification mechanism, comprises that front end measuring system accuracy of identification, wireless network transmissions safety and Back end data are processed and the robustness of Long-distance Control; 2) real-time: need to adopt the methods such as sparseness measuring to cut down data volume in the situation that guaranteeing precision, shorten EEG measuring task consuming time; 3) wearable: brain electricity identification procedure need to design hardware structure wearable, portable, radio communication, to adapt to heterogeneous networks measurement environment, optimize the practicality of brain electricity identification scheme; 4) sparse and low expense: the authentication of network brain electricity need to be in conjunction with network environment, reduces unnecessary measurement links and hardware device, the expense of reduction time, energy and cost of manufacture.
For meeting the demand of above network brain electricity authentication, need to redesign sparse wearable EEG measuring software and hardware system, simplify EEG measuring task, more need the existing feature extraction mode recognition methods of supporting improvement.Support vector machine (Support Vector Machine, SVM) is a kind of machine learning method based on Statistical Learning Theory, is suitable for for small sample, the pattern recognition that non-linear and high dimensional feature is vectorial.The EEG signals sample size that wearable EEG measuring equipment obtains is limited, and the characteristic vector dimension of extracting is high, so mainly take at present support vector machine sorting algorithm to complete brain electricity identification task.But, the data transmission environments of the features such as sparse eeg data measurement data amount is few, be very easily subject to that outside noise affects, unstable networks and support vector machine itself sensitivity to input training sample noise, makes traditional support vector machine method also cannot adapt to the needs of net environment eeg data classification.
Summary of the invention
For the problems referred to above, the object of this invention is to provide a kind of EEG signals identification system and recognition methods of reliable, the real-time network-oriented environment based on wearable EEG measuring device.
For achieving the above object, the present invention takes following technical scheme: a kind of EEG signals identification system of network-oriented environment, is characterized in that: it comprises some wearable EEG measuring devices, more than one mobile network's terminal and an eeg data blood processor; Described wearable EEG measuring device transfers to described mobile network's terminal after the EEG Processing of the tested individuality measuring is become to eeg data, the eeg data that described mobile network's end-on is received is stored, and the eeg data of storage is transferred to described eeg data blood processor, described eeg data blood processor is processed rear output recognition result to the network eeg data receiving, and recognition result is transferred to described mobile network's terminal shows.
Described wearable EEG measuring device comprises the dry electrode of some EEG measuring, an amplification filtering module, an analog-to-digital conversion module and a bluetooth serial ports module; The dry electrode of some described EEG measuring transfers to described amplification filtering module by wire by the brain electric analoging signal measuring, brain electric analoging signal after amplification filtering transfers to described analog-to-digital conversion module, and being converted to brain electricity digital signal waiting for transmission, brain electricity digital signal transfers to described mobile network's terminal through described bluetooth serial ports module.
Described mobile network's terminal comprises bluetooth receiver module, data memory module, wireless network card and display module; Described bluetooth receiver module is stored brain electricity digital data transmission to the described data memory module receiving, the eeg data of described data memory module storage transfers to described eeg data blood processor by described wireless network card, described eeg data blood processor transfers to display module by recognition result by described wireless network card and shows after the eeg data receiving is processed.
Described mobile network's terminal adopts the one in mobile phone, panel computer and notebook computer.
Described eeg data blood processor comprises High_speed NIC, data sectional and characteristic extracting module, prestige computing module and identification module, the eeg data that described mobile network's terminal sends transfers to described data sectional and characteristic extracting module by described High_speed NIC, described data sectional and characteristic extracting module are carried out segment processing to the eeg data receiving and are extracted after brain electrical feature information, obtain brain electrical feature vector training sample and transfer to described prestige computing module, described prestige computing module carries out credit value assessment to the brain electrical feature sample receiving, filter out reliable brain electrical feature sample, the reliable brain electrical feature sample and the credit value thereof that filter out are transferred to described identification module by described prestige computing module, recognition result is identified and exported to described identification module to the identity of tested individuality, recognition result transfers to described mobile network's terminal by described High_speed NIC and shows.
An EEG signals personal identification method that adopts the network-oriented environment of identification system, it comprises the following steps: 1) an EEG signals identification system that comprises the network-oriented environment of some wearable EEG measuring devices, more than one mobile network's terminal and an eeg data blood processor is set; Wherein, wearable EEG measuring device comprises the dry electrode of EEG measuring, amplification filtering module, analog-to-digital conversion module and bluetooth serial ports module; Mobile network's terminal comprises bluetooth receiver module, data memory module, wireless network card and display module; Eeg data blood processor comprises High_speed NIC, data sectional and characteristic extracting module, prestige computing module and identification module; 2) use the EEG signals of the tested individuality of the dry electrode pair of EEG measuring to carry out perception measurement, the brain electric analoging signal measuring is exported brain electricity digital signal after amplification filtering module and analog-to-digital conversion module processing, and brain electricity digital signal transfers to mobile network's terminal by bluetooth serial ports module; 3) in mobile network's terminal, bluetooth receiver module is by the brain electricity digital data transmission receiving to data memory module, and data memory module transfers to eeg data blood processor by the eeg data of storage by wireless network card; 4) eeg data blood processor is processed the EEG signals receiving, complete the identification to tested individuality and export recognition result, it specifically comprises the following steps: (1) data sectional and characteristic extracting module are carried out segment processing to the network eeg data receiving by High_speed NIC and extracted after brain electrical feature information, and the brain electrical feature vector training sample set obtaining is transferred to prestige computing module; (2) in prestige computing module, the brain electrical feature vector training sample that calculating receives is concentrated various kinds credit value originally, and prestige threshold value is set, brain electrical feature vector training sample is screened, and brain electrical feature vector training sample and credit value thereof through screening are transferred to identification module; (3) according to the vector training sample of the brain electrical feature through screening and the credit value thereof that receive, identification module is determined input sample and the degree of membership parameter of fuzzy support vector machine, completes the identification to tested individuality and exports recognition result; 5) the identification result that identification module is exported feeds back to mobile network's terminal by High_speed NIC and shows.
