CN106901727A - A kind of depression Risk Screening device based on EEG signals - Google Patents
A kind of depression Risk Screening device based on EEG signals Download PDFInfo
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- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
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- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
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- A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
- A61B5/165—Evaluating the state of mind, e.g. depression, anxiety
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
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Abstract
The invention discloses a kind of depression Risk Screening device based on EEG signals, belong to depression Risk Screening field；Including EEG signals central processing unit and detection means；Also include generalization brain electric data collecting system, EEG signals pretreatment system, examination depression risk brain electric system, brain electricity display module and result print module；Generalization brain electric data collecting system includes Fp1 electrode sensors, FpZ electrode sensors, Fp2 electrode sensors, preamplifier, 50Hz trappers and low pass filter；It realizes that individual consumer can dress detection means, user is facilitated to test oneself, automatic detection brain noise, removal eye electricity artefact, reduce the interference between various noises, eeg data is directly processed, sleep stage step is skipped, reduced in the calculation error produced by the steps such as sleep stage, improve the degree of accuracy of depression risk assessment, collection contains much information, and is sufficiently used the data message of sleep cerebral electricity, carries out selectivity during Feature Selection strong.
Description
Technical field
The present invention relates to depression Risk Screening device field, more specifically to a kind of suppression based on EEG signals
Strongly fragrant disease Risk Screening device.
Background technology
Depression is a kind of common mental illness, low and pessimistic as principal character with mental state, may during serious symptom
Suicide can be produced.Sorted out and divided in the illness to depression, background, diagnosis basis, instrument and evaluation criteria herein
On the basis of analysis, not only number is numerous to find patients with depression, and age of onset, scope and industry are also progressively expanding, and give
Society and family bring white elephant.However, the personnel for being engaged in Diagnosis of Depression and evaluation work are deficienter, and it is examined
The subjective factor that disconnected and assessment is relied on is more, easily causes mistaken diagnosis and fails to pinpoint a disease in diagnosis.Thus, the accuracy in the urgent need to improving its diagnosis
And efficiency.Synthesis and analysis hair that the present invention passes through the present Research to depression, analysis method, means and on going result
Existing, this disease diagnosis is inaccurate and a low major reason of efficiency is the absence of the biological diagnosis index of objective quantification, scientific and effective
Diagnostic model and an accurate risk screening plant.Electrical activity of brain is the overall macroscopic view of the various electrical activities of a large amount of brain cell groups
Comprehensive effect.There is close relationship with cerebral function state in its active degree and region, thus, also represent one kind of brain
State, can reflect and characterize the disease and health condition of brain;The depressed disease Risk Screening diagnosis for being currently based on brain electricity is main
It is based on sleep stage technology.On sleep stage, once there are several division methods.What is be widely used at present is 1968,
Rechtschaffen and Kales, in the wave character of different sleep periods, adult's sleep is divided according to EEG, EOG, EMG signal
Into six phases：Lucid interval, non phase（It was further divided into for 1,2,3,4 phases）, and rapid eye movement phase, i.e. staging scale（Such as
Fig. 9）.Calculate Sleep latency, sleep total time, awakening index, 1 phase of sleep, Sleep Stage 2,3 phases of sleep, 4 phases of sleep, quick
The dynamic percentage of eye, rapid-eye-movement sleep periodicity, rapid-eye-movement sleep incubation period, rapid-eye-movement sleep intensity, rapid eye movement are slept
The feature such as dormancy density and rapid-eye-movement sleep time, carries it into grader and carries out depression risk judgment.
Existing examination depression risk device and acquisition method have more defect, and it is essentially consisted in：
1. traditional electroencephalogram technology applied in stricter environment mostly, such as the clinical treatment of hospital or scientific research institutions
Laboratory, it has not been convenient to which individual consumer uses, and these application scenarios have preferable experiment condition：On the one hand, these environment are all
Special physical isolation was done, there is good electromagnetic shielding and sound shielding；On the other hand, there is the operation trained of specialty
Personnel, even under so strict limitation, the measurement of EEG signals can also be influenceed by some factors.
2. sleep stage standard differs, and the accuracy rate of sleep stage is not high enough, makes depression Risk Screening system accuracies
It is relatively low.
3. information content is smaller, and feature is not very clear and definite, can not obtain the feature accordingly for subject
Information provides the depression Risk Results of rationalization.
