CN102429658A - Intraoperative motion area function locating system based on electroencephalogram slow cortex potential wavelet analysis - Google Patents
Intraoperative motion area function locating system based on electroencephalogram slow cortex potential wavelet analysis Download PDFInfo
- Publication number
- CN102429658A CN102429658A CN2011104292274A CN201110429227A CN102429658A CN 102429658 A CN102429658 A CN 102429658A CN 2011104292274 A CN2011104292274 A CN 2011104292274A CN 201110429227 A CN201110429227 A CN 201110429227A CN 102429658 A CN102429658 A CN 102429658A
- Authority
- CN
- China
- Prior art keywords
- signal
- cortex
- electrode
- scp
- wavelet analysis
- 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.)
- Pending
Links
Images
Abstract
The invention discloses an intraoperative motion area function locating system based on electroencephalogram slow cortex potential wavelet analysis. The system is used for collecting a cortex electroencephalogram signal by implanting an electrode array. The cortex electroencephalogram signal is processed by an amplifying filter and then input to a signal processing module through an A/D (Analogue/Digital) converter. An electroencephalogram signal pre-processing unit of the signal processing module is used for pre-processing and filtering data collected by each electrode through a decomposition and reconstruction algorithm of wavelet analysis. An SCP (Slow Cortex Potential) signal characteristic extraction unit and a mode classification unit are used for extracting and classifying characteristics by adopting SCP-based signal characteristic extraction and a classification algorithm, identifying specific attributes of each electrode and finally processing and outputting a functional area locating image. The locating system disclosed by the invention is capable of accurately, rapidly and non-invasively detecting an electroencephalogram signal in a motion functional area and outputting a brain motion area function locating image. By means of the specific analysis of the cortex electroencephalogram signal of the brain motion area, the clinical application of the intraoperative functional location of the brain cortex motion area in a human-body nerve surgery is realized.
Description
Technical field
The present invention relates to the medical electronics instrument field, be specifically related to a kind of based on motor region functional localization system in the art of the slow cortical potential wavelet analysis of brain electricity.
Background technology
Brain function district pathological changes
,Mainly refer to be positioned at the tumor in motion, sensation and language district; Vascular malformation and epileptogenic focus, its sickness rate reports that at the investigation of wide scope of China's tissue only the prevalence of epilepsy just has 8 ‰ by World Health Organization (WHO), China has epileptic patient more than 1,000 ten thousand people now; Wherein medically intractable epilepsy accounts for about 30% of epileptic patient; China has 3,000,000 intractable epileptic patient to need operative treatment at present, and this does not also comprise the low level glioma that is positioned at functional areas, metastatic tumor; Former benign tumor, cavernous hemangioma and arteriovenous malformotion etc.Brain function district pathological changes is serious threat people's life not only, and have a strong impact on patient's existence and quality of life, and the individual who causes simultaneously, society and financial burden all are permanent and huge, have become serious society, economy and humanistic care problem.
The neurosurgery treatment is one of first-selected Therapeutic Method of brain function district pathological changes; Confirm cerebral nerve brain domain border through the location, functional areas; The help doctor excises focus to greatest extent and controls growth of tumor and recurrence, protects perilesional normal cerebral tissue as much as possible simultaneously, avoids function of nervous system's infringement; Keep normal function of nervous system, be related to the life quality of patient's postoperative.How in the art accurately in real time " brain domain " location be exactly the key of this type of operation.
At present, the method for neural cortex (motor region) functional localization mainly comprises methods such as microneurosurgery technology, neural video technology, neural electrophysiological technique.
The classical functional localization of dissecting is significant for clinical medicine, but certain error is arranged, because the occupy-place effect of individual variation and tumor, causes that functional areas pass and reinvent, and the classical functional localization error of dissecting can reach 20mm.
