CN104545900A - Event related potential analyzing method based on paired sample T test - Google Patents
Event related potential analyzing method based on paired sample T test Download PDFInfo
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/369—Electroencephalography [EEG]
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
Abstract
The invention relates to an event related potential analyzing method based on a paired sample T test. The method includes: designing an electroencephalogram induction experiment containing two kinds of stimulations, using electroencephalogram collecting equipment to record multiple associated scalp electroencephalogram signals, and performing preliminary preprocessing; extracting ERP signals under the two kinds of stimulations; performing paired sample T test on the ERP signals under the two kinds of stimulations to determine a time period with significant difference; calculating the difference area of the ERP signals under the two kinds of stimulations in the time period with significant difference, and drawing a brain electrical activity mapping to determine the difference brain areas. The method has the advantages that the significant difference time period of the ERP under the two kinds of stimulations, the brain electrical activity mapping based on the ERP waveform difference area is drawn, and the different brain area distribution in the significant difference time period is obtained; the method is significant to ERP researches which are poor in signal to noise ratio and unobvious in single component, and a new idea is provided to the stripping of the ERP signals and spontaneous electroencephalogram.
Description
Technical field
The present invention relates to a kind of event related potential analytical method.Particularly relate to and a kind ofly comprise multiple environmental stimuli and single stimulates the less event related potential analytical method based on paired sample T test of number of repetition.
Background technology
EEG signals is spontaneity, the rhythmicity electrical activity of the brain cell group recorded by electrode, according to whether containing outside stimulus, spontaneous brain electricity (Electroencephalo-graph can be divided into, and event related potential (Event-related Potentials, ERP) two kinds EEG).Event related potential is a kind of EEG signals that people bring out out when particular stimulation event is carried out to perception processing or performed certain Cognitive task, be usually used in reflecting stimulate occur before and after the situation of change of brain potential, distribute to brain attentional resources, object remembered, thinking decision-making, Cognitive Processing etc. be relevant.Because ERP signal has the temporal resolution of Millisecond, good Noninvasive, and collecting device operation is comparatively simple, and this signal has a lot of application in brain function research and disease of brain are examined in advance.
For many years, a significant difficulty of ERP research is exactly the stripping with spontaneous brain electricity.Research display, brain is not all the time in running, even if when not giving any environmental stimuli, central nervous system also also exists rhythmicity, spontaneity discharge phenomenon, and the ERP signal amplitude that external event brings out is much smaller than spontaneous brain electricity, and be usually submerged in spontaneous brain electricity.Because spontaneous brain electricity has very large individual difference and randomness, therefore can not form a fixing spontaneous brain electricity template, make ERP signal be convenient to peel off.In real process, usual employing repeatedly repeats the amplitude and the purity that apply outside stimulus, the mode that is averaging again improves ERP signal, and then itself and spontaneous brain electricity is peeled off.In order to obtain the good ERP signal of signal to noise ratio, usually need the tens repetition environmental stimulis that even hundreds of is secondary, on the one hand, repeatedly repetitive stimulation will inevitably cause the fatigue of sensorium, and is difficult to keep on all four repeatability; On the other hand, a large amount of stimulus material preparation and be not easy, especially for the stimulation that the picture, sound etc. of specific meanings are more complicated.
In addition, ERP in the past analyzes and focuses mostly in certain or certain several ERP composition (as P1, N1, P3 etc.) analysis, but the ERP signal less for repetitive stimulation number of times, signal to noise ratio is not good enough, the ERP composition with clear and definite physical significance is often difficult to identify, also causes the difficulty of ERP feature extraction under different stimulated.
Based on the event related potential analytical method of paired sample T test from the angle analysis ERP signal of significant difference, can extract the difference characteristic of significance, avoid the difficulty of single ERP constituents extraction, be the new approaches of ERP relative analysis.In addition, because though the spontaneous brain electricity under two kinds of different environmental stimulis is not quite identical, but not there is significant diversity yet, if carry out paired sample T test to the ERP signal under two kinds stimulate, the significant difference period obtained must be the period that two true ERP signals have significant difference, can realize the indirect stripping of ERP signal and spontaneous brain electricity.