The content of the invention
1. the technical problem to be solved
Used for inconvenient individual consumer present in prior art, disturbing factor Interference Detection, accuracy in detection is low, brain electricity
The information content minor issue of collection, it is an object of the invention to provide a kind of depression Risk Screening device based on EEG signals,
It can realize facilitating individual consumer to use, and reduce the interference between various noises, improve accuracy in detection, the sleep of collection
Brain is electric to contain much information.
2. technical scheme
A kind of depression Risk Screening device based on EEG signals, including EEG signals central processing unit and detection means；
Described EEG signals central processing unit includes preamplifier, 50Hz trappers, low pass filter, converter, brain electricity
Signal processor, impedance detector, direct current rectifier and the RF transceiver of bluetooth 2.0；Described detection means includes Fp1 electrodes
Sensor, FpZ electrode sensors, Fp2 electrode sensors, reference electrode and switch；Described detection means is connected by wire
To in EEG signals central processing unit；Described wire includes wire a, wire b and wire c；Described wire c is connected to out
Shut；Described EEG Processing device also includes generalization brain electric data collecting system, EEG signals pretreatment system, sieve
Look into depression risk brain electric system, brain electricity display module and result print module；Generalization brain electric data collecting system includes Fp1
Electrode sensor, FpZ electrode sensors, Fp2 electrode sensors, preamplifier, 50Hz trappers and low pass filter, Fp1
Electrode sensor, FpZ electrode sensors and Fp2 electrode sensors are connected with preamplifier；Preamplifier, 50Hz trappers
It is sequentially connected with low pass filter；The EEG signals pretreatment system includes converter, EEG Processing device, impedance inspection
Device, direct current rectifier and the RF transceiver of bluetooth 2.0 are surveyed, the converter, EEG Processing device and the radio frequency of bluetooth 2.0 are received
Hair device is sequentially connected；Described EEG Processing device is electrically connected to impedance detector, and impedance detector is connected by wire a
To in detection means；Described EEG Processing device is electrically connected in preamplifier by direct current rectifier；The sieve
Looking into depression risk brain electric system includes brain electricity display module one, management module, EEG signals after-treatment module, brain electricity display
Module two and EEG signals data screening system, the brain electricity display module one, management module, EEG signals after-treatment mould
Block, brain electricity display module two, EEG signals data screening system are sequentially connected, and the EEG signals data screening system includes spy
Levy extraction module, feature selection module, disaggregated model module and depression Risk Screening module；Present apparatus individual consumer can be straight
Wearing is connect, user friendly to test oneself, EEG signals pretreatment system being capable of automatic detection brain noise, removal eye electricity artefact, drop
Interference between low various noises, directly processes eeg data, skips sleep stage step, reduces in steps such as sleep stages
Produced calculation error, improves the degree of accuracy of depression risk assessment, and collection contains much information, EEG signals data examination system
The data message that system is sufficiently used sleep cerebral electricity carries out Feature Selection, and selectivity is strong.
Preferably, the examination depression risk brain electric system also includes data acquisition modeling, off-line analysis module
And DBM, data acquisition modeling produces rational physical signs parameter, after off-line analysis module is to pretreatment
The EEG signals for obtaining carry out after-treatment, and DBM was used to store by crowd's essential information of label and the last time
Eeg data when undergoing training.
Preferably, the EEG signals pretreatment system uses AR models and adaptive predictor model method, by AR
Model method carrys out automatic detection brain noise, and eye electricity artefact is removed by adaptive predictor model.
Preferably, the data acquisition modeling passes through generalization eeg collection system respectively to the Healthy People of label
The depression crowd of group and label carries out brain electric data collecting, is easy to gather rational physical signs parameter.
Preferably, described wire a include three electric wires be connected respectively to Fp1 electrode sensors, FpZ electrode sensors and
Fp2 electrode sensors；Described wire b is made up of two electric wires；The two-stage of reference electrode is connected to EEG signals by wire b
On BIAS mouthfuls of central processing unit and com port.
Preferably, described detection means is helmet-like, and centre is provided with sound insulation by filling layer, and described sound insulation by filling layer is soft
Matter acoustic material is made, and detection means front end is provided with eyeshade, and both sides are provided with soft earplug；Described switch is in soft ear
Plug bottom；Individual consumer is ensured a comfortable environment of peace and quiet whenever and wherever possible, and be adapted to using filled type
Different crowds are conveniently adjusted size, and comfort level can be ensured using soft acoustic material, it is also possible to reduce outer bound pair electrode and pass
The influence of sensor；The Fp1 electrode sensors are arranged on left front volume point, and Fp2 electrode sensors are arranged on right forehead point, FpZ electricity
Pole sensor is arranged on forehead center point, and described reference electrode is arranged on hindbrain position；Described Fp1 electrode sensors, FpZ
Electrode sensor, Fp2 electrode sensors and reference electrode electrode tip use medical mounted wet electrode head, medical mounted wet electrode
Head avoids the interference of electrode contact impedance.