Described step 4) in, data sectional and characteristic extracting module are carried out segment processing and are extracted brain electrical feature information the network eeg data receiving, and it specifically comprises the following steps: the i that (I) preset data segmentation and characteristic extracting module receive
sthe network eeg data of individual tested individuality
for:
In formula, N
efor the data length of the original amplitude sequence of tested individual brain wave, i
s=1,2 ... N
s, N
sfor the number of tested individuality; (II) data sectional and characteristic extracting module are by i
sthe network eeg data of individual tested individuality
be divided into N
isection is chosen at random starting point, to network eeg data in each decile eeg data section
carry out not decile segmentation, obtain N
dindividual not decile eeg data section, needs to meet following principle in fragmentation procedure: first not the head end of decile eeg data section and last not the end of decile eeg data section be N with data length respectively
ethe head end of the original amplitude sequence of brain wave identical with end; Each not length of decile eeg data section is greater than the length of decile eeg data section, and does not exceed the first and last end of the original amplitude sequence of brain wave; (III) is respectively to N
sindividual tested individuality is the N after decile segmentation not
dindividual eeg data section is carried out feature extraction, Primary Construction brain electrical feature vector training sample set:
In formula,
represent i
sthe i of individual tested individuality
dsection brain electrical feature vector training sample,
represent the class label of this training sample, N
dfor the number of decile eeg data section not, N
sfor the number of tested individuality.
Described step 4) in, prestige computing module calculates brain electrical feature vector training sample and concentrates various kinds credit value originally, and by prestige threshold value is set, brain electrical feature vector training sample is screened, it comprises the following steps: (I) used correlation computations theory, measures identical tested individual i
sthe similarity of different brain electrical features vector training samples; For same tested individual i
s, calculate i
d1with i
d2the similarity L of individual brain electrical feature vector training sample:
In formula,
with
represent respectively tested individual i
si
d1with i
d2individual brain electrical feature vector training sample; (II), according to similarity assessment result, calculates i
sthe i of individual tested individuality
dsection brain electrical feature vector training sample
credit value
In formula, i
d=1,2...N
d, i
s=1,2...N
s, N
dfor the number of decile eeg data section not, N
sfor the number of tested individuality; (III) arranges thresholding credit value in prestige computing module, by comparing credit value and the thresholding credit value of brain electrical feature vector training sample, complete the screening to brain electrical feature vector training sample, it specifically comprises the following steps: 1. credit value and the thresholding credit value of each brain electrical feature vector training sample are compared, remove the brain electrical feature vector training sample of credit value lower than thresholding credit value, and the label of not decile eeg data section corresponding the brain electrical feature vector training sample of removal is fed back to data sectional and characteristic extracting module; 2. according to the label of the not decile eeg data section receiving, data sectional and characteristic extracting module are chosen starting point again from each decile eeg data section, network eeg data is carried out to not decile segmentation, and utilize the neencephalon electrical feature vector training sample that the new not decile eeg data section intercepting builds to replace the brain electrical feature vector training sample of credit value lower than thresholding credit value; 3. repeating step 1. with step 2., until the brain electrical feature that all structures obtain vector training sample meets the requirement of thresholding credit value, complete the screening to brain electrical feature vector training sample, and brain electrical feature vector training sample and credit value thereof through screening are transferred to identification module.
Described step 4) in, identification module completes the identification to tested individuality and exports recognition result, it specifically comprises the following steps: (I), according to the vector training sample of the brain electrical feature through screening and the credit value thereof that receive, identification module is determined degree of membership and constructed prestige and optimize two class fuzzy support vector classification devices; (II), according to two class fuzzy support vector classification devices of construction complete, solves two class classification problems; Adopt 1-a-r rule to construct multiple two class fuzzy support vector classification devices, complete the fuzzy support vector machine Multiclass Classification of calculating based on prestige; (III) makes tested individuality complete EEG measuring task at a certain net environment, using the eeg data of the tested individuality of wearable EEG measuring measurement device as data to be identified, and pass through mobile network's terminal transmission to eeg data blood processor, adopt fuzzy support vector machine Multiclass Classification in identification module, the identity of tested individuality is identified.