Summary of the invention
Technical problem to be solved by this invention is, provides a kind of and can be used for the poor and single composition of signal to noise ratio and the event related potential analytical method based on paired sample T test studied of unconspicuous ERP.
The technical solution adopted in the present invention is: a kind of event related potential analytical method based on paired sample T test, comprises the steps:
1) design brings out experiment containing the brain electricity that two kinds stimulate, and utilizes the multiple scalp EEG signals of leading of brain wave acquisition equipment record, carries out preliminary pretreatment;
2) the ERP signal under two kinds of stimulations is extracted;
3) paired sample T test is carried out to the ERP signal under two kinds of stimulations, determine the period with significant difference;
4) calculate the difference area of ERP signal within the significant difference period under two kinds of stimulations, and draw brain mapping, determine difference brain district.
Step 1) described in preliminary pretreatment, being the interference for removing low frequency wonder, High-frequency Interference and eye galvanic electricity physiological signal in scalp EEG signals recording process, carrying out becoming the pretreatment operation that average reference, 0.5-10Hz bandpass filtering and independent component analysis remove eye electricity to original EEG signals.
Step 2) described in the two or more stimulation of extraction under ERP signal, comprise following process:
(1) split the whole paragraph header skin EEG signals after preliminary pretreatment, obtain the evoked brain potential fragment that resting electroencephalogramidentification fragment that 20 durations are 4s and 20 durations are 1s, wherein, two kinds stimulate each 10 corresponding of evoked brain potential fragment;
(2) choose the 200ms before stimulation presents, namely the rear 200ms of quiescent stage is benchmark brain electricity, and the average amplitude of Calculation Basis brain electricity, each evoked brain potential fragment is deducted benchmark brain electricity average amplitude, realize removing base line operations;
(3) respectively to going two kinds of evoked brain potential fragments after baseline to carry out superposed average, obtaining each ERP signal under stimulating at two kinds that leads of every subjects, being expressed as X={X
ijkand Y={Y
ijk, wherein, i=1,2 ..., N
1, N
1=15, N
1it is subjects's number; J=1,2 ..., N
2, N
2=32, N
2the number that leads, k=1,2 ..., N
3, N
3=1024, N
3it is number of data points.
Step 3) described in two kinds stimulate under ERP signal carry out paired sample T test, that the ERP sequence corresponding to each data point in each leading carries out paired sample T test, for the individual kth data point of leading of jth, first set up a new variables Z={Z
ijk, Z
ijk=X
ijk-Y
ijk, i=1,2 ..., N
1, calculate the average of new variables
and variance
structure statistic
inspection t
jkwhether obeying degree of freedom is N
1the T distribution of-1, and calculate corresponding significance degree P
jkif, P
jk<0.05, then sequence
and sequence
have significant difference, the individual kth data point of leading of jth namely under two kinds of stimulations has significant difference, otherwise the individual kth data point of leading of the jth under two kinds of stimulations does not have significant difference.
Step 3) described in determination there is period of significant difference, be carry out on the basis determined to lead the jth under two kinds stimulate whether a kth data point have a significant difference, comprise following process:
(1) for the N that jth is led
3individual data point, if the consecutive numbers strong point k having more than 10, makes sequence
and sequence
have significant difference, the period that so these continuous print data point k is corresponding is exactly the period that jth the ERP signal led under two kinds of stimulations has significant difference, if there is not the consecutive numbers strong point k of more than 10, makes sequence
and sequence
have significant difference, then the ERP signal that jth is led under two kinds of stimulations does not have the significant difference period;
(2) the significant difference period distribution of leading according to all, selects the period that more than one relatively large, and make to comprise as far as possible the significant difference period that majority leads, the data point set that the selected significant difference period is corresponding is labeled as
wherein, r=1,2 ..., hop count when m, m are selected significant difference, range of choices be 1,2 ..., 100}, n
rbe the number of data points corresponding to each significant difference period respectively, value is all greater than 10.