Rely on the high-resolution spiral CT and the functional type magnetic resonance (f-MRI) of image technology; And the many cortex dissect physiologies of can accomplishing of single photon emission computerized tomography,SPECT (SPECT), positron emission tomography scanning (PET), magneticencephalogram (MEG) and operation guiding system are located; But there is certain false positive in the iconography method, still can not monitor the state of operation process and definite brain function in real time.Functional type magnetic resonance (f-MRI) is that blood oxygen level carries out functional localization in the dependence cerebral blood flow, and the maximum error that can reach 20mm appears in the blood supply meeting that pathological changes influences cortex.Positron emission tomography scanning (PET) system also can position the active zone of brain metabolism, but only there is 65% coincidence rate the functional areas that it and electrophysiological stimulation are shown.
Can confirm the cortex and the location, subcortical function district of brain functioies such as motion, sensation, language even memory in real time based on stimulus of direct current art under cortex or the cortex in the art of electrophysiological technique; Be the most accurate, believable at present brain domain localization method commonly used, can reach about 5 mm based on the degree of accuracy of stimulus of direct current art under cortex or the cortex in the art of electrophysiological technique; But exist electricity irritation possibly damage cerebral cortex, trigger problems such as epilepsy and second operation, and the operating time reach 0.5 to several hours.
The defective of above-mentioned functions district localization method has shown in the neurosurgery treatment practice; The relation of functional structure and pathological changes can not differentiated and grasp to the functional localization technology of traditional operation fully; Very easily when the excision focus, cause the brain function structural damage, the permanent function of nervous system infringement complication of traditional operation is 13-27% according to statistics.In addition, because severe complication appears in functional areas disease surgery easily, also make the operative doctor excision not positive, usually appeasing property excision is merely 43% like the excision fully and time full resection rate of low level glioma.So not only make the pathological changes aftertreatment become difficult, and cause the recurrence of disease or symptom to be difficult to control easily, have a strong impact on the treatment prognosis.
This shows that present neural cortex (motor region) functional localization method is in speed, accurately and aspect the safety can not satisfy the brain domain operation needs fully.How can be in art accurately, fast, noinvasive, even non-wake-up states down the location brain domain be to perplex clinical and rationale problem neuromedicine research always, need to be resolved hurrily.
Summary of the invention
The objective of the invention is to the slow cortical potential of motor region specificity brain electricity is principle, and combined with wavelet transformed discloses a kind of based on motor region functional localization system in the art of the slow cortical potential wavelet analysis of brain electricity.This system can be accurately, fast, noinvasive ground detects motor function district EEG signals and imports; And the specificity analyses through the slow cortex EEG signals of brain motor region; Accomplish the output of brain motor region functional localization figure, realize that accurate, quick, the non-invasive clinical of nerve system of human body motor area of cerebral cortex functional localization used.
Having specific slow cortical potential (SCP) all exists in all individualities.Characteristics of low-frequency in the cortex brain electricity is that cortex produces change in voltage slowly, and the skew of these voltages can occur in 0.5-10 in the time of second, is called as slow cortical potential (SCP).Slow cortical potential is an event related potential ERP relevant with motion, results from deep, crown cerebral cortex.Slow cortical potential is relevant with the cortex activation with motion, and the cortical activity that the slow cortical potential of negative sense is relevant with motion is relevant, and the slow cortical potential of forward is relevant with the decline of cortex excitement degree.The event related potential ERP of real motion has reflected motor cortex excited or state of suppressing when limb motion, and the spatial distribution of the ERP of different limb motions on scalp also meets the characteristic that the body specific region distributes.Therefore, through detecting the spatial distribution of slow cortical potential (SCP) on cortex that produces when the limb motion, spatial distribution that can cortex motor function district, detection of dynamic location.Wavelet transformation has many resolution characteristics, utilizes the decomposition and reconstruction fast algorithm of Mallat from motor region brain electricity, to extract slow cortical potential, for motor region specificity EEG signals SCP detects the strong instrument that provides.