Preferably, the characteristic extracting module uses nonlinear dynamics theory, the feature selection module and classification mould
Pattern block uses SVMs, and nonlinear dynamics theory is analyzed to EEG signals, extracts the non-of EEG signals
Linear character, SVMs is selected and classified to eeg data feature.
3. beneficial effect
Compared to prior art, the advantage of the invention is that：
（1）Present apparatus individual consumer can directly be dressed, user friendly to test oneself, and EEG signals pretreatment system can be examined automatically
Brain noise is surveyed, removal eye electricity artefact reduces the interference between various noises, directly processes eeg data, skips sleep stage
Step, reduces in the calculation error produced by the steps such as sleep stage, improves the degree of accuracy of depression risk assessment, collection
Contain much information, the data message that EEG signals data screening system is sufficiently used sleep cerebral electricity carries out Feature Selection, select
Property it is strong, cause that dc source reaches predetermined test function by impedance detector and direct current rectifier.
（2）Data acquisition modeling produces rational physical signs parameter, and off-line analysis module after pretreatment to obtaining
To EEG signals carry out after-treatment, DBM is used to store to be received by crowd's essential information of label and the last time
Eeg data during instruction.
（3）By AR model methods come automatic detection brain noise, eye electricity is removed by adaptive predictor model pseudo-
Mark.
（4）By generalization eeg collection system healthy population respectively to label and the depression crowd of label has entered
Row brain electric data collecting, is easy to gather rational physical signs parameter.
（5）Different crowds are adapted to using filled type and are conveniently adjusted size, can ensured using soft acoustic material
Comfort level, it is also possible to reduce the influence of outer bound pair electrode sensor, medical mounted wet electrode head avoids electrode contact impedance
Interference.
（6）Nonlinear dynamics theory is analyzed to EEG signals, extracts the nonlinear characteristic of EEG signals, supports
Vector machine is selected and classified to eeg data feature.
Brief description of the drawings
Fig. 1 is structural representation of the invention；
Fig. 2 is present invention entirety examination system block diagram
Fig. 3 is generalization eeg collection system block diagram of the invention；
Fig. 4 is adaptive prediction phase of the invention removal eye electricity artefact exemplary block diagram；
Fig. 5 is examination depression risk brain electric system block diagram of the invention；
Fig. 6 is EEG signals data screening system block diagram of the invention；
Fig. 7 is data acquisition modeling block diagram of the invention；
Fig. 8 behaviour brain system electrode position figures；
Fig. 9 is sleep stage structure chart；
Figure 10 is detection means top view of the invention.
Label declaration in figure：1st, EEG signals central processing unit；2nd, detection means；3rd, preamplifier；4th, 50Hz falls into
Ripple device；5th, low pass filter；6th, converter；7th, EEG Processing device；8th, impedance detector；9th, direct current rectifier；10th, it is blue
The RF transceiver of tooth 2.0；11st, Fp1 electrode sensors；12nd, FpZ electrode sensors；13rd, Fp2 electrode sensors；14th, with reference to electricity
Pole；15th, wire；16th, wire a；17th, wire b；18th, switch；19th, sound insulation by filling layer；20th, eyeshade；21st, soft earplug；22nd, lead
Line c.
Specific embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention；Technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation is described；Obviously；Described embodiment is only a part of embodiment of the invention；Rather than whole embodiments.It is based on
Embodiment in the present invention；It is every other that those of ordinary skill in the art are obtained under the premise of creative work is not made
Embodiment；Belong to the scope of protection of the invention.