Step 4) described in calculating two kinds stimulate under the difference area of ERP signal within the significant difference period, the each difference area of ERP signal within the selected significant difference period all calculated under two kinds of stimulations that lead to every subjects, wherein, in the significant difference period
the difference area of the ERP signal under two kinds of interior stimulations is
and superposed average is carried out to the data of all subjectss, obtain the ERP signal difference area that jth is led within the selected significant difference period under two kinds of stimulations
Step 4) described in drafting brain mapping, determine difference brain district, for each significant difference period, ERP signal difference area drafting brain mapping under all stimulating according to all two kinds of leading, and then analyze two kinds and stimulate institute to bring out the spatial distribution state of ERP signal difference, the primary activation brain district obtained within the significant difference period distributes.
A kind of event related potential analytical method based on paired sample T test of the present invention, from paired sample T test, determine the ERP significant difference period under two kinds of stimulations, and the brain mapping depicted based on ERP different wave shape area, and then obtain the difference brain district distribution within the significant difference period.The present invention mainly for comprise multiple environmental stimuli and single stimulate number of repetition less evoked brain potential research, poor and the single composition for signal to noise ratio unconspicuous ERP studies significant, and provide new thinking for the stripping of ERP signal and spontaneous brain electricity.
Accompanying drawing explanation
Fig. 1 is a kind of flow chart of the event related potential analytical method based on paired sample T test.
Detailed description of the invention
Below in conjunction with embodiment and accompanying drawing, a kind of event related potential analytical method based on paired sample T test of the present invention is described in detail.
A kind of event related potential based on paired sample T test (Event-related Potentials of the present invention, ERP) analytical method, first the many top guides skin EEG signals under utilizing brain wave acquisition equipment record two kinds to stimulate, and carry out preliminary pretreatment; Secondly the ERP signal under two kinds of stimulations is extracted; Again respectively paired sample T test is carried out to the ERP signal that each leads under two kinds of environmental stimulis, obtain each and lead the significant difference period of ERP signal under two kinds of stimulations; Finally by calculating the area that in the significant difference period, two ERP signals surround, two kinds can be obtained and stimulate the lower all difference area distributions within the significant difference period of leading of full brain, and then show by brain mapping the spatial distribution that two kinds stimulate the ERP signal difference brought out.
As shown in Figure 1, a kind of event related potential analytical method based on paired sample T test of the present invention, specifically comprises the steps:
1) design brings out experiment containing the brain electricity that two kinds stimulate, and utilizes the multiple scalp EEG signals of leading of brain wave acquisition equipment record, carries out preliminary pretreatment;
Described design brings out experiment containing the brain electricity that two kinds stimulate, be design two kinds of visions, the brain electricity that stimulates of audition or body sense brings out experiment, stimulate for two kinds of pictures, from international Emotional Picture storehouse (International Affective Picture System, IAPS) positive scene situation picture and each 10 of passive scene situation picture is chosen in, picture adopts the random mode occurred to present, every pictures presentative time is 1s, the quiescent stage of 4s is had, for the brain Electrical change calmed down caused by a upper pictures before picture presents.
Described utilizes the multiple scalp EEG signals of leading of brain wave acquisition equipment record, adopt Biosemi ActiveTwo eeg collection system record 15 tested 32 top guide skin EEG signals in experimentation, sample rate is 1024Hz, and the total duration of tracer signal is 100s.
Described preliminary pretreatment, be for low frequency wonder, High-frequency Interference and the eye removed in scalp EEG signals recording process such as to move at the interference of other electricity physiological signals, carry out becoming the pretreatment operation that average reference, 0.5-10Hz bandpass filtering and independent component analysis remove eye electricity etc. to original EEG signals.
2) the ERP signal under two kinds of stimulations is extracted;
ERP signal under the two or more stimulation of described extraction, comprises following process:
(1) split the whole paragraph header skin EEG signals after preliminary pretreatment, obtain the evoked brain potential fragment that resting electroencephalogramidentification fragment that 20 durations are 4s and 20 durations are 1s, wherein, two kinds stimulate each 10 corresponding of evoked brain potential fragment;
(2) choose the 200ms before stimulation presents, namely the rear 200ms of quiescent stage is benchmark brain electricity, and the average amplitude of Calculation Basis brain electricity, each evoked brain potential fragment is deducted benchmark brain electricity average amplitude, realize removing base line operations;
(3) respectively to going two kinds of evoked brain potential fragments after baseline to carry out superposed average, obtaining each ERP signal under stimulating at two kinds that leads of every subjects, being expressed as X={X
ijkand Y={Y
ijk, wherein, i=1,2 ..., N
1, N
1=15, N
1it is subjects's number; J=1,2 ..., N
2, N
2=32, N
2the number that leads, k=1,2 ..., N
3, N
3=1024, N
3it is number of data points.