The present invention is owing to taking above technical scheme, it has the following advantages: 1, the EEG signals identification system of the present invention's network-oriented environment of some wearable EEG measuring devices, more than one mobile network's terminal and an eeg data blood processor because employing comprises is to being measured, transmit and identify by the EEG signals of side individuality, therefore compared with the EEG measuring equipment of available technology adopting complexity, the present invention has expanded the application scenarios of brain electricity authentication, is applicable to the identification of network environment.2, while adopting personal identification method of the present invention to identify the identity of tested individuality, utilize EEG measuring task simple, real-time, non-directivity to reduce and measure difficulty, therefore consuming time shorter when the present invention utilizes wearable EEG measuring device to measure the EEG signals of tested individuality, data volume still less.3, the present invention is due to data sectional and characteristic extracting module and prestige computing module being set in eeg data blood processor, data sectional and characteristic extracting module are carried out segment processing to the eeg data receiving and are extracted after brain electrical feature information, obtain brain electrical feature vector training sample and transfer to prestige computing module, thresholding credit value is set in prestige computing module, by comparing credit value and the thresholding credit value of brain electrical feature vector training sample, complete the screening to brain electrical feature vector training sample, for the brain electrical feature vector training sample that does not meet the requirement of thresholding credit value, rebuild brain electrical feature vector training sample by data sectional and characteristic extracting module, prestige computing module transfers to identification module by the reliable brain electrical feature sample filtering out and credit value thereof and carries out identification, therefore adopt recognition methods of the present invention that enough reliable training samples can be provided, thereby make recognition result more reliable.4, the present invention determines that owing to adopting in identification module based on prestige result of calculation the fuzzy support vector machine Multiclass Classification of degree of membership identifies the identity of tested individuality, therefore recognition methods of the present invention can improve the accuracy rate of identification, realizes reliably, brain electricity identification fast at net environment.Based on above advantage, the present invention can be widely used in network remote identification.
Brief description of the drawings
Fig. 1 is the structural representation of the EEG signals identification system of network-oriented environment of the present invention;
Fig. 2 is the flow chart of the EEG signals personal identification method of network-oriented environment of the present invention;
Fig. 3 is network eeg data section segmentation schematic diagram;
Fig. 4 is the real-time data acquisition processing platform based on LabVIEW;
Fig. 5 is network eeg data section result schematic diagram; Wherein, transverse axis express time, its unit is s, and the longitudinal axis represents EEG signals amplitude, and its unit is μ V;
Fig. 6 is eeg data section prestige result of calculation schematic diagram; Wherein, transverse axis represents eeg data segment number, and the longitudinal axis represents the credit value of eeg data section;
Fig. 7 is the result schematic diagram of the eeg data similarity measurement after prestige is optimized; Wherein, transverse axis represents the numbering of tested individuality, and the longitudinal axis represents the average similarity of eeg data section;
the average similarity of the identical tested individual eeg data section of curve representation forming,
the average similarity of the different tested individual eeg data sections of curve representation that form;
Fig. 8 is different sorting technique brain electricity identification result comparison schematic diagrams; Wherein, transverse axis represents the segments of eeg data, and the longitudinal axis represents the accuracy of brain electricity identification;
the curve representation forming adopts the accuracy of the brain electricity identification that basic svm classifier algorithm obtains;
the accuracy of the brain electricity identification that the fuzzy svm classifier algorithm of curve representation employing Gauss distribution forming obtains;
the accuracy of the brain electricity identification that the curve representation employing prestige Optimization of Fuzzy svm classifier algorithm forming obtains.
Detailed description of the invention
Below in conjunction with drawings and Examples, the present invention is described in detail.
As shown in Figure 1, the EEG signals identification system of network-oriented environment of the present invention comprise some wearable EEG measuring device 1, more than one mobile network's terminal 2 and an eeg data blood processor 3.Wearable EEG measuring device 1 transfers to mobile network's terminal 2 after the EEG Processing of the tested individuality measuring is become to eeg data, mobile network's terminal 2 is stored the eeg data receiving, and the eeg data of storage is transferred to eeg data blood processor 3, eeg data blood processor 3 is processed rear output recognition result and transfers to mobile network's terminal 2 the network eeg data receiving, and by mobile network's terminal 2, recognition result is shown.
In above-described embodiment, wearable EEG measuring device 1 comprises the dry electrode 11 of some EEG measuring, an amplification filtering module 12, an analog-to-digital conversion module 13 and a bluetooth serial ports module 14.The dry electrode 11 of some EEG measuring transfers to amplification filtering module 12 by wire by the brain electric analoging signal measuring, brain electric analoging signal after amplification filtering transfers to analog-to-digital conversion module 13, brain electric analoging signal is converted to brain electricity digital signal waiting for transmission, and brain electricity digital signal transfers to mobile network's terminal 2 through bluetooth serial ports module 14.
In above-described embodiment, mobile network's terminal 2 adopts the one in mobile phone, panel computer and notebook computer.Mobile network's terminal 2 comprises bluetooth receiver module 21, data memory module 22, wireless network card 23 and display module 24.Bluetooth receiver module 21 is stored brain electricity digital data transmission to the data memory module 22 receiving, the eeg data that data memory module 22 is stored transfers to eeg data blood processor 3 by wireless network card 23, after eeg data blood processor 3 is processed the eeg data receiving, recognition result is transferred to display module 24 by wireless network card 23, by display module 24 Identification display results.
