CN103816007B - A kind of tinnitus treatment Apparatus and method for based on brain electricity frequency domain character indexing algorithm - Google Patents
A kind of tinnitus treatment Apparatus and method for based on brain electricity frequency domain character indexing algorithm Download PDFInfo
- Publication number
- CN103816007B CN103816007B CN201310596284.0A CN201310596284A CN103816007B CN 103816007 B CN103816007 B CN 103816007B CN 201310596284 A CN201310596284 A CN 201310596284A CN 103816007 B CN103816007 B CN 103816007B
- Authority
- CN
- China
- Prior art keywords
- signal
- index
- eeg
- circuit
- time
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
- 210000004556 brain Anatomy 0.000 title claims abstract description 35
- 238000000034 method Methods 0.000 title claims abstract description 29
- 230000005611 electricity Effects 0.000 title claims abstract description 22
- 208000009205 Tinnitus Diseases 0.000 title claims abstract description 19
- 238000004422 calculation algorithm Methods 0.000 title claims abstract description 19
- 231100000886 tinnitus Toxicity 0.000 title claims abstract description 19
- 230000008569 process Effects 0.000 claims abstract description 14
- 238000000605 extraction Methods 0.000 claims abstract description 7
- 238000007781 pre-processing Methods 0.000 claims abstract description 4
- 238000011002 quantification Methods 0.000 claims abstract description 4
- 238000004088 simulation Methods 0.000 claims abstract description 4
- 230000001629 suppression Effects 0.000 claims abstract description 4
- 238000004458 analytical method Methods 0.000 claims description 8
- 230000006998 cognitive state Effects 0.000 claims description 6
- 239000000284 extract Substances 0.000 claims description 6
- 238000009825 accumulation Methods 0.000 claims description 5
- 238000001914 filtration Methods 0.000 claims description 5
- 230000036626 alertness Effects 0.000 claims description 4
- 230000008859 change Effects 0.000 claims description 4
- 238000005516 engineering process Methods 0.000 claims description 4
- 206010041349 Somnolence Diseases 0.000 claims description 3
- 230000005764 inhibitory process Effects 0.000 claims description 3
- 230000002452 interceptive effect Effects 0.000 claims description 3
- 230000004044 response Effects 0.000 claims description 3
- 230000008054 signal transmission Effects 0.000 claims description 2
- 230000000694 effects Effects 0.000 abstract description 3
- 230000006870 function Effects 0.000 description 8
- 238000010606 normalization Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 230000036962 time dependent Effects 0.000 description 3
- 230000015572 biosynthetic process Effects 0.000 description 2
- 201000010099 disease Diseases 0.000 description 2
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 238000002372 labelling Methods 0.000 description 2
- 230000033764 rhythmic process Effects 0.000 description 2
- 238000003786 synthesis reaction Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000002490 cerebral effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000002060 circadian Effects 0.000 description 1
- 230000001149 cognitive effect Effects 0.000 description 1
- 230000008034 disappearance Effects 0.000 description 1
- 230000035479 physiological effects, processes and functions Effects 0.000 description 1
- 230000002459 sustained effect Effects 0.000 description 1
- 238000005303 weighing Methods 0.000 description 1
Landscapes
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
The present invention relates to a kind of tinnitus treatment Apparatus and method for based on brain electricity frequency domain character indexing algorithm.Comprise earphone, eeg signal acquisition device, IC circuit, signal transmitting apparatus and terminal unit, eeg signal acquisition device is sampled to the simulation brain wave that people's cerebration produces, is quantized, and becomes discrete digital signal, carries out follow-up process; The digital signal of quantification amplifies by described IC circuit, the capacity of resisting disturbance in enhancement process and transmitting procedure; Described signal processor carries out noise suppression preprocessing to the signal gathered, and strengthens the intensity of eeg signal, and described signal transmitting apparatus connects IC circuit and terminal is established, and the parameter of IC circuit extraction is transferred to terminal unit; Described terminal unit is a PC, processes above-mentioned parameter, and carries out showing and feeding back.The present invention objectively can judge the effect for the treatment of according to the brain of patient electricity, accuracy is high, without advantages such as patient's subjective judgment.Both can be used for hospital, also may be used for family.