By the scalp EEG signals superposed average under repeatedly repetitive stimulation obtain ERP signal according to being: spontaneous brain electricity is a kind stochastic signal, and multiple stacking can make spontaneous brain electricity amplitude reduce; And ERP signal has characteristic when significantly locking, multiple stacking can make ERP signal amplitude increase.
3) paired sample T test is carried out to the ERP signal under two kinds of stimulations, determine the period with significant difference;
Be averaging by the scalp EEG signals under 10 repetitive stimulations in the ERP signal obtained, the amplitude of spontaneous brain electricity is still very large, and therefore, each ERP composition is not given prominence to, ERP composition amplitude and preclinical extraction cannot be carried out, also cannot contrast between variety classes stimulates.Because although the spontaneous brain electricity under variety classes stimulation is not completely the same, but not there is significant diversity yet, therefore, T inspection is carried out by the ERP signal under stimulating variety classes, the significant difference period obtained must be the period that two true ERP signals have significant difference, furthermore achieved that the stripping of ERP signal and spontaneous brain electricity.
Described carries out paired sample T test to the ERP signal under two kinds stimulate, that the ERP sequence corresponding to each data point in each leading carries out paired sample T test, owing to being with a collection of tested two kinds of stimulations accepted in same experiment, so select paired sample T test, significance level is set to 0.05.For the individual kth data point of leading of jth, first set up a new variables Z={Z
ijk, Z
ijk=X
ijk-Y
ijk, i=1,2 ..., N
1, calculate the average of new variables
and variance
structure statistic
inspection t
jkwhether obeying degree of freedom is N
1the T distribution of-1, and calculate corresponding significance degree P
jkif, P
jk<0.05, then sequence
and sequence
have significant difference, the individual kth data point of leading of jth namely under two kinds of stimulations has significant difference, otherwise the individual kth data point of leading of the jth under two kinds of stimulations does not have significant difference.
Described determination has the period of significant difference, is to carry out on the basis determined to lead the jth under two kinds stimulate whether a kth data point have a significant difference, comprises following process:
(1) for the N that jth is led
3individual data point, if the consecutive numbers strong point k having more than 10, makes sequence
and sequence
have significant difference, the period that so these continuous print data point k is corresponding is exactly the period that jth the ERP signal led under two kinds of stimulations has significant difference, if there is not the consecutive numbers strong point k of more than 10, makes sequence
and sequence
have significant difference, then the ERP signal that jth is led under two kinds of stimulations does not have the significant difference period;
(2) respectively to each N led
3individual data point carries out paired sample T test, and each leading may contain multiple significant difference period, also may not containing there were significant differences period.The significant difference period distribution of leading according to all, selects the period that more than one relatively large, and make to comprise as far as possible the significant difference period that majority leads, the data point set that the selected significant difference period is corresponding is labeled as
wherein, r=1,2 ..., hop count when m, m are selected significant difference, range of choices be 1,2 ..., 100}, n
rbe the number of data points corresponding to each significant difference period respectively, value is all greater than 10.
4) calculate the difference area of ERP signal within the significant difference period under two kinds of stimulations, and draw brain mapping, determine difference brain district.
The difference area of ERP signal within the significant difference period under described calculating two kinds stimulates is each difference area of ERP signal within the selected significant difference period all calculated under two kinds of stimulations that lead to every subjects, wherein, in the significant difference period
the difference area of the ERP signal under two kinds of interior stimulations is
and superposed average is carried out to the data of all subjectss, obtain the ERP signal difference area that jth is led within the selected significant difference period under two kinds of stimulations
Described drafting brain mapping, determine difference brain district, for each significant difference period, ERP signal difference area drafting brain mapping under all stimulating according to all two kinds of leading, and then analyze two kinds and stimulate institute to bring out the spatial distribution state of ERP signal difference, the primary activation brain district obtained within the significant difference period distributes.