In above-described embodiment, eeg data blood processor 3 comprises High_speed NIC 31, data sectional and characteristic extracting module 32, prestige computing module 33 and identification module 34.The eeg data that mobile network's terminal 2 sends transfers to data sectional and characteristic extracting module 32 by High_speed NIC 31, data sectional and characteristic extracting module 32 are carried out segment processing to the eeg data receiving and are extracted after brain electrical feature information, obtain brain electrical feature vector training sample and transfer to prestige computing module 33, prestige computing module 33 carries out credit value assessment to the brain electrical feature sample receiving, filter out reliable brain electrical feature sample, the reliable brain electrical feature sample and the credit value thereof that filter out are transferred to identification module 34 by prestige computing module 33, according to the reliable brain electrical feature sample and the credit value thereof that receive, recognition result is identified and exported to identification module 34 to the identity of tested individuality, recognition result transfers to mobile network's terminal 2 by High_speed NIC 31 and shows.
As shown in Figure 2, adopt the EEG signals identification system of network-oriented environment of the present invention tested individuality to be carried out to the method for identification, it comprises the following steps:
1) arrange one comprise some wearable EEG measuring device 1, the EEG signals identification system of the network-oriented environment of more than one mobile network's terminal 2 and an eeg data blood processor 3.Wherein, wearable EEG measuring device 1 comprises the dry electrode 11 of EEG measuring, amplification filtering module 12, analog-to-digital conversion module 13 and bluetooth serial ports module 14.Mobile network's terminal 2 comprises bluetooth receiver module 21, data memory module 22, wireless network card 23 and display module 24.Eeg data blood processor 3 comprises High_speed NIC 31, data sectional and characteristic extracting module 32, prestige computing module 33 and identification module 34.
2) use the dry electrode 11 of EEG measuring to carry out perception measurement to the EEG signals of tested individuality, the brain electric analoging signal measuring is exported brain electricity digital signal after amplification filtering module 12 and analog-to-digital conversion module 13 processing, brain electricity digital signal transfers to mobile network's terminal 2 by bluetooth serial ports module 14, provides initial data for building brain electricity identity data training set.
3) in mobile network's terminal 2, bluetooth receiver module 21 is by the brain electricity digital data transmission receiving to data memory module 22, and data memory module 22 transfers to eeg data blood processor 3 by the eeg data of storage by wireless network card 23.
4) eeg data blood processor 3 is processed the EEG signals receiving, and completes the identification to tested individuality, and it specifically comprises the following steps:
(1) data sectional and characteristic extracting module 32 receive by High_speed NIC 31 the network eeg data that mobile network's terminal 2 sends, as shown in Figure 3, data sectional and characteristic extracting module 32 are carried out segment processing to the network eeg data receiving and are extracted after brain electrical feature information, obtain brain electrical feature vector training sample set and transfer to prestige computing module 33, it specifically comprises:
The i that (I) preset data segmentation and characteristic extracting module 32 receive
sthe network eeg data of individual tested individuality
for:
In formula, N
efor the data length of the original amplitude sequence of tested individual brain wave, i
s=1,2 ... N
s, N
sfor the number of tested individuality.
(II) data sectional and characteristic extracting module 32 are by the network eeg data of is tested individuality
be divided into N
isection.In each decile eeg data section, choose at random starting point, to network eeg data
carry out not decile segmentation, obtain N
dindividual not decile eeg data section, needs to meet following principle in fragmentation procedure:
First not the head end of decile eeg data section and last not decile eeg data section end respectively with step 1. in data length be N
ethe head end of the original amplitude sequence of brain wave identical with end.
Each not length of decile eeg data section is greater than the length of decile eeg data section, and does not exceed the first and last end of the original amplitude sequence of brain wave.
(III) is respectively to N
sindividual tested individuality not c eeg data section after decile segmentation carries out feature extraction, Primary Construction brain electrical feature vector training sample set:
In formula,
represent i
sthe i of individual tested individuality
dsection brain electrical feature vector training sample,
represent the class label of this training sample, N
dfor the number of decile eeg data section not.
(2) in prestige computing module 33, the brain electrical feature vector training sample that calculating receives is concentrated various kinds credit value originally, and by prestige threshold value is set, brain electrical feature vector training sample is screened, and brain electrical feature vector training sample and credit value thereof through screening are transferred to identification module 34, it specifically comprises:
(I) used correlation computations theory, measures identical tested individual i
sthe similarity of different brain electrical features vector training samples;
For same tested individual i
s, calculate i
d1with i
d2the similarity L of individual brain electrical feature vector training sample:
In formula,
with
represent respectively tested individual i
si
d1with i
d2individual brain electrical feature vector training sample.
(II), according to similarity assessment result, calculates i
sthe i of individual tested individuality
dsection brain electrical feature vector training sample
credit value
In formula, i
d=1,2...N
d, i
s=1,2...N
s, N
dfor the number of decile eeg data section not, N
sfor the number of tested individuality.