Embodiment 1：
Refer to Fig. 1-10, a kind of depression Risk Screening device based on EEG signals, including EEG signals center processing dress
Put 1 and detection means 2；EEG signals central processing unit 1 include preamplifier 3,50Hz trappers 4, low pass filter 5,
Converter 6, EEG Processing device 7, impedance detector 8, direct current rectifier 9 and the RF transceiver 10 of bluetooth 2.0；Detection dress
Putting 2 includes Fp1 electrode sensors 11, FpZ electrode sensors 12, Fp2 electrode sensors 13, reference electrode 14 and switch 18；Inspection
Device 2 is surveyed to be connected in EEG signals central processing unit 1 by wire 15；Wire 15 includes wire a16, wire b17 and leads
Line c22；Wire c22 is connected on switch 18；EEG Processing device 1 also includes generalization brain electric data collecting system, brain
Electric signal pretreatment system, examination depression risk brain electric system, brain electricity display module and result print module；Generalization brain electricity
Data collecting system include Fp1 electrode sensors 11, FpZ electrode sensors 12, Fp2 electrode sensors 13, preamplifier 3,
50Hz trappers 4 and low pass filter 5, Fp1 electrode sensors 11, FpZ electrode sensors 12 and Fp2 electrode sensors 13 with it is preceding
Amplifier 3 is put to be connected；Preamplifier 3,50Hz trappers and low pass filter are sequentially connected；The EEG signals pretreatment system
System includes converter 6, EEG Processing device 7, impedance detector 8, direct current rectifier 9 and the RF transceiver 10 of bluetooth 2.0,
Converter 6, EEG Processing device 7 and the RF transceiver 10 of bluetooth 2.0 are sequentially connected；EEG Processing device 7 electrically connects
Impedance detector 8 is connected to, impedance detector 8 is connected in detection means 2 by wire a16；EEG Processing device 7 is by straight
Stream rectifier 9 is electrically connected in preamplifier 3；Wire a16 is connected respectively to Fp1 electrode sensors including three electric wires
11st, FpZ electrode sensors 12 and Fp2 electrode sensors 13；Described wire b17 is made up of two electric wires；Reference electrode 14
Two-stage is connected on the BIAS of EEG signals central processing unit 1 mouthfuls and com port by wire b17.
Detection means 2 is helmet-like, and centre is provided with sound insulation by filling layer 19, and sound insulation by filling layer 19 makes for soft acoustic material
Form, the front end of detection means 2 is provided with eyeshade 20, both sides are provided with soft earplug 21；Switch 18 is in the bottom of soft earplug 21；So
Individual consumer can be made to ensure a comfortable environment of peace and quiet whenever and wherever possible, and different crowds are adapted to just using filled type
In being sized, comfort level can be ensured using soft acoustic material, it is also possible to reduce the influence of outer bound pair electrode sensor；
Fp1 electrode sensors 11 are arranged on left front volume point, and Fp2 electrode sensors 13 are arranged on right forehead point, and FpZ electrode sensors 12 set
Put in forehead center point, reference electrode 14 is arranged on hindbrain position；Fp1 electrode sensors 11, FpZ electrode sensors 12, Fp2 electricity
Pole sensor 13 and the electrode tip of reference electrode 14 use medical mounted wet electrode head, medical mounted wet electrode head to avoid electrode
The interference of contact impedance.
EEG signals pretreatment system uses AR models and adaptive predictor model method, is come from by AR model methods
Dynamic detection brain noise, the brain noise that will be detected by being sent to adaptive predictor after filter initialization, while adaptive
Fallout predictor is answered to receive the EEG signals from eye electricity region, adaptive predictor is simultaneously to brain noise and eye electricity region brain telecommunications
Number processed, eye electricity artefact and removal brain noise, EEG signals pretreatment system are removed by adaptive predictor model
System being capable of automatic detection brain noise, removal eye electricity artefact, the interference between the various noises of reduction, EEG signals pretreatment system
Eeg data is directly processed, sleep stage step is skipped, reduced in the calculation error produced by the steps such as sleep stage, improved
The degree of accuracy of depression risk assessment.
Examination depression risk brain electric system includes brain electricity display module one, management module, EEG signals after-treatment mould
Block, brain electricity display module two and EEG signals data screening system, brain electricity display module one, management module, EEG signals are secondary
Processing module, brain electricity display module two, EEG signals data screening system are sequentially connected, and EEG signals after-treatment module passes through
EEG signals to being obtained after pretreatment carry out after-treatment, to obtain the index such as EEG power spectrum array of figure and brain electricity θ, α, β
Histogrammic Real time dynamic display, realizes brain electricity index real-time monitoring, and doctor passes through function Real Time Observation patient's brain electric information
Indices change, and intuitively, grasp patient's cerebral functional lateralitv exactly, are used to monitor the effect of feedback treating, the brain telecommunications
Number screening system includes characteristic extracting module, feature selection module, disaggregated model module and depression Risk Screening module,
Characteristic extracting module uses nonlinear dynamics theory, the feature selection module and disaggregated model module to use supporting vector
Machine, nonlinear dynamics theory is analyzed to EEG signals, extracts the nonlinear characteristic of EEG signals, SVMs pair
Eeg data feature is selected and classified, and collection contains much information, and EEG signals data screening system is sufficiently used sleep
The data message of brain electricity carries out Feature Selection, and selectivity is strong.