Based on above-mentioned principle, the technical scheme that the present invention adopts is described below:
A kind of based on motor region functional localization system in the neurosurgery art of slow cortex brain electricity wavelet analysis; Comprise the eeg signal acquisition module; Signal processing module; Location, functional areas map output module, said signal processing module comprises EEG signals pretreatment unit, SCP signal characteristic extraction unit and pattern classification unit; The EEG signals that the eeg signal acquisition module is gathered; Carry out pretreatment filtering via the EEG signals pretreatment unit; Be sent to SCP signal characteristic extraction unit and extract specificity SCP characteristic signal; Classify through the pattern classification unit again, at last through location, functional areas map output module feedback positioning result.
Said eeg signal acquisition module comprises implanted electrode, amplifilter and A/D transducer; Implanted electrode is gathered EEG signals; Carry out amplification filtering via amplifilter and handle, convert EEG signals into digital signal through A/D converter then, be input to signal processing module at last.
Said implanted electrode is the dura mater platinum electrode, comprises platinum 6*8 or 8*8 electrod-array, and electrode diameter is 4mm, and the adjacent electrode spacing is 10mm.Implanted electrode is placed on people's the cerebral cortex.Amplifilter and A/D transducer adopt the Synamps2 amplifier, are used for the amplification and the digitized of electrode detection signal.
The pretreatment filtering of said EEG signals pretreatment unit comprises multiple dimensioned decomposition.The discrete db5 wavelet transformation of said multiple dimensioned decomposition utilization carries out 8 layers of wavelet decomposition, and the decomposition and the restructing algorithm of the small echo Mallat algorithm of employing are seen formula (3).Said SCP signal characteristic extraction unit extracts a8 monolayer detail coefficients, the reconstruct of counting entirely then, and the signal Sd8 after its reconstruct exports as SCP.Said pattern classification unit is that characteristic threshold value is/not classification the identification specificity electrode to the SCP signal with 1.6.
Wherein,
H,
GBe the wavelet decomposition wave filter in the time domain,
h,
gBe the wavelet reconstruction wave filter in the time domain; T is a discrete-time series,
t=1,2 ...,
N jBe the decomposition number of plies,
j=1,2,
J,
JBe the decomposition degree of depth,
f(
t) be primary signal.
a j For
f(
t)
jThe wavelet coefficient of the approximate part of layer;
d j For
f(
t)
jThe wavelet coefficient of layer detail section.
A8 (frequency range 0-1.95Hz) is corresponding to the frequency range of SCP, and its reconstruction signal Sa8 is corresponding to the SCP signal.During reconstruct Sa8 list band signal, only extract the approximate or detail coefficients of its monolayer, all the other coefficients put 0, then to the reconstruct of counting entirely of this monolayer coefficient.Formula (4) is seen in the calculating of Sa8 reconstruction signal characteristic quantity (motion event front and back SCP time self-energy takes place than ERD):
Wherein, ER is the quadratic sum of each sampling point value of each the sub-band reconstruction signal in the ERD time window before the motion event, and EA is the quadratic sum of each sampling point value of each the sub-band reconstruction signal in the ERD time window behind the calculating motion event.
The motion specific function district network for location of location, said functional areas map output module output; Be that the specificity electrode coordinate of discerning with the pattern classification unit is a boundary point match boundary curve; That is: motion specific function district network for location, the zone that closed curve surrounds are motion specific function district.
The present invention has following advantage and effect:
(1) specificity detects the accuracy height: the incident ERP specificity that the present invention is based on motor function district SCP; Rational feature band, eigenvalue have been selected; The feature extraction and the sorting algorithm of the present invention's design have the reliable detection principle; Guaranteed that fundamentally specificity detects accuracy, detected accuracy and reach 84% that false drop rate is 11%.