(III) arranges thresholding credit value in prestige computing module 33, by comparing credit value and the thresholding credit value of brain electrical feature vector training sample, completes the screening to brain electrical feature vector training sample, and it specifically comprises the following steps:
1. credit value and the thresholding credit value of each brain electrical feature vector training sample are compared, remove the brain electrical feature vector training sample of credit value lower than thresholding credit value, and the label of not decile eeg data section corresponding the brain electrical feature vector training sample of removal is fed back to data sectional and characteristic extracting module 32.
2. according to the label of the not decile eeg data section receiving, data sectional and characteristic extracting module 32 are chosen starting point again from each decile eeg data section, network eeg data is carried out to not decile segmentation, and utilize the neencephalon electrical feature vector training sample that the new not decile eeg data section intercepting builds to replace the brain electrical feature vector training sample of credit value lower than thresholding credit value.
3. repeating step 1. with step 2., until the brain electrical feature that all structures obtain vector training sample meets the requirement of thresholding credit value, complete the screening to brain electrical feature vector training sample, and brain electrical feature vector training sample and credit value thereof through screening are transferred to identification module 34.
(3) according to the vector training sample of the brain electrical feature through screening and the credit value thereof that receive, identification module 34 is determined input sample and the degree of membership parameter of fuzzy support vector machine, complete the identification to tested individuality and export recognition result, it specifically comprises the following steps:
(I), according to the vector training sample of the brain electrical feature through screening and the credit value thereof that receive, identification module 34 is determined degree of membership and constructs prestige and optimize two class fuzzy support vector classification devices;
For i
sthe i of individual tested individuality
dsection brain electrical feature vector training sample
class label
introduce fuzzy membership
two class classification problems are expressed as:
In formula,
Utilize this two classes classification problem identification brain electrical feature vector training sample whether to belong to class a.
In formula, degree of membership
represent the weight of sample for the definite impact of optimum hyperplane.
I
sindividual tested individual i
dsection brain electrical feature vector
degree of membership
be expressed as:
In formula, Q is yardstick convergent-divergent normalized function, i
d=1,2...N
d, i
s=1,2...N
s.
Very large to the brain electrical feature vector training sample difficulty of classifying owing to finding suitable hyperplane in sample space in input, therefore adopt map-germ function phi by input sample space being mapped to feature space Z searching hyperplane, hyperplane is expressed as:
In formula, w and b are the parameter of determining hyperplane, and w represents the vector perpendicular to hyperplane, and b represents the displacement of hyperplane to initial point.
For construct optimum hyperplane in the situation that brain electrical feature vector training sample is linearly inseparable, introduce slack variable
with punishment component C.The situation that is linearly inseparable for brain electrical feature vector training sample, the problem that solves optimum hyperplane is summed up as following quadratic programming problem:
In formula, i
d=1,2 ... N
d, i
s=1,2 ... N
s, slack variable
Punishment component C tolerance fuzzy support vector machine is to all strength of punishments that comprise error point, and degree of membership
give different punishment weights to the input sample in different range of error, to reduce the impact on classifying quality of input sample that deviation is larger.Degree of membership
less, i.e. sample
the error comprising is larger, and less on determining the impact of optimum hyperplane, vice versa.Slack variable
the tolerance of tolerance linearly inseparable error.Punishment component C, slack variable
q matches with yardstick convergent-divergent normalized function, and is determined by experiment.
Adopt Lagrangian to solve the quadratic programming problem that meets Karush-Kuhn-Tucker (KKT) condition, construction complete prestige is optimized two class fuzzy support vector classification devices.Thereby judge whether brain electrical feature vector training sample belongs to class a.
(II), according to two class fuzzy support vector classification devices of construction complete, solves two class classification problems; Adopt 1-a-r rule to construct multiple two class fuzzy support vector classification devices, complete the fuzzy support vector machine Multiclass Classification of calculating based on prestige.
(III) makes tested individuality complete EEG measuring task at a certain net environment, the eeg data of the tested individuality that wearable EEG measuring device 1 is measured is as data to be identified, and transfer to eeg data blood processor 3 by mobile network's terminal 2, adopt fuzzy support vector machine Multiclass Classification in identification module 34, the identity of tested individuality is identified.
5) the identification result that identification module 34 is exported feeds back to mobile network's terminal 2 by High_speed NIC 31 and shows.
Embodiment: an EEG signals identification system that comprises the network-oriented environment of some wearable EEG measuring device 1, mobile network's terminal 2 and eeg data blood processor 3 is set.Wherein, wearable EEG measuring device 1 adopts the MINDWAVEMOBILE device of Neurosky company, eeg data blood processor 3 to adopt LabVIEW data acquisition and the analysis platform of NI PXI hardware supported.The EEG signals that MINDWAVEMOBILE equipment collects transfers to mobile network's terminal 2 by Bluetooth2.1, mobile network's terminal 2 is stored the EEG signals receiving, and the eeg data of storage is transferred to LabVIEW data acquisition and analysis platform, LabVIEW data acquisition and analysis platform carry out data sectional and feature extraction and classification based training and calculating to the network eeg data receiving, and obtain identification result and feed back to mobile network's terminal 2.