Description
Technical field
The present invention relates to a kind of tinnitus treatment Apparatus and method for based on brain electricity frequency domain character indexing algorithm.
Background technology
Tinnitus is common clinical, frequently-occurring disease, and its cause of disease does not have clear and definite conclusion at present.The treatment means of usual employing sound mask, namely selects the sound of different frequency to listen to patient, and can that sound make it feel tinnitus relief or disappearance to allow it feel, then listened to patient by this sound go, the object having reached treatment or alleviated.This method felt by patient is not objective, poor accuracy.
Summary of the invention
Object of the present invention, provides a kind of tinnitus treatment equipment based on brain electricity frequency domain character indexing algorithm, objectively to judge, to assess tinnitus treatment effect.
Technical scheme of the present invention is as follows:
Based on a tinnitus treatment equipment for brain electricity frequency domain character indexing algorithm, described equipment comprises earphone, eeg signal acquisition device, IC circuit, signal transmitting apparatus and terminal unit, it is characterized in that:
Eeg signal acquisition device comprises eeg signal acquisition electrode, EEG signals reference electrode and signal processor, and described harvester is sampled to the simulation brain wave that people's cerebration produces, quantized, and becomes discrete digital signal, carries out follow-up process;
Described IC circuit comprises signal amplifier and signal processor, and the digital signal of quantification for for signal preamplifier, amplifies by described signal amplifier, the capacity of resisting disturbance in enhancement process and transmitting procedure;
Described signal processor carries out noise suppression preprocessing to the signal gathered, and strengthens the intensity of eeg signal, and therefrom extracts the parameter of reflection people cognitive state change, the state of assessment user;
Described signal transmitting apparatus connects IC circuit and terminal is established, and the parameter of IC circuit extraction is transferred to terminal unit;
Described terminal unit is a PC, processes above-mentioned parameter, and carries out showing and feeding back.
Further, described earphone is headband receiver, eeg signal acquisition device and IC circuit integrated in headset body, and be integrated with battery compartment and on and off switch further.
Further, described eeg signal acquisition electrode is positioned at head, and EEG signals reference electrode clamp is positioned at ear.
Further, described signal transmitting apparatus is wireless signal transmission.
Based on a tinnitus treatment method for brain electricity frequency domain character indexing algorithm, it is characterized in that, described method, described method step is as follows:
(1) initialization apparatus hardware, setting can acceptable conditions;
(2) sound source automatically-broadcasting voice, gathers eeg data simultaneously;
(3) to the data filtering gathered, denoising, time frequency analysis, calculating brain electricity index;
(4) brain of calculating electricity index is transferred to client and shows;
(5) judge whether to meet acceptable conditions; If met, then can accept labelling to the sound setting of playing; If do not met, then again carry out correlation step to step (2).
Further, described indexing algorithm is specific as follows:
(1) pretreatment: carry out digital filtering to the brain wave quantized, removes the interfering noises such as myoelectricity; Described wave filter is infinite-duration impulse response (IIR) band filter;
(2) feature representation and extraction: the basic index extracting reflection general cognitive state from the EEG signals after pretreatment, specifically comprises alpha ripple (8-13Hz), beta ripple (13-20Hz), delta(1-4Hz), theta(4-7Hz); Use time-frequency analysis technology these indexs to be extracted from original time domain signal, frequency domain is expressed with the time series form of energy or power;
(3) indexing represents: above-mentioned basic index is carried out standardization, makes the same index of different users and same user different time have identical physical meaning; Described algorithm exports Vigilance level and the horizontal two indices of tensity, the horizontal index of described Vigilance and and the horizontal index of tensity specific as follows:
A (t), b (t) and c (t) represent the clock signal of alpha, beta and theta tri-frequency ranges respectively, and they are respectively by the energy accumulation realization also selecting special frequency channel via time frequency analysis of original EEG signals;
Alertness index:
S1 (t)=c (t)/a (t), wherein t express time, a and c represents the energy of alpha and theta respectively;
Tensity index:
S2 (t)=b (t) * c (t), wherein t express time, b and c represents the energy of beta and theta respectively;
(4) judgement of attention level: namely with normal user when not having sleepy, fatigue state to occur, continue to keep the horizontal index of the attention Vigilance of 2 minutes and and the horizontal index of tensity two index series sequential average 60% as decision threshold, be tired generation lower than this threshold judgement, show that the sound now produced by earphone well restrains tinnitus, then show that the sound that earphone produces is undesirable to tinnitus inhibition lower than threshold value.