(2) the electrode detection precision is high: the electrode that system adopts has distance between the implanted electrode of 4mm diameter and 10mm, has higher space and frequency resolution, and neuronic electrical activity information near the 5mm radius of electrode centers point can be provided.The essence of characteristic threshold value is near detected smallest effective characteristic quantity in 5mm radius electrode centers point, and space, the location microcosmic degree of accuracy of therefore calculating native system can reach 5 mm.Can further improve space, location microcosmic degree of accuracy with stimulus of direct current under cortex in the art or the cortex.Compare with stimulus of direct current art under cortex in the art or the cortex, space, native system location microcosmic degree of accuracy is suitable.
(3) detection speed is fast: native system is that the specificity brain electricity in motor function district is a detected object with the ERP that brings out slow cortex brain electricity SCP; The sampling experimental period of the SCP of evoked brain potential ERP and time window are 4 seconds; And employing single specificity brain electricity extracting mode; Therefore, sample rate is 4 seconds in theory.Consider that reliability adopts the common method of judging of sampling experimental result 10 times, add the time of Computer Processing, a functional localization of native system detection time is 60 seconds.Reaching 0.5 to several hours with the stimulus of direct current art operating time under cortex in the art or the cortex compares; Native system has greatly improved detection speed; Greatly reduced doctor's operating time and patient's misery, saved huge man power and material, had good economy and humanistic care and be worth.
(4) detection is non-invasive: this paper is the specificity brain electricity in motor function district with the SCP of evoked brain potential ERP, and SCP adopts the passive detection mode of EEG signals, does not have the wound that initiatively stimulation causes.Avoid in the art under cortex or the cortex stimulus of direct current art possibly damage cerebral cortex, triggered problem such as epilepsy.Greatly reduced doctor's operating time and patient's misery, saved huge man power and material, had good economy and humanistic care and be worth.
Description of drawings
Fig. 1 is a motor region brain function navigation system structure chart.
Fig. 2 is eeg signal acquisition modular structure figure.
Fig. 3 is single test small echo signal decomposition and reconstruct.
Fig. 4 is the wavelet transform filtering of primary signal.
Fig. 5 is a motion specific function district network for location.
The specific embodiment
Below in conjunction with accompanying drawing and embodiment the present invention is described further, but enforcement of the present invention is not limited thereto.
A kind of based on motor region functional localization system in the neurosurgery art of slow cortical potential brain electricity wavelet analysis; As shown in Figure 1; Comprise the eeg signal acquisition module; Signal processing module, location, functional areas map output module, signal processing module comprises EEG signals pretreatment unit, SCP signal characteristic extraction unit and pattern classification unit.The composition of eeg signal acquisition module is as shown in Figure 2, comprises implanted electrode, amplifilter and A/D transducer.Cortex EEG signals ECoG gathers through the dura mater bottom electrode array of implanted electrode in this system, carries out amplification filtering via amplifilter and handles, and converts EEG signals into digital signal through A/D converter then, is input to signal processing module again; The EEG signals pretreatment unit of signal processing module is through the decomposition and reconstruction algorithm of wavelet analysis; Data to each electrode collection are carried out pretreatment filtering; Be sent to SCP signal characteristic extraction unit and extract specificity SCP signal characteristic; Classify through the pattern classification unit again, accomplish the functional areas network for location through location, functional areas map at last and handle output.
On every patient's cerebral cortex, lay dura mater bottom electrode array and extract the ECoG data; For patient implant dura mater platinum 6*8 or 8*8 electrod-array (subdural electrode arrays (and Ad-Tech, Racine, WI); Each electrode diameter is 4mm, and distance is 10mm between adjacent electrode; (Neuroscan, ElPaso TX) are used for the amplification and the digitized of electrode detection signal to the Synamps2 amplifier, and the ECoG data sampling rate is 1000Hz, through the filtering of 0.05～200Hz passband.
Signal processing module is provided by COMPREHENSIVE CALCULATING machine processing system, the motion indication is provided and stores instruction time to patient, receives and store the EEG signals data after the Synamps2 amplifier is handled.