Adopt the EEG signals identification system of network-oriented environment of the present invention to identify the identity of tested individuality, its process is:
1) adopt MINDWAVE MOBILE EEG measuring device 1 to gather the EEG signals of 18 tested individualities, it comprises the data to be identified that every tested individuality all gathered to training data and the 10s of 60s.The eeg data collecting transfers to LabVIEW data acquisition and analysis platform by mobile network's terminal 2.
Adopt MINDWAVE MOBILE EEG measuring device 1 to measure the EEG signals of 18 tested individualities in unconscious meditation state 60s, and transfer to LabVIEW data acquisition and analysis platform by mobile network's terminal 2.
2) LabVIEW data acquisition and analysis platform are processed the eeg data receiving, and it comprises:
First, LabVIEW data acquisition and analysis platform carry out segmentation to the 60s training data collecting, and obtain network eeg data section as shown in Figure 5.This eeg data section is carried out to analyzing and processing, and extracts the following feature of EEG signals:
(1) each frequency range brain wave Energy distribution: the energy accounting of α ripple (8Hz～13Hz), β ripple (13Hz～30Hz), γ ripple (30Hz～100Hz), θ ripple (4Hz～8Hz), δ ripple (<4Hz).
(2) power spectrum characteristic analysis: the frequency values that peak point is corresponding.
(3) focus of brain wave and the analysis of meditation degree: the absorbed degree of measurand and meditation depth survey etc.
As shown in table 1, taking a certain eeg data section of 3 tested individualities of difference as example, extract the principal character of its EEG signals.
Table 1 EEG signals principal character is extracted
| Characteristic quantity | Tested individual 1 | Tested individual 2 | Tested individual 3 |
| Low frequency α wave energy accounting | 56.69％ | 58.26％ | 53.93％ |
| High frequency alpha waves energy accounting | 10.99％ | 8.13％ | 15.64％ |
| Low frequency β wave energy accounting | 3.79％ | 3.66％ | 11.92％ |
| High frequency β wave energy accounting | 3.31％ | 1.80％ | 9.84％ |
| Low frequency γ wave energy accounting | 1.93％ | 1.50％ | 3.20％ |
| Intermediate frequency γ wave energy accounting | 1.40％ | 1.40％ | 3.15％ |
| θ wave energy accounting | 0.65％ | 0.90％ | 1.57％ |
| δ wave energy accounting | 21.24％ | 24.35％ | 0.75％ |
| Average absorbed index | 27.63 | 65.09 | 43.18 |
| Average meditation index | 38.00 | 44.77 | 41.73 |
Secondly,, based on the prestige theory of computation, calculate the credit value of each eeg data section by the intersegmental similarity of eeg data.As shown in Figure 6, the eeg data of a certain specific tested individuality is divided into 20 sections, and is numbered 1～20.According to the credit value of the each data segment of mean value calculation of the similarity of each data segment and other data segments.
It is 0.95 that thresholding credit value is set, and removes 18 the data segment of being numbered lower than thresholding credit value 0.95.Again choose data segment, then carry out prestige calculating, until each data segment credit value all exceedes thresholding credit value 0.95.
The theory of utilizing equally prestige to calculate, compare identical tested individual brain electrical feature similarity and different tested individual brain electrical feature similarity, as shown in Figure 7, obtain the eeg data similarity measurement after prestige is optimized, identical tested individual eeg data similarity is obviously better than different tested individual eeg data similaritys, and average prestige result of calculation exceeds approximately 5%.Therefore, the data segmentation method that the present invention proposes and the data segment accuracy evaluation calculating based on prestige and screening technique can improve the characteristic similarity of same tested individual eeg data, increase the difference of different tested individual EEG signals features and identical tested individual EEG signals feature, the input sample of Optimum Classification algorithm.
Wearable EEG signals process gathers, segmentation, and by the assessment screening of prestige result of calculation, for fuzzy support vector machine provides enough input samples.Selection of kernel function radial basis kernel function.The result of calculating in conjunction with prestige, determines degree of membership.Use fuzzy support vector machine while classifying, often use Gauss distribution to determine degree of membership, here using basic support vector machine sorting algorithm and conventional Gauss distribution fuzzy support vector classification algorithm as reference.Fig. 8 is than the brain electricity identification result of right support vector machine basic classification algorithm, Gauss distribution fuzzy support vector classification algorithm and prestige Optimization of Fuzzy support vector machine sorting algorithm.As shown in Figure 8, along with the increase of segments, three kinds of sorting techniques have all obtained more sufficient training sample, and accuracy of identification improves, and are greater than after 12 but work as segments, and accuracy of identification raising speed slows down.Use fuzzy support vector classification algorithm can utilize the character of algorithm itself to improve the accuracy of classified counting, and improved algorithm uses the result screening input training sample of prestige calculating, more scientific definite degree of membership, further improve again accuracy of identification, be better than two kinds of comparator algorithms, the highest discrimination has reached 91.98%.Especially in the time that input sample is less, because prestige calculating has been carried out assessment screening to brain electrical characteristic data section, significantly reduced the impact of error information section on recognition result in less input sample situation, therefore the raising of improved algorithm identified precision is more obvious.