Further, in described step (1), the low pass initial frequency of band filter is 1Hz, and high pass cut off frequency is 35Hz.
Further, the specific algorithm of described step (2) feature representation submodule is as follows:
Adopt Morlat function to be mother wavelet function, continuous wavelet transform is carried out to brain electricity time-domain signal; Input signal is the discrete brain electricity time series that brain wave acquisition arrives of singly leading, after converting, multiple time series signal and the wavelet coefficient of a series of different frequency range is obtained with above mother wavelet function convolution, wherein time and input signal length are consistent, frequency range to being 1-35Hz, retain wherein 1-35Hz for extracting prosodic feature; For specific moment and frequency, coefficient represents the time-frequency distributions situation of signal, to its delivery, namely represents with power; According to band limits height, extract alpha ripple (8-13Hz), beta ripple (13-20Hz) respectively, delta(1-4Hz), theta(4-7Hz) time series of mould of Phase information coefficient of corresponding frequency band, namely power represents the timing variations of band energy.
Further, described step (3) indexing represents employing feature normalization algorithm, that is:
A certain band energy is accounted for the ratio of gross energy as index:
Wherein, t express time, f represents frequency, and P represents power, thus P
ft () represents the time dependent function of energy within the scope of a certain frequency f, the denominator part of formula then represents the energy accumulation summation in 1 to 35Hz frequency range; According to above model by P
ft (), divided by after gross energy normalization, the relative energy of each frequency range becomes the numerical value within the scope of 0-1, namely uses P
f 't () represents.
Beneficial effect of the present invention is:
The present invention is based on patient's attention index when brain electricity frequency domain character indexing algorithm calculates tinnitus treatment, thus objectively can judge the effect for the treatment of according to the brain electricity of patient, have accuracy high, without advantages such as patient's subjective judgment.Both can be used for hospital, also may be used for family.
Accompanying drawing explanation
Fig. 1 is device structure schematic diagram of the present invention.
Fig. 2 is method flow schematic diagram of the present invention.
Wherein, 1-earphone, 2-eeg signal acquisition electrode, 3-EEG signals reference electrode, 4-IC circuit, 5-battery compartment, 6-on and off switch, 7-terminal unit.
Detailed description of the invention
As shown in Figure 1, it is device structure schematic diagram of the present invention, comprise earphone, eeg signal acquisition device, IC circuit, signal transmitting apparatus and terminal unit, eeg signal acquisition device comprises eeg signal acquisition electrode and EEG signals reference electrode, described harvester is sampled to the simulation brain wave that people's cerebration produces, is quantized, become discrete digital signal, carry out follow-up process; These two electrodes are integrated mutually with earphone, and in order to gather EEG signals more accurately, eeg signal acquisition electrode is positioned at head, and EEG signals reference electrode clamp is positioned at ear.
IC circuit comprises signal amplifier and signal processor, and described signal amplifier is signal preamplifier, is amplified by the digital signal of quantification, the capacity of resisting disturbance in enhancement process and transmitting procedure; IC circuit is also mounted in the body of earphone.Signal processor carries out noise suppression preprocessing to the signal gathered, and strengthens the intensity of eeg signal, and therefrom extracts the parameter of reflection people cognitive state change, the state of assessment user; Signal transmitting apparatus connects IC circuit and terminal is established, and the parameter of IC circuit extraction is transferred to terminal unit.
Terminal unit is a PC, processes above-mentioned parameter, and carries out showing and feeding back.In general, transmission of wireless signals is adopted between terminal unit and IC circuit.
Earphone is headband receiver, eeg signal acquisition device and IC circuit integrated in headset body, and be integrated with battery compartment and on and off switch further.