Brain electrical activity mapping is a kind of graph technology of concentrated expression brain electrophysiology information, usually multiple single feature different colours led is mapped its value size and the header planes color graphics (or gray scale difference image) obtained, wave amplitude and the distribution of cerebral nerve activity can be reacted more intuitively.
Although be described the preferred embodiments of the present invention by reference to the accompanying drawings above, the present invention is not limited to above-mentioned detailed description of the invention, and above-mentioned detailed description of the invention is only schematic, is not restrictive.
Those of ordinary skill in the art is under enlightenment of the present invention, and do not departing under the ambit that present inventive concept and claim protects, can also make a lot of form, these all belong within protection scope of the present invention.
Claims (7)
1., based on an event related potential analytical method for paired sample T test, it is characterized in that, comprise the steps:
1) design brings out experiment containing the brain electricity that two kinds stimulate, and utilizes the multiple scalp EEG signals of leading of brain wave acquisition equipment record, carries out preliminary pretreatment;
2) the ERP signal under two kinds of stimulations is extracted;
3) paired sample T test is carried out to the ERP signal under two kinds of stimulations, determine the period with significant difference;
4) calculate the difference area of ERP signal within the significant difference period under two kinds of stimulations, and draw brain mapping, determine difference brain district.
2. a kind of event related potential analytical method based on paired sample T test according to claim 1, it is characterized in that, step 1) described in preliminary pretreatment, being the interference for removing low frequency wonder, High-frequency Interference and eye galvanic electricity physiological signal in scalp EEG signals recording process, carrying out becoming the pretreatment operation that average reference, 0.5-10Hz bandpass filtering and independent component analysis remove eye electricity to original EEG signals.
3. a kind of event related potential analytical method based on paired sample T test according to claim 1, is characterized in that, step 2) described in the two or more stimulation of extraction under ERP signal, comprise following process:
(1) split the whole paragraph header skin EEG signals after preliminary pretreatment, obtain the evoked brain potential fragment that resting electroencephalogramidentification fragment that 20 durations are 4s and 20 durations are 1s, wherein, two kinds stimulate each 10 corresponding of evoked brain potential fragment;
(2) choose the 200ms before stimulation presents, namely the rear 200ms of quiescent stage is benchmark brain electricity, and the average amplitude of Calculation Basis brain electricity, each evoked brain potential fragment is deducted benchmark brain electricity average amplitude, realize removing base line operations;
(3) respectively to going two kinds of evoked brain potential fragments after baseline to carry out superposed average, obtaining each ERP signal under stimulating at two kinds that leads of every subjects, being expressed as X={X
ijkand Y={Y
ijk, wherein, i=1,2 ..., N
1, N
1=15, N
1it is subjects's number; J=1,2 ..., N
2, N
2=32, N
2the number that leads, k=1,2 ..., N
3, N
3=1024, N
3it is number of data points.
4. a kind of event related potential analytical method based on paired sample T test according to claim 1, it is characterized in that, step 3) described in two kinds stimulate under ERP signal carry out paired sample T test, that the ERP sequence corresponding to each data point in each leading carries out paired sample T test, for the individual kth data point of leading of jth, first set up a new variables Z={Z
ijk, Z
ijk=X
ijk-Y
ijk, i=1,2 ..., N
1, calculate the average of new variables
and variance
structure statistic
inspection t
jkwhether obeying degree of freedom is N
1the T distribution of-1, and calculate corresponding significance degree P
jkif, P
jk<0.05, then sequence
and sequence
have significant difference, the individual kth data point of leading of jth namely under two kinds of stimulations has significant difference, otherwise the individual kth data point of leading of the jth under two kinds of stimulations does not have significant difference.