Examination depression risk brain electric system also includes data acquisition modeling, off-line analysis module and database mould
Block, management module is managed to examination process and subject information, including subject information establishment, inquire about, change, deleting
Remove, and diagnose the selection of examination scheme, the storage of the index and form and data required by examination depression scheme is set,
The examination process and result of DBM storage examination scheme, the essential information of subject and system, so that management system is entered
Row inquiry and management, data acquisition modeling produces rational physical signs parameter, after off-line analysis module is to pretreatment
The EEG signals for obtaining carry out after-treatment, and DBM was used to store by crowd's essential information of label and the last time
Eeg data when undergoing training, the data acquisition modeling pre-processes system by generalization eeg collection system, eeg data
System, eeg data secondary treatment system, characteristic extracting module, feature selection module and disaggregated model module are respectively to label
The depression crowd of healthy population and label carries out brain electric data collecting, is easy to gather rational physical signs parameter, completely newly
Data model, classified in systems according to different syndromes, its data structure is simple, clear, there is good Dynamic data exchange
Property, level security, user is understandable easy-to-use, can carry out cross validation with data with existing, improve the accuracy of sieving and diagnosis with
Specific aim, brain electricity display module is to make the AEEG by the person of trying for receiving feedback training, is by the brain electricity of subject
The display of signal waveform dynamic realtime ground over the display, realizes electroencephalogram and is dynamically traced without pen.
Operation principle：Subject first registers the information of oneself, numbering, name, sex, age including subject etc.
Information, detection means is worn over the head of subject, opens switch, and detection means is opened automatically according to the head size of subject
Packed layer is opened, eyeshade 20 and soft earplug 21 are attached on the eyes and ear of subject automatically, make subject in a relative peace
Sleep state is progressed into quiet environment, real-time eeg recording is opened, the EEG signals of patient pass through Fp1 electrode sensors
11st, FpZ electrode sensors 12 and Fp2 electrode sensors 13 extract, and faint EEG signals are entered by preamplifier 3
Row amplifies, while original EEG signals are carried out with power frequency filtering process, the signal of amplification is converted into by 16 A/D converters 6
Data signal, by calling EEG signals pretreatment system to pre-process EEG signals to reduce the interference of artifact, it is ensured that
Correctness during feedback treating, is then transferred to computer by bluetooth 2.0, is then displayed on screen in real time.Doctor calls
Corresponding signal handler carries out after-treatment to the signal for gathering in brain electricity analytical, using nonlinear dynamics theory to brain
Electric signal is analyzed, and extracts the nonlinear characteristic of EEG signals, obtains the characteristic value of feedback information, by the characteristic value of information
Bring SVMs into, carry out Risk Screening, final output result, during systematic training, system can automatically patient
EEG signals store, doctor can subject finish training after after, play back the EEG signals and be analyzed, beat
Print, is used to monitor the therapeutic process of patient.
The above；Preferably specific embodiment only of the invention；But protection scope of the present invention is not limited thereto；
Any one skilled in the art the invention discloses technical scope in；Technology according to the present invention scheme and its
Improve design and be subject to equivalent or change；Should all cover within the scope of the present invention.