A kind of tinnitus treatment method based on brain electricity frequency domain character indexing algorithm of the present invention, described method step is as follows:
(1) initialization apparatus hardware, setting can acceptable conditions;
(2) sound source automatically-broadcasting voice, gathers eeg data simultaneously, general collection 10 seconds;
(3) to the data filtering gathered, denoising, time frequency analysis, calculating brain electricity index;
(4) brain of calculating electricity index is transferred to client and shows;
(5) judge whether to meet acceptable conditions; If met, then can accept labelling to the sound setting of playing; If do not met, then again carry out correlation step to step (2).
Wherein, indexing algorithm is specific as follows:
(1) pretreatment: carry out digital filtering to the brain wave quantized, removes the interfering noises such as myoelectricity; Described wave filter is infinite-duration impulse response (IIR) band filter; The low pass initial frequency of band filter is 1Hz, and high pass cut off frequency is 35Hz.
(2) feature representation and extraction: the basic index extracting reflection general cognitive state from the EEG signals after pretreatment, specifically comprises alpha ripple (8-13Hz), beta ripple (13-20Hz), delta(1-4Hz), theta(4-7Hz); Use time-frequency analysis technology these indexs to be extracted from original time domain signal, frequency domain is expressed with the time series form of energy or power;
(3) indexing represents: above-mentioned basic index is carried out standardization, makes the same index of different users and same user different time have identical physical meaning; Described algorithm exports Vigilance level and the horizontal two indices of tensity, the horizontal index of described Vigilance and and the horizontal index of tensity specific as follows:
A (t), b (t) and c (t) represent the clock signal of alpha, beta and theta tri-frequency ranges respectively, and they are respectively by the energy accumulation realization also selecting special frequency channel via time frequency analysis of original EEG signals;
Alertness index:
S1 (t)=c (t)/a (t), wherein t express time, a and c represents the energy of alpha and theta respectively;
Tensity index:
S2 (t)=b (t) * c (t), wherein t express time, b and c represents the energy of beta and theta respectively;
(4) judgement of attention level: namely with normal user when not having sleepy, fatigue state to occur, continue to keep the horizontal index of the attention Vigilance of 2 minutes and and the horizontal index of tensity two index series sequential average 60% as decision threshold, be tired generation lower than this threshold judgement, show that the sound now produced by earphone well restrains tinnitus, then show that the sound that earphone produces is undesirable to tinnitus inhibition lower than threshold value.
Wherein, the specific algorithm of feature representation submodule is as follows:
Adopt Morlat function to be mother wavelet function, continuous wavelet transform is carried out to brain electricity time-domain signal; Input signal is the discrete brain electricity time series that brain wave acquisition arrives of singly leading, after converting, multiple time series signal and the wavelet coefficient of a series of different frequency range is obtained with above-mentioned mother wavelet function convolution, wherein time and input signal length are consistent, frequency range to being 1-35Hz, retain wherein 1-35Hz for extracting prosodic feature; For specific moment and frequency, coefficient represents the time-frequency distributions situation of signal, to its delivery, namely represents with power; According to band limits height, extract alpha ripple (8-13Hz), beta ripple (13-20Hz) respectively, delta(1-4Hz), theta(4-7Hz) time series of mould of Phase information coefficient of corresponding frequency band, namely power represents the timing variations of band energy.
Wherein, described step (3) indexing represents employing feature normalization algorithm, that is:
A certain band energy is accounted for the ratio of gross energy as index:
Wherein, t express time, f represents frequency, and P represents power, thus P
ft () represents the time dependent function of energy within the scope of a certain frequency f, the denominator part of formula then represents the energy accumulation summation in 1 to 35Hz frequency range; According to above model by P
ft (), divided by after gross energy normalization, the relative energy of each frequency range becomes the numerical value within the scope of 0-1, namely uses P
f 't () represents.
brain is worked in coordination with level index and is represented:
On the basis of above two basic indexs, we are again according to inherent physiology, psychological pattern that the different rhythm and pace of moving things represents, the brain proposing to utilize the synchronicity between circadian signal to carry out concentrated expression user works in coordination with state, thus whether the state of comprehensive representation cerebral activity is applicable to work.