5. a kind of event related potential analytical method based on paired sample T test according to claim 1, it is characterized in that, step 3) described in determination there is period of significant difference, be carry out on the basis determined to lead the jth under two kinds stimulate whether a kth data point have a significant difference, comprise following process:
(1) for the N that jth is led
3individual data point, if the consecutive numbers strong point k having more than 10, makes sequence
and sequence
have significant difference, the period that so these continuous print data point k is corresponding is exactly the period that jth the ERP signal led under two kinds of stimulations has significant difference, if there is not the consecutive numbers strong point k of more than 10, makes sequence
and sequence
have significant difference, then the ERP signal that jth is led under two kinds of stimulations does not have the significant difference period;
(2) the significant difference period distribution of leading according to all, selects the period that more than one relatively large, and make to comprise as far as possible the significant difference period that majority leads, the data point set that the selected significant difference period is corresponding is labeled as
wherein, r=1,2 ..., hop count when m, m are selected significant difference, range of choices be 1,2 ..., 100}, nr are the number of data points corresponding to each significant difference period respectively, and value is all greater than 10.
6. a kind of event related potential analytical method based on paired sample T test according to claim 1, it is characterized in that, step 4) described in calculating two kinds stimulate under the difference area of ERP signal within the significant difference period, the each difference area of ERP signal within the selected significant difference period all calculated under two kinds of stimulations that lead to every subjects, wherein, in the significant difference period
the difference area of the ERP signal under two kinds of interior stimulations is
and superposed average is carried out to the data of all subjectss, obtain the ERP signal difference area that jth is led within the selected significant difference period under two kinds of stimulations
7. a kind of event related potential analytical method based on paired sample T test according to claim 1, it is characterized in that, step 4) described in drafting brain mapping, determine difference brain district, for each significant difference period, ERP signal difference area drafting brain mapping under all stimulating according to all two kinds of leading, and then analyze two kinds and stimulate institute to bring out the spatial distribution state of ERP signal difference, the primary activation brain district obtained within the significant difference period distributes.
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105653039A (en) * | 2016-01-20 | 2016-06-08 | 同济大学 | Hand motion automatic correction and recognition method based on electroencephalogram signal detection |
| CN108784692A (en) * | 2018-05-11 | 2018-11-13 | 上海大学 | A kind of Feeling control training system and method based on individual brain electricity difference |
| CN110327043A (en) * | 2019-08-05 | 2019-10-15 | 哈尔滨工业大学 | A kind of event related potential waveform map method for solving based on sparse modeling |
| CN111008610A (en) * | 2019-12-16 | 2020-04-14 | 哈尔滨工业大学 | Information-related brain potential induction experimental method |
| CN111096744A (en) * | 2020-01-21 | 2020-05-05 | 苏州小蓝医疗科技有限公司 | Novel electroencephalogram signal acquisition implementation device and method |
| CN111281382A (en) * | 2020-03-04 | 2020-06-16 | 徐州市健康研究院有限公司 | Feature extraction and classification method based on electroencephalogram signals |
| CN113419626A (en) * | 2021-06-17 | 2021-09-21 | 深圳大学 | Method and device for analyzing steady-state cognitive response based on sound stimulation sequence |
| CN113520310A (en) * | 2021-03-15 | 2021-10-22 | 天津大学 | Electroencephalogram ERP-based touch information processing method |
| CN114081512A (en) * | 2021-12-20 | 2022-02-25 | 天津大学 | Method for evaluating influence degree of transcranial direct current stimulation on brain auditory processing capacity |
| CN114246594A (en) * | 2021-12-17 | 2022-03-29 | 天津大学 | Electroencephalogram signal processing method, and training method and device of background electroencephalogram prediction model |