Claims (7)
1. a kind of depression Risk Screening device based on EEG signals, it is characterised in that：Including EEG signals center processing dress
Put（1）And detection means（2）；Described EEG signals central processing unit（1）Including preamplifier（3）, 50Hz trappers
（4）, low pass filter（5）, converter（6）, EEG Processing device（7）, impedance detector（8）, direct current rectifier（9）With
The RF transceiver of bluetooth 2.0（10）；Described detection means（2）Including Fp1 electrode sensors（11）, FpZ electrode sensors
（12）, Fp2 electrode sensors（13）, reference electrode（14）And switch（18）；Described detection means（2）By wire（15）Even
It is connected to EEG signals central processing unit（1）In；Described wire（15）Including wire a（16）, wire b（17）With wire c
（22）；Described wire c（22）It is connected to switch（18）In；Described EEG Processing device（1）Including generalization brain electricity
Data collecting system, EEG signals pretreatment system, examination depression risk brain electric system, brain electricity display module and result printing
Module；Described generalization brain electric data collecting system includes Fp1 electrode sensors（11）, FpZ electrode sensors（12）、Fp2
Electrode sensor（13）, preamplifier（3）, 50Hz trappers（4）And low pass filter（5）, Fp1 electrode sensors（11）、
FpZ electrode sensors（12）With Fp2 electrode sensors（13）With preamplifier（3）It is connected；Preamplifier（3）, 50Hz fall into
Ripple device（4）And low pass filter（5）It is sequentially connected；The EEG signals pretreatment system includes converter（6）, EEG signals
Processor（7）, impedance detector（8）, direct current rectifier（9）With the RF transceiver of bluetooth 2.0（10）, the converter（6）、
EEG Processing device（7）With the RF transceiver of bluetooth 2.0（10）It is sequentially connected；Described EEG Processing device（7）Electrically
It is connected to impedance detector（8）, impedance detector（8）By wire a（16）It is connected to detection means（2）In；Described brain electricity
Signal processor（7）By direct current rectifier（9）It is electrically connected to preamplifier（3）In；The examination depression risk brain
Electric system includes brain electricity display module one, management module, EEG signals after-treatment module, brain electricity display module two and brain telecommunications
Number screening system, the brain electricity display module one, management module, EEG signals after-treatment module, brain electricity display module
2nd, EEG signals data screening system is sequentially connected, and the EEG signals data screening system includes characteristic extracting module, feature
Selecting module, disaggregated model module and depression Risk Screening module.
2. a kind of depression Risk Screening device based on EEG signals according to claim 1, it is characterised in that：It is described
Examination depression risk brain electric system also includes data acquisition modeling, off-line analysis module and DBM.
3. a kind of depression Risk Screening device based on EEG signals according to claim 1, it is characterised in that：It is described
EEG signals pretreatment system uses AR models and adaptive predictor model method.
4. a kind of depression Risk Screening device based on EEG signals according to claim 2, it is characterised in that：It is described
Data acquisition modeling is by generalization eeg collection system healthy population respectively to label and the depression of label
Crowd carries out brain electric data collecting.
5. a kind of depression Risk Screening device based on EEG signals according to claim 1, it is characterised in that：It is described
Wire a（16）Fp1 electrode sensors are connected respectively to including three electric wires（11）, FpZ electrode sensors（12）With Fp2 electrodes
Sensor（13）；Described wire b（17）It is made up of two root electric wires；Reference electrode（14）Two-stage pass through wire b（17）Even
It is connected on EEG signals central processing unit（1）BIAS mouthfuls and com port on.
6. a kind of depression Risk Screening device based on EEG signals according to claim 1, it is characterised in that：It is described
Detection means（2）It is helmet-like, centre is provided with sound insulation by filling layer（19）, described sound insulation by filling layer（19）It is soft sound insulation material
Material is made, detection means（2）Front end is provided with eyeshade（20）, both sides are provided with soft earplug（31）；Described switch（18）Place
In soft earplug（31）Bottom；The Fp1 electrode sensors（11）It is arranged on left front volume point, Fp2 electrode sensors（13）Set
In right forehead point, FpZ electrode sensors（12）It is arranged on forehead center point, described reference electrode（14）It is arranged on back brain
Position；Described Fp1 electrode sensors（11）, FpZ electrode sensors（12）, Fp2 electrode sensors（13）And reference electrode（14）
Electrode tip uses medical mounted wet electrode head.
7. a kind of depression Risk Screening device based on EEG signals according to claim 1, it is characterised in that：It is described
Characteristic extracting module uses nonlinear dynamics theory, the feature selection module and disaggregated model module to use supporting vector
Machine.
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Cited By (8)
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| CN107788976A (en) * | 2017-09-22 | 2018-03-13 | 复旦大学 | Sleep monitor system based on Amplitude integrated electroencephalogram |
| CN107865638A (en) * | 2017-09-21 | 2018-04-03 | 广东思派康电子科技有限公司 | Computer-readable recording medium, built-in earplug detection means |
| CN109363670A (en) * | 2018-11-13 | 2019-02-22 | 杭州电子科技大学 | A kind of depression intelligent detecting method based on sleep monitor |
| CN109646021A (en) * | 2018-12-28 | 2019-04-19 | 浙江大学 | A kind of psychological research auxiliary headset equipment and its data analysing method |
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