Index calculate flow process is as follows:
From primary signal, extract alpha and the theta ripple of 8-13Hz and 4-7Hz band limits respectively, represent with a (t) and c (t), wherein t express time.
Respectively Hilbert conversion is carried out to brain electricity range signal a (t) and b (t), obtains its phase signals, phi a (t) and φ b (t), represent the time dependent situation of signal phase;
Calculate the synchronicity index S between alpha and theta energy time sequence, represent in a period of time (with representing), the phase difference value that two frequency band signals are overall, the i.e. quality of synchronicity, participate in the degree of sustained attention level for weighing full brain, synchronicity is better, the factors such as the cognitive resources more transferring full brain maintains higher attention level, can ensure Vigilance, and customer service is tired, improve the working ability stimulated to external world, thus keep good duty.
The computation model of index S is as follows:
Wherein, S represents the synchronicity index intending calculating, and wherein represent selected a period of time length, signal goes out according to the length of this time period step by step calculation from primary signal, and e represents natural constant, and its value is about 2.71828; T represents a certain moment in section seclected time; φ (t) represents the phase information of the rhythm and pace of moving things; Carry out adding up to the difference of the phase place in a period of time and can calculate overall phase synchronism, represent with natural logrithm form and can ensure index between zero and one.
In this patent, seclected time, segment length was 1s, and namely every 1s exports above index S once, to follow the tracks of the change of attention index in real time, was transferred to terminal and was pointed out.
Flow chart can be undertaken by following flow process:
Pretreatment-> feature representation and extraction-> feature normalization-> characteristic index represent
Alpha
Theta
Beta
The first two synthesis A: Alertness;
Latter two synthesizes B: tensity;
First and the 3rd degree of depth synthesis C: concertedness index;
Then threshold discrimination and index output is pointed to.
Claims (4)
1., based on a tinnitus treatment equipment for brain electricity frequency domain character indexing algorithm, described equipment comprises earphone, eeg signal acquisition device, IC circuit, signal transmitting apparatus and terminal unit, it is characterized in that:
Eeg signal acquisition device comprises eeg signal acquisition electrode, EEG signals reference electrode and signal processor, and described harvester is sampled to the simulation brain wave that people's cerebration produces, quantized, and becomes discrete digital signal, carries out follow-up process;
Described IC circuit comprises signal amplifier and signal processor, and the digital signal of quantification for for signal preamplifier, amplifies by described signal amplifier, the capacity of resisting disturbance in enhancement process and transmitting procedure;
Described signal processor carries out noise suppression preprocessing to the signal gathered, and strengthens the intensity of eeg signal, and therefrom extracts the parameter of reflection people cognitive state change, the state of assessment user;
Described signal transmitting apparatus connects IC circuit and terminal is established, and the parameter of IC circuit extraction is transferred to terminal unit;
Described terminal unit is a PC, processes above-mentioned parameter, and carries out showing and feeding back;
Described PC includes a brain electricity frequency domain character index calculate module, and step is as follows:
(1) pretreatment: carry out digital filtering to the brain wave quantized, removes the interfering noises such as myoelectricity; Described wave filter is infinite-duration impulse response (IIR) band filter;
(2) feature representation and extraction: the basic index extracting reflection general cognitive state from the EEG signals after pretreatment, specifically comprises alpha ripple (8-13Hz), beta ripple (13-20Hz), delta(1-4Hz), theta(4-7Hz); Use time-frequency analysis technology these indexs to be extracted from original time domain signal, frequency domain is expressed with the time series form of energy or power;
(3) indexing represents: above-mentioned basic index is carried out standardization, makes the same index of different users and same user different time have identical physical meaning; Described algorithm exports Vigilance level and the horizontal two indices of tensity, the horizontal index of described Vigilance and and the horizontal index of tensity specific as follows:
A (t), b (t) and c (t) represent the clock signal of alpha, beta and theta tri-frequency ranges respectively, and they are respectively by the energy accumulation realization also selecting special frequency channel via time frequency analysis of original EEG signals;
Alertness index:
S1 (t)=c (t)/a (t), wherein t express time, a and c represents the energy of alpha and theta respectively;
Tensity index:
S2 (t)=b (t) * c (t), wherein t express time, b and c represents the energy of beta and theta respectively;
(4) judgement of attention level: namely with normal user when not having sleepy, fatigue state to occur, continue to keep the horizontal index of the attention Vigilance of 2 minutes and and the horizontal index of tensity two index series sequential average 60% as decision threshold, be tired generation lower than this threshold judgement, show that the sound now produced by earphone well restrains tinnitus, then show that the sound that earphone produces is undesirable to tinnitus inhibition lower than threshold value.