| CN114246594B (en) * | 2021-12-17 | 2024-04-09 | 天津大学 | Electroencephalogram signal processing method, background electroencephalogram prediction model training method and device |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5331969A (en) * | 1985-07-30 | 1994-07-26 | Swinburne Limited | Equipment for testing or measuring brain activity |
| US5709214A (en) * | 1996-05-02 | 1998-01-20 | Enhanced Cardiology, Inc. | PD2i electrophysiological analyzer |
| US20020103428A1 (en) * | 2001-01-30 | 2002-08-01 | Decharms R. Christopher | Methods for physiological monitoring, training, exercise and regulation |
| US20060036153A1 (en) * | 2004-06-14 | 2006-02-16 | Laken Steven J | Questions and control paradigms for detecting deception by measuring brain activity |
| CN102778949A (en) * | 2012-06-14 | 2012-11-14 | 天津大学 | Brain-computer interface method based on SSVEP (Steady State Visual Evoked Potential) blocking and P300 bicharacteristics |
| CN103092971A (en) * | 2013-01-24 | 2013-05-08 | 电子科技大学 | Classification method used in brain-computer interfaces |
-
2014
- 2014-12-29 CN CN201410836452.3A patent/CN104545900B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5331969A (en) * | 1985-07-30 | 1994-07-26 | Swinburne Limited | Equipment for testing or measuring brain activity |
| US5709214A (en) * | 1996-05-02 | 1998-01-20 | Enhanced Cardiology, Inc. | PD2i electrophysiological analyzer |
| US20020103428A1 (en) * | 2001-01-30 | 2002-08-01 | Decharms R. Christopher | Methods for physiological monitoring, training, exercise and regulation |
| US20060036153A1 (en) * | 2004-06-14 | 2006-02-16 | Laken Steven J | Questions and control paradigms for detecting deception by measuring brain activity |
| CN102778949A (en) * | 2012-06-14 | 2012-11-14 | 天津大学 | Brain-computer interface method based on SSVEP (Steady State Visual Evoked Potential) blocking and P300 bicharacteristics |
| CN103092971A (en) * | 2013-01-24 | 2013-05-08 | 电子科技大学 | Classification method used in brain-computer interfaces |
Non-Patent Citations (1)
| Title |
|---|
| 陈湛愔: "情绪词汇加工的心理生理和病理机制 ——事件相关电位时空模式研究", 《医药卫生科技辑》 * |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105653039B (en) * | 2016-01-20 | 2018-12-18 | 同济大学 | A kind of hand motion automatic straightening recognition methods based on EEG signals detection |
| CN105653039A (en) * | 2016-01-20 | 2016-06-08 | 同济大学 | Hand motion automatic correction and recognition method based on electroencephalogram signal detection |
| CN108784692A (en) * | 2018-05-11 | 2018-11-13 | 上海大学 | A kind of Feeling control training system and method based on individual brain electricity difference |
| CN110327043B (en) * | 2019-08-05 | 2022-04-01 | 哈尔滨工业大学 | Event-related potential waveform map solving method based on sparse modeling |
| CN110327043A (en) * | 2019-08-05 | 2019-10-15 | 哈尔滨工业大学 | A kind of event related potential waveform map method for solving based on sparse modeling |
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| CN111008610B (en) * | 2019-12-16 | 2024-03-22 | 哈尔滨工业大学 | Information-related brain potential evoked experiment method |
| CN111096744A (en) * | 2020-01-21 | 2020-05-05 | 苏州小蓝医疗科技有限公司 | Novel electroencephalogram signal acquisition implementation device and method |
| CN111281382A (en) * | 2020-03-04 | 2020-06-16 | 徐州市健康研究院有限公司 | Feature extraction and classification method based on electroencephalogram signals |
| CN111281382B (en) * | 2020-03-04 | 2023-08-18 | 徐州市健康研究院有限公司 | Feature extraction and classification method based on electroencephalogram signals |
| CN113520310A (en) * | 2021-03-15 | 2021-10-22 | 天津大学 | Electroencephalogram ERP-based touch information processing method |
| CN113419626A (en) * | 2021-06-17 | 2021-09-21 | 深圳大学 | Method and device for analyzing steady-state cognitive response based on sound stimulation sequence |
| CN114246594A (en) * | 2021-12-17 | 2022-03-29 | 天津大学 | Electroencephalogram signal processing method, and training method and device of background electroencephalogram prediction model |
| CN114246594B (en) * | 2021-12-17 | 2024-04-09 | 天津大学 | Electroencephalogram signal processing method, background electroencephalogram prediction model training method and device |
| CN114081512A (en) * | 2021-12-20 | 2022-02-25 | 天津大学 | Method for evaluating influence degree of transcranial direct current stimulation on brain auditory processing capacity |
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