Finally, adopt the identification sorting algorithm of EEG signals to identify the identity of tested individuality.
The various embodiments described above are only for illustrating the present invention; wherein the structure of each parts, connected mode and method step etc. all can change to some extent; every equivalents of carrying out on the basis of technical solution of the present invention and improvement, all should not get rid of outside protection scope of the present invention.
Claims (10)
1. an EEG signals identification system for network-oriented environment, is characterized in that: it comprises some wearable EEG measuring devices, more than one mobile network's terminal and an eeg data blood processor; Described wearable EEG measuring device transfers to described mobile network's terminal after the EEG Processing of the tested individuality measuring is become to eeg data, the eeg data that described mobile network's end-on is received is stored, and the eeg data of storage is transferred to described eeg data blood processor, described eeg data blood processor is processed rear output recognition result to the network eeg data receiving, and recognition result is transferred to described mobile network's terminal shows.
2. the EEG signals identification system of a kind of network-oriented environment as claimed in claim 1, is characterized in that: described wearable EEG measuring device comprises the dry electrode of some EEG measuring, an amplification filtering module, an analog-to-digital conversion module and a bluetooth serial ports module; The dry electrode of some described EEG measuring transfers to described amplification filtering module by wire by the brain electric analoging signal measuring, brain electric analoging signal after amplification filtering transfers to described analog-to-digital conversion module, and being converted to brain electricity digital signal waiting for transmission, brain electricity digital signal transfers to described mobile network's terminal through described bluetooth serial ports module.
3. the EEG signals identification system of a kind of network-oriented environment as claimed in claim 1, is characterized in that: described mobile network's terminal comprises bluetooth receiver module, data memory module, wireless network card and display module; Described bluetooth receiver module is stored brain electricity digital data transmission to the described data memory module receiving, the eeg data of described data memory module storage transfers to described eeg data blood processor by described wireless network card, described eeg data blood processor transfers to display module by recognition result by described wireless network card and shows after the eeg data receiving is processed.
4. the EEG signals identification system of a kind of network-oriented environment as claimed in claim 2, is characterized in that: described mobile network's terminal comprises bluetooth receiver module, data memory module, wireless network card and display module; Described bluetooth receiver module is stored brain electricity digital data transmission to the described data memory module receiving, the eeg data of described data memory module storage transfers to described eeg data blood processor by described wireless network card, described eeg data blood processor transfers to display module by recognition result by described wireless network card and shows after the eeg data receiving is processed.
5. the EEG signals identification system of a kind of network-oriented environment as claimed in claim 1 or 2 or 3 or 4, is characterized in that: described mobile network's terminal adopts the one in mobile phone, panel computer and notebook computer.
6. the EEG signals identification system of a kind of network-oriented environment as claimed in claim 1 or 2 or 3 or 4, is characterized in that: described eeg data blood processor comprises High_speed NIC, data sectional and characteristic extracting module, prestige computing module and identification module, the eeg data that described mobile network's terminal sends transfers to described data sectional and characteristic extracting module by described High_speed NIC, described data sectional and characteristic extracting module are carried out segment processing to the eeg data receiving and are extracted after brain electrical feature information, obtain brain electrical feature vector training sample and transfer to described prestige computing module, described prestige computing module carries out credit value assessment to the brain electrical feature sample receiving, filter out reliable brain electrical feature sample, the reliable brain electrical feature sample and the credit value thereof that filter out are transferred to described identification module by described prestige computing module, recognition result is identified and exported to described identification module to the identity of tested individuality, recognition result transfers to described mobile network's terminal by described High_speed NIC and shows.
7. an EEG signals personal identification method for the network-oriented environment of the identification system of employing as described in claim 1～6 any one, it comprises the following steps:
1) an EEG signals identification system that comprises the network-oriented environment of some wearable EEG measuring devices, more than one mobile network's terminal and an eeg data blood processor is set; Wherein, wearable EEG measuring device comprises the dry electrode of EEG measuring, amplification filtering module, analog-to-digital conversion module and bluetooth serial ports module; Mobile network's terminal comprises bluetooth receiver module, data memory module, wireless network card and display module; Eeg data blood processor comprises High_speed NIC, data sectional and characteristic extracting module, prestige computing module and identification module;
2) use the EEG signals of the tested individuality of the dry electrode pair of EEG measuring to carry out perception measurement, the brain electric analoging signal measuring is exported brain electricity digital signal after amplification filtering module and analog-to-digital conversion module processing, and brain electricity digital signal transfers to mobile network's terminal by bluetooth serial ports module;
3) in mobile network's terminal, bluetooth receiver module is by the brain electricity digital data transmission receiving to data memory module, and data memory module transfers to eeg data blood processor by the eeg data of storage by wireless network card;
4) eeg data blood processor is processed the EEG signals receiving, and completes the identification to tested individuality and exports recognition result, and it specifically comprises the following steps:
(1) data sectional and characteristic extracting module are carried out segment processing to the network eeg data receiving by High_speed NIC and are extracted after brain electrical feature information, and the brain electrical feature vector training sample set obtaining is transferred to prestige computing module;
(2) in prestige computing module, the brain electrical feature vector training sample that calculating receives is concentrated various kinds credit value originally, and prestige threshold value is set, brain electrical feature vector training sample is screened, and brain electrical feature vector training sample and credit value thereof through screening are transferred to identification module;
(3) according to the vector training sample of the brain electrical feature through screening and the credit value thereof that receive, identification module is determined input sample and the degree of membership parameter of fuzzy support vector machine, completes the identification to tested individuality and exports recognition result;
5) the identification result that identification module is exported feeds back to mobile network's terminal by High_speed NIC and shows.