2. equipment according to claim 1, is characterized in that: described earphone is headband receiver, eeg signal acquisition device and IC circuit integrated in headset body, and be integrated with battery compartment and on and off switch further.
3. equipment according to claim 1, is characterized in that: described eeg signal acquisition electrode is positioned at head, and EEG signals reference electrode clamp is positioned at ear.
4. equipment according to claim 1, is characterized in that: described signal transmitting apparatus is wireless signal transmission.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310596284.0A CN103816007B (en) | 2013-11-22 | 2013-11-22 | A kind of tinnitus treatment Apparatus and method for based on brain electricity frequency domain character indexing algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310596284.0A CN103816007B (en) | 2013-11-22 | 2013-11-22 | A kind of tinnitus treatment Apparatus and method for based on brain electricity frequency domain character indexing algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103816007A CN103816007A (en) | 2014-05-28 |
| CN103816007B true CN103816007B (en) | 2016-04-06 |
Family
ID=50751501
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201310596284.0A Expired - Fee Related CN103816007B (en) | 2013-11-22 | 2013-11-22 | A kind of tinnitus treatment Apparatus and method for based on brain electricity frequency domain character indexing algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103816007B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106927029A (en) * | 2017-03-03 | 2017-07-07 | 东华大学 | A kind of brain control four-axle aircraft induced based on single channel brain wave |
| CN110290746B (en) * | 2017-12-30 | 2022-04-22 | 深圳迈瑞生物医疗电子股份有限公司 | High-frequency radio frequency interference removing device and method |
| CN108523882A (en) * | 2018-02-27 | 2018-09-14 | 中国地质大学（武汉） | A kind of apoplexy emergency help device based on EEG signals |
| CN113261979B (en) * | 2021-07-19 | 2021-10-08 | 季华实验室 | Tinnitus identification system based on electroencephalogram signals |
| CN113456087A (en) * | 2021-08-18 | 2021-10-01 | 乔月华 | Tinnitus diagnosis and treatment system based on neurobiological feedback therapy and use method thereof |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201076468Y (en) * | 2007-07-11 | 2008-06-25 | 北京麦邦光电仪器有限公司 | Otoacoustic detection and analysis device |
| CN102265335A (en) * | 2009-07-03 | 2011-11-30 | 松下电器产业株式会社 | Hearing aid adjustment device, method and program |
| CN102474696A (en) * | 2009-07-13 | 2012-05-23 | 唯听助听器公司 | A hearing aid adapted fordetecting brain waves and a method for adapting such a hearing aid |
| CN102793543A (en) * | 2012-08-24 | 2012-11-28 | 刘政 | Health and happiness TTS/DTS (text to speech-data transformation services) tinnitus and deafness diagnostic equipment technical system |
| CN102821681A (en) * | 2010-04-28 | 2012-12-12 | 松下电器产业株式会社 | Brain wave measuring device, electric noise estimation method, and computer program for executing electric noise estimation method |
| CN102860046A (en) * | 2010-04-16 | 2013-01-02 | 唯听助听器公司 | A hearing aid and a method for alleviating tinnitus |
| CN103270779A (en) * | 2011-02-10 | 2013-08-28 | 松下电器产业株式会社 | Electroencephalograph, hearing aid, electroencephalogram recording method and program for same |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100839109B1 (en) * | 2006-09-20 | 2008-06-19 | [주]이어로직코리아 | The Method and Device for Objective Automated Audiometry |
-
2013
- 2013-11-22 CN CN201310596284.0A patent/CN103816007B/en not_active Expired - Fee Related
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201076468Y (en) * | 2007-07-11 | 2008-06-25 | 北京麦邦光电仪器有限公司 | Otoacoustic detection and analysis device |
| CN102265335A (en) * | 2009-07-03 | 2011-11-30 | 松下电器产业株式会社 | Hearing aid adjustment device, method and program |
| CN102474696A (en) * | 2009-07-13 | 2012-05-23 | 唯听助听器公司 | A hearing aid adapted fordetecting brain waves and a method for adapting such a hearing aid |