During each the experiment, according to " motion-rest " indication of computer display, patient elder generation motion finger 2 seconds was had a rest 2 seconds then; Repeat repeatedly above-mentioned identical experiment again.Collection is positioned at the specific cortex zone ECoG data in cerebral nerve cortex motor function district, carries out image data altogether 10 times, is used for the work of treatment of signal processing module.
Confirm to decompose level according to SCP EEG signals and the interferential frequency band of power frequency.The EEG signals that the present invention studied comprise SCP (0-2Hz), some transient signals and power frequency disturb (50 Hz), confirms that through table 1 frequency band computing formula decomposing level is 8.Under different scale, the pairing frequency band of the details of wavelet decomposition is different, sees table 1:
Table 1: the pairing frequency band of each coefficient of wavelet decomposition
| Coefficient of wavelet decomposition | The frequency band computing formula | |
| a8 | ||
| 0,fs/512 | 0—1.95 | |
| d8 | fs/512,fs/256 | 1.95—3.9 |
| d7 | fs/256, fs/128 | 3.9—7.8125 |
| d6 | fs/128, fs/64 | 7.8125—15.625 |
| d5 | fs/64, fs/32 | 15.625—31.25 |
| d4 | fs/32, fs/16 | 31.2—62.5 |
| d3 | fs/16, fs/8 | 62.5—125 |
| d2 | fs/8, fs/4 | 125—250 |
| d1 | fs/4, fs/2 | 250—500 |
The EEG signals pretreatment unit utilizes the multiresolution characteristic of wavelet transformation, and the EEG signals that will contain noise carry out multiple dimensioned decomposition, obtain the subband signal of different frequency bands.Specific as follows: the original cortex eeg data that single test is had background noise is imported the matlab software application, utilizes discrete db5 wavelet transformation to carry out 8 layers of wavelet decomposition, and the result sees Fig. 3.Abscissa express time among the figure, unit are sampling number (sample frequency are 1000Hz), and the longitudinal axis is represented amplitude, and unit is μ V.The moment of zero corresponding constantly experiment beginning of abscissa.D1-d8 is the detail signal that yardstick 1-8 goes up wavelet decomposition, and a8 is the approximation signal of the wavelet decomposition on the yardstick 8.
SCP signal characteristic extraction unit is handled the frequency band that contains noise, and the EEG signals behind the noise such as power frequency interference are removed in reconstruct then, and extract motor region SCP specificity EEG signals.Specific as follows: only extract a8 (frequency range 0-1.95 Hz) monolayer detail coefficients, all the other coefficients put 0, and then to the reconstruct of counting entirely of this monolayer coefficient, its reconstruction signal Sd8 sees Fig. 4 as the output of SCP signal.Simultaneously, noise and some other transition interfering signals such as power frequency interference have been eliminated.Abscissa express time among the figure, unit are sampling number (sample frequency are 1000Hz), and the longitudinal axis is represented amplitude, and unit is μ V.The moment of zero corresponding constantly experiment beginning of abscissa.
The pattern classification unit is according to the specificity of motor region SCP specificity EEG signals; ERD/ERS index with a8 (frequency range 0-1.95 Hz) feature band reconstruction signal Sa8 is an eigenvalue; With 1.6 is that characteristic threshold value is carried out " being/deny " classification, discerns the specificity attribute of each electrode.
Motion specific function district network for location output module forms coordinate system with the structure of dura mater bottom electrode array; Coordinate with whole 48 specificity electrodes is a boundary point match boundary curve; Form closed curve figure and export ground motion specific function district network for location exactly; The zone that surrounds in the closed curve is motion specific function district, and is as shown in Figure 5.Be applied to clinical medicine surgical functions district when location, cooperate the secondary function district to locate, confirm to form last accurate motor function district network for location in the border in motion specific function district with other method (like the electric cortical stimulation method etc.).Extract and sorting result according to the SCP signal characteristic, realize the network for location output of brain motor function district.