8. the EEG signals personal identification method of a kind of network-oriented environment as claimed in claim 7, it is characterized in that: described step 4) in, data sectional and characteristic extracting module are carried out segment processing and are extracted brain electrical feature information the network eeg data receiving, and it specifically comprises the following steps:
The network eeg data of the is that (I) preset data segmentation and characteristic extracting module receive a tested individuality
for:
In formula, N
efor the data length of the original amplitude sequence of tested individual brain wave, i
s=1,2 ... N
s, N
sfor the number of tested individuality;
(II) data sectional and characteristic extracting module are by i
sthe network eeg data of individual tested individuality
be divided into N
isection is chosen at random starting point, to network eeg data in each decile eeg data section
carry out not decile segmentation, obtain N
dindividual not decile eeg data section, needs to meet following principle in fragmentation procedure:
First not the head end of decile eeg data section and last not the end of decile eeg data section be N with data length respectively
ethe head end of the original amplitude sequence of brain wave identical with end;
Each not length of decile eeg data section is greater than the length of decile eeg data section, and does not exceed the first and last end of the original amplitude sequence of brain wave;
(III) is respectively to N
sindividual tested individuality is the N after decile segmentation not
dindividual eeg data section is carried out feature extraction, Primary Construction brain electrical feature vector training sample set:
In formula,
represent i
sthe i of individual tested individuality
dsection brain electrical feature vector training sample,
represent the class label of this training sample, N
dfor the number of decile eeg data section not, N
sfor the number of tested individuality.
9. the EEG signals personal identification method of a kind of network-oriented environment as claimed in claim 7, it is characterized in that: described step 4) in, prestige computing module calculates brain electrical feature vector training sample and concentrates various kinds credit value originally, and by prestige threshold value is set, brain electrical feature vector training sample is screened, and it comprises the following steps:
(I) used correlation computations theory, measures identical tested individual i
sthe similarity of different brain electrical features vector training samples;
For same tested individual i
s, calculate i
d1with i
d2the similarity L of individual brain electrical feature vector training sample:
In formula,
with
represent respectively tested individual i
si
d1with i
d2individual brain electrical feature vector training sample;
(II), according to similarity assessment result, calculates i
sthe i of individual tested individuality
dsection brain electrical feature vector training sample
credit value
In formula, i
d=1,2...N
d, i
s=1,2...N
s, N
dfor the number of decile eeg data section not, N
sfor the number of tested individuality;
(III) arranges thresholding credit value in prestige computing module, by comparing credit value and the thresholding credit value of brain electrical feature vector training sample, completes the screening to brain electrical feature vector training sample, and it specifically comprises the following steps:
1. credit value and the thresholding credit value of each brain electrical feature vector training sample are compared, remove the brain electrical feature vector training sample of credit value lower than thresholding credit value, and the label of not decile eeg data section corresponding the brain electrical feature vector training sample of removal is fed back to data sectional and characteristic extracting module;
2. according to the label of the not decile eeg data section receiving, data sectional and characteristic extracting module are chosen starting point again from each decile eeg data section, network eeg data is carried out to not decile segmentation, and utilize the neencephalon electrical feature vector training sample that the new not decile eeg data section intercepting builds to replace the brain electrical feature vector training sample of credit value lower than thresholding credit value;
3. repeating step 1. with step 2., until the brain electrical feature that all structures obtain vector training sample meets the requirement of thresholding credit value, complete the screening to brain electrical feature vector training sample, and brain electrical feature vector training sample and credit value thereof through screening are transferred to identification module.
10. the EEG signals personal identification method of a kind of network-oriented environment as described in claim 7 or 8 or 9, it is characterized in that: described step 4) in, identification module completes the identification to tested individuality and exports recognition result, and it specifically comprises the following steps:
(I), according to the vector training sample of the brain electrical feature through screening and the credit value thereof that receive, identification module is determined degree of membership and is constructed prestige and optimize two class fuzzy support vector classification devices;
(II), according to two class fuzzy support vector classification devices of construction complete, solves two class classification problems; Adopt 1-a-r rule to construct multiple two class fuzzy support vector classification devices, complete the fuzzy support vector machine Multiclass Classification of calculating based on prestige;
(III) makes tested individuality complete EEG measuring task at a certain net environment, using the eeg data of the tested individuality of wearable EEG measuring measurement device as data to be identified, and pass through mobile network's terminal transmission to eeg data blood processor, adopt fuzzy support vector machine Multiclass Classification in identification module, the identity of tested individuality is identified.
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