| CN102860046A (en) * | 2010-04-16 | 2013-01-02 | 唯听助听器公司 | A hearing aid and a method for alleviating tinnitus |
| CN102821681A (en) * | 2010-04-28 | 2012-12-12 | 松下电器产业株式会社 | Brain wave measuring device, electric noise estimation method, and computer program for executing electric noise estimation method |
| CN103270779A (en) * | 2011-02-10 | 2013-08-28 | 松下电器产业株式会社 | Electroencephalograph, hearing aid, electroencephalogram recording method and program for same |
| CN102793543A (en) * | 2012-08-24 | 2012-11-28 | 刘政 | Health and happiness TTS/DTS (text to speech-data transformation services) tinnitus and deafness diagnostic equipment technical system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103816007A (en) | 2014-05-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103815902B (en) | Based on Estimating System of Classroom Teaching and the method for brain electricity frequency domain character indexing algorithm | |
| CN103816007B (en) | A kind of tinnitus treatment Apparatus and method for based on brain electricity frequency domain character indexing algorithm | |
| WO2021109601A1 (en) | Method for measuring depth of anesthesia, storage medium, and electronic device | |
| CN102973277B (en) | Frequency following response signal test system | |
| CN103610461B (en) | Based on the EEG Signal Denoising method of dual density small echo neighborhood dependent thresholds process | |
| CN106388778B (en) | Electroencephalogram signal preprocessing method and system in sleep state analysis | |
| CN108577834B (en) | A method of it is detected automatically for epilepsy interphase spike | |
| CN109743656A (en) | Smart motion earphone and its implementation and system based on brain electricity idea | |
| US20130184552A1 (en) | Bi-hemispheric brain wave system and method of performing bi-hemispherical brain wave measurements | |
| CN105147248A (en) | Physiological information-based depressive disorder evaluation system and evaluation method thereof | |
| CN104688220A (en) | Method for removing ocular artifacts in EEG signals | |
| CN103815900B (en) | A kind of method of the measurement Vigilance based on brain electricity frequency domain character indexing algorithm | |
| CN104810024A (en) | Double-path microphone speech noise reduction treatment method and system | |
| CN105942974A (en) | Sleep analysis method and system based on low frequency electroencephalogram | |
| CN108852304A (en) | Sleeping quality analyzing device and method based on EEG signals | |
| CN106473705A (en) | Electroencephalogram signal processing method and system for sleep state monitoring | |
| WO2018009736A1 (en) | Motion-dependent averaging for physiological metric estimating systems and methods | |
| CN113855052B (en) | Nerve feedback intervention system and method based on positive idea meditation | |
| CN105342606A (en) | Method for treating sleep disorder by regulating central nerves | |
| CN103761974A (en) | Cochlear implant | |
| CN103815901B (en) | A kind of frequency domain character extracting method being applied to the portable brain electric equipment that singly leads | |
| CN113143289A (en) | Intelligent brain wave music earphone capable of being interconnected and interacted | |
| CN113907709B (en) | Portable sleep monitoring system based on ear EEG | |
| CN113180704A (en) | Sleep spindle wave detection method and system based on EEG brain waves | |
| Peng et al. | An improved EEG de-noising approach in electroencephalogram (EEG) for home care |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20201221 Address after: 703, 7 / F, Qingyun contemporary building, No.9 building, Mantingfangyuan community, Qingyun Li, Haidian District, Beijing 100086 Patentee after: ISEN TECH & TRADING Co.,Ltd. Address before: Room 509, block a, digital building, No.2, Zhongguancun South Street, Haidian District, Beijing 100086 Patentee before: Liu Zhiyong |
|
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20160406 Termination date: 20201122 |