Claims (10)
1. one kind based on motor region functional localization system in the art of the slow cortical potential wavelet analysis of brain electricity; Comprise the eeg signal acquisition module; Signal processing module; Location, functional areas map output module is characterized in that said signal processing module comprises EEG signals pretreatment unit, SCP signal characteristic extraction unit and pattern classification unit; The EEG signals that the eeg signal acquisition module is gathered; Carry out pretreatment filtering via the EEG signals pretreatment unit; Be sent to SCP signal characteristic extraction unit and extract specificity SCP prosodic feature; Classify through the pattern classification unit again, at last through location, functional areas map output module feedback positioning result.
2. according to claim 1 a kind of based on motor region functional localization system in the art of the slow cortical potential wavelet analysis of brain electricity; It is characterized in that said eeg signal acquisition module comprises implanted electrode, amplifilter and A/D transducer; Implanted electrode is gathered EEG signals; Carry out amplification filtering via amplifilter and handle, convert EEG signals into digital signal through A/D converter then, be input to signal processing module at last.
3. according to claim 2 a kind of based on motor region functional localization system in the art of the slow cortical potential wavelet analysis of brain electricity; It is characterized in that said implanted electrode is the dura mater platinum electrode; Comprise platinum 6*8 or 8*8 electrod-array, electrode diameter is 4mm, and the adjacent electrode spacing is 10mm.
4. according to claim 3 a kind of based on motor region functional localization system in the art of the slow cortical potential wavelet analysis of brain electricity, it is characterized in that said implanted electrode is placed on people's the cerebral cortex.
5. according to claim 2 a kind of based on motor region functional localization system in the art of the slow cortical potential wavelet analysis of brain electricity; It is characterized in that amplifilter and A/D transducer adopt the Synamps2 amplifier, are used for the amplification and the digitized of electrode detection signal.
6. according to claim 1 a kind of based on motor region functional localization system in the art of the slow cortical potential wavelet analysis of brain electricity, it is characterized in that the pretreatment filtering of said EEG signals pretreatment unit comprises multiple dimensioned decomposition.
7. according to claim 6 a kind of based on motor region functional localization system in the art of the slow cortical potential wavelet analysis of brain electricity, it is characterized in that the discrete db5 wavelet transformation of said multiple dimensioned decomposition utilization carries out 8 layers of wavelet decomposition, it is according to like formula (1):
Wherein,
H,
GBe the wavelet decomposition wave filter in the time domain,
h,
gBe the wavelet reconstruction wave filter in the time domain; T is a discrete-time series,
t=1,2 ...,
N jBe the decomposition number of plies,
j=1,2,
J,
JBe the decomposition degree of depth,
f(
t) be primary signal;
a j For
f(
t)
jThe wavelet coefficient of the approximate part of layer;
d j For
f(
t)
jThe wavelet coefficient of layer detail section.
8. according to claim 7 a kind of based on motor region functional localization system in the art of the slow cortical potential wavelet analysis of brain electricity; It is characterized in that said SCP signal characteristic extraction unit only extracts a8 monolayer detail coefficients; The reconstruct of counting entirely then; Signal Sa8 after its reconstruct exports as SCP, and formula (2) is seen in the calculating of Sa8 reconstruction signal characteristic quantity (motion event the 2 seconds self-energys in front and back takes place than ERD):
Wherein, ER is the quadratic sum of each sampling point value of each the sub-band reconstruction signal in preceding 2 seconds of the motion event, and EA is for calculating the quadratic sum of each sampling point value of each the sub-band reconstruction signal in 2 seconds behind the motion event.
9. according to claim 8 a kind of based on motor region functional localization system in the art of the slow cortical potential wavelet analysis of brain electricity, it is characterized in that said pattern classification unit is that characteristic threshold value is/not classification the identification specificity electrode to SCP with 1.6.
10. according to claim 9 a kind of based on motor region functional localization system in the art of the slow cortical potential wavelet analysis of brain electricity; It is characterized in that the motion specific function district network for location of location, said functional areas map output module output, is that the specificity electrode coordinate of discerning with the pattern classification unit is a boundary point match boundary curve.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2011104292274A CN102429658A (en) | 2011-12-20 | 2011-12-20 | Intraoperative motion area function locating system based on electroencephalogram slow cortex potential wavelet analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2011104292274A CN102429658A (en) | 2011-12-20 | 2011-12-20 | Intraoperative motion area function locating system based on electroencephalogram slow cortex potential wavelet analysis |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN102429658A true CN102429658A (en) | 2012-05-02 |
Family
ID=45978271
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2011104292274A Pending CN102429658A (en) | 2011-12-20 | 2011-12-20 | Intraoperative motion area function locating system based on electroencephalogram slow cortex potential wavelet analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102429658A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105662598A (en) * | 2015-12-31 | 2016-06-15 | 清华大学 | Device, method and system for positioning cerebral-cortex functional area |
| CN106725463A (en) * | 2017-01-18 | 2017-05-31 | 浙江大学 | Using the method and system that Cortical ECoG signal is positioned to cerebral cortex hand function area |
| CN107735021A (en) * | 2015-05-02 | 2018-02-23 | 健康研究公司 | Fast mapping linguistic function and motor function in the case where no subject participates in |
| CN109727672A (en) * | 2018-12-28 | 2019-05-07 | 江苏瑞尔医疗科技有限公司 | Patient's thorax and abdomen malignant respiratory movement predicting tracing method |
| CN117679047A (en) * | 2024-02-02 | 2024-03-12 | 长春理工大学 | Efficient epileptic detection method and system for multi-scale lightweight network system |
| CN117679047B (en) * | 2024-02-02 | 2024-04-05 | 长春理工大学 | Efficient epileptic detection method and system for multi-scale lightweight network system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5638826A (en) * | 1995-06-01 | 1997-06-17 | Health Research, Inc. | Communication method and system using brain waves for multidimensional control |
| WO2009051638A1 (en) * | 2007-10-16 | 2009-04-23 | Medtronic, Inc. | Therapy control based on a patient movement state |
| CN101980106A (en) * | 2010-10-15 | 2011-02-23 | 华南理工大学 | Two-dimensional cursor control method and device for brain-computer interface |
| CN202397462U (en) * | 2011-12-20 | 2012-08-29 | 华南理工大学 | Intraoperative motor area function positioning system based on brain wave slow cortical potential (SCP) wavelet analysis |
-
2011
- 2011-12-20 CN CN2011104292274A patent/CN102429658A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5638826A (en) * | 1995-06-01 | 1997-06-17 | Health Research, Inc. | Communication method and system using brain waves for multidimensional control |
| WO2009051638A1 (en) * | 2007-10-16 | 2009-04-23 | Medtronic, Inc. | Therapy control based on a patient movement state |
| CN101980106A (en) * | 2010-10-15 | 2011-02-23 | 华南理工大学 | Two-dimensional cursor control method and device for brain-computer interface |
| CN202397462U (en) * | 2011-12-20 | 2012-08-29 | 华南理工大学 | Intraoperative motor area function positioning system based on brain wave slow cortical potential (SCP) wavelet analysis |
Non-Patent Citations (1)
| Title |
|---|
| 姜涛: "基于皮质脑电的脑运动区功能定位原理与算法研究", 《华南理工大学博士学位论文》 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107735021A (en) * | 2015-05-02 | 2018-02-23 | 健康研究公司 | Fast mapping linguistic function and motor function in the case where no subject participates in |
| US11051749B2 (en) | 2015-05-02 | 2021-07-06 | Health Research, Inc. | Rapid mapping of language function and motor function without subject participation |
| US11779256B2 (en) | 2015-05-02 | 2023-10-10 | Health Research, Inc. | Rapid mapping of language function and motor function without subject participation |
| CN105662598A (en) * | 2015-12-31 | 2016-06-15 | 清华大学 | Device, method and system for positioning cerebral-cortex functional area |
| CN105662598B (en) * | 2015-12-31 | 2017-12-29 | 清华大学 | A kind of cerebral cortex Orientation of functions device, method and system |
| CN106725463A (en) * | 2017-01-18 | 2017-05-31 | 浙江大学 | Using the method and system that Cortical ECoG signal is positioned to cerebral cortex hand function area |
| CN106725463B (en) * | 2017-01-18 | 2020-02-21 | 浙江大学 | Method and system for positioning cerebral cortex hand functional area by applying cortical electroencephalogram signals |
| CN109727672A (en) * | 2018-12-28 | 2019-05-07 | 江苏瑞尔医疗科技有限公司 | Patient's thorax and abdomen malignant respiratory movement predicting tracing method |
| CN117679047A (en) * | 2024-02-02 | 2024-03-12 | 长春理工大学 | Efficient epileptic detection method and system for multi-scale lightweight network system |
| CN117679047B (en) * | 2024-02-02 | 2024-04-05 | 长春理工大学 | Efficient epileptic detection method and system for multi-scale lightweight network system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110876626B (en) | Depression detection system based on optimal lead selection of multi-lead electroencephalogram | |
| CN105520732B (en) | For assessing the method and Portable Automatic brain function assessment equipment of brain function | |
| US9826914B2 (en) | Functional analysis of neurophysiological data | |
| CN101677775B (en) | System and method for pain detection and computation of a pain quantification index | |
| Rak et al. | Brain-computer interface as measurement and control system the review paper | |
| Martinek et al. | Advanced bioelectrical signal processing methods: Past, present, and future approach—Part III: Other biosignals | |
| Martinek et al. | Advanced bioelectrical signal processing methods: past, present and future approach—part I: cardiac signals | |
| CN102715903A (en) | Method for extracting electroencephalogram characteristic based on quantitative electroencephalogram | |
| Valderrama et al. | Gain of the human dura in vivo and its effects on invasive brain signal feature detection | |
| CN103989472A (en) | Stroke patient neural rehabilitation assessment method based on electro-cerebral alpha sample entropies | |
| CN102429658A (en) | Intraoperative motion area function locating system based on electroencephalogram slow cortex potential wavelet analysis | |
| Sun et al. | A study of resting-state EEG biomarkers for depression recognition | |
| CN110338760B (en) | Schizophrenia three-classification method based on electroencephalogram frequency domain data | |
| CN102512162A (en) | Intraoperative motor area function localization system based on multi-mode electroencephalogram wavelet analysis | |
| CN202397462U (en) | Intraoperative motor area function positioning system based on brain wave slow cortical potential (SCP) wavelet analysis | |
| CN102512161A (en) | Intraoperative motor area function localization system based on cortex electroencephalogram mu rhythm wavelet analysis | |
| CN202397463U (en) | Intra-operative motor area function positioning system based on multi-mode electroencephalogram wavelet analysis | |
| CN202397464U (en) | Intraoperative motion area functional localization system based on cortical electroencephalogram mu rhythm wavelet analysis | |
| Zhao et al. | An early warning of atrial fibrillation based on short-time ECG signals | |
| Chen et al. | EEG-based seizure detection using discrete wavelet transform through full-level decomposition | |
| Liu et al. | Detection of myocardial infarction from multi-lead ECG using dual-Q tunable Q-factor wavelet transform | |
| Lee et al. | Single-trial analysis of cortical oscillatory activities during voluntary movements using empirical mode decomposition (EMD)-based spatiotemporal approach | |
| Rajendran et al. | Classification of heart disease from ECG signals using Machine Learning | |
| Bhuvaneswari et al. | Classification of electromyography signal using wavelet decomposition method | |
| Yazdanpour-Naeini | Automatic detection of high frequency oscillations of neural signals in epileptic patients |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C02 | Deemed withdrawal of patent application after publication (patent law 2001) | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20120502 |