CN101477405B - Stable state vision inducting brain-machine interface method based on two frequency stimulation of left and right view field - Google Patents
Stable state vision inducting brain-machine interface method based on two frequency stimulation of left and right view field Download PDFInfo
- Publication number
- CN101477405B CN101477405B CN2009100762095A CN200910076209A CN101477405B CN 101477405 B CN101477405 B CN 101477405B CN 2009100762095 A CN2009100762095 A CN 2009100762095A CN 200910076209 A CN200910076209 A CN 200910076209A CN 101477405 B CN101477405 B CN 101477405B
- Authority
- CN
- China
- Prior art keywords
- brain
- frequency
- target
- stimulation
- visual
- 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
- 238000000034 method Methods 0.000 title claims abstract description 30
- 230000000638 stimulation Effects 0.000 title claims abstract description 20
- 210000004556 brain Anatomy 0.000 claims abstract description 26
- 230000000007 visual effect Effects 0.000 claims abstract description 17
- 238000000605 extraction Methods 0.000 claims abstract description 3
- 238000012360 testing method Methods 0.000 claims description 12
- 238000010219 correlation analysis Methods 0.000 claims description 11
- 238000001914 filtration Methods 0.000 claims description 5
- 230000004424 eye movement Effects 0.000 claims description 2
- 210000003128 head Anatomy 0.000 claims description 2
- 238000012545 processing Methods 0.000 claims description 2
- 230000009466 transformation Effects 0.000 claims description 2
- 230000003993 interaction Effects 0.000 abstract description 3
- 230000000763 evoking effect Effects 0.000 abstract 3
- 238000004458 analytical method Methods 0.000 abstract 1
- 230000004397 blinking Effects 0.000 abstract 1
- 238000007781 pre-processing Methods 0.000 abstract 1
- 238000010586 diagram Methods 0.000 description 4
- 241000239290 Araneae Species 0.000 description 3
- 230000008901 benefit Effects 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 230000005611 electricity Effects 0.000 description 2
- 239000000284 extract Substances 0.000 description 2
- 238000009877 rendering Methods 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 230000006854 communication Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000000052 comparative effect Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 210000001061 forehead Anatomy 0.000 description 1
- 238000002347 injection Methods 0.000 description 1
- 239000007924 injection Substances 0.000 description 1
- 210000001595 mastoid Anatomy 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 210000003205 muscle Anatomy 0.000 description 1
- 230000036403 neuro physiology Effects 0.000 description 1
- 230000007604 neuronal communication Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 210000004761 scalp Anatomy 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 230000004936 stimulating effect Effects 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- 210000000857 visual cortex Anatomy 0.000 description 1
Images
Landscapes
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
A steady-state visual evoked brain-computer interface method is based on the stimulation of two frequencies of left and right visual fields, and relates to a visual evoked presentation method of a visual brain-computer interface. The brain-computer interface method is characterized in that four different targets are combined through two blinking frequencies by utilizing a principle of the crossing of the left and the right visual fields; and a correlational analysis method is used to identify which target is watched by a user after brain electrical acquisition equipment records a multi-brain electrical signal in the visual brain area. The equipment and the method of the invention comprise a recording mode of the brain electrical signal, lead selection and signal preprocessing, and a feature extraction method. The visual target presentation method of the invention can be realized through a screen display of a common computer, and the size, the brightness and even the color of the target can be controlled easily. Compared with other steady-state visual evoked brain-computer interfaces, the presentation method can increase the number of the targets under a condition that the usable frequencies are limited, thereby improving the brain-computer interaction ability.
Description
Technical field
The invention belongs to human-computer interaction technique field, can help external units such as physical disabilities' operational computations machine, also can be used for the electronic entertainment of novel brain-machine interaction.
Background technology
The brain-computer interface system is a kind of periphery muscle and neural communication system that participates in of not needing, it is intended to set up the direct communication channel in a human brain and the periphery world, by extracting the feature in the EEG signals, thereby the brain instruction or the information that identify are passed to controlled external unit, finally finish the direct control of brain external unit.EEG signals adopts non-intrusion type scalp eeg recording to obtain usually.
The steady-state induced current potential of vision is a kind of signal that brain-computer interface BCI often uses, and it is the response signal that the brain visual cortex stimulates greater than the 6Hz flicker for the external world, can obtain by extracting the occipital region EEG signals.With respect to for the brain-computer interface system of other signal, have higher rate of information transmission usually based on the brain-computer interface of stable state vision inducting current potential, the system that more has concurrently is easy, only needs the advantage of less training.In order to make the brain-computer interface system more portable, practical, the screen that uses a computer is good methods as the stimulation means, but at the screen that uses a computer during as the target presentation mode, because the problem of intrinsic refresh rate, excite the optional frequency scope of stable state vision inducting current potential very little, make target present limited amount.In the use for the disabled person, less target means the increase of selecting level, and this will increase the complexity of user's degree of fatigue and System menu.
Chinese patent " extracts the method for brain-computer interface signal " (200310121033.3) based on transient visual induced potential, and " based on the control system of brain electricity steady-state induced response " (99122161.3) employing is traditional stable state vision inducting current potential target presentation mode, one of them light-emitting diodes plate forms a target, this light-emitting diodes plate is with fixing frequency scintillation, the user makes when watching this target attentively, can obtain the peak figure of respective frequencies by the fast fourier transform of calculating EEG signals.A light-emitting diodes plate uses a frequency, forms a target, therefore adopts n frequency can form n target.If with common display is visual stimulus equipment,, have only less several frequencies to use owing to be subjected to the restriction of screen refresh rate.
The present invention proposes to adopt the target presentation mode of left and right view field double frequency stimulation.Utilize the left and right view field intersection principle that proves already on the neuro-physiology, use two light-emitting diodes plates to stimulate simultaneously, synthetic target with two frequencies.The frequency content of part about the brain occipital region utilizes the canonical correlation analysis method to detect.By the combination of frequency of left and right view field, n frequency can form square number of targets of n, and the target numbers that can realize than traditional target presentation mode has improved n doubly.Have very big theoretical research and practical application meaning.
Summary of the invention
The present invention has proposed a kind of new target rendering method on the basis of the classical stimulus modality of stable state vision inducting current potential, can adopt limited frequency resource to realize more target identification.
The invention is characterized in that this method contains following steps successively:
Step 3, according to step 2, use described two the flicker light-emitting blocks according to combination, form 4 kinds of targets after, carry out according to the following steps:
Step 31, the user watches any in described 4 kinds of targets attentively;
Step 32, described computing machine is sending the parallel port synchronizing signal simultaneously, by described test electrode record brain wave, use correlation analysis method to calculate the related coefficient of brain occipital region left and right sides two halves and frequency of stimulation respectively, comprise following operation successively: at first brain wave is made Filtering Processing, disturb to get rid of the 50Hz power frequency; Next is made baseline and handles, and removes baseline wander each brain wave is all become stably, and average is close to 0 signal; Once more, reduce the electric artefact of eye, remove the pairing data of the stimulation that comprises eye movement; Then, the stimulation of each brain electric potential is alignd constantly, choose the left and right sides, occipital region two halves EEG signals respectively as two set; At last, described computing machine is done correlation analysis extraction frequency content information according to frequency of stimulation and left and right sides two halves EEG signals;
Step 33 according to the size of the described related coefficient of calculating gained, is judged the target that the user watches attentively;
Step 34: described computing machine carries out corresponding visual feedback prompting according to detected target;
Step 35: if finish target selection smoothly, then return step 32, repeating step 32-35 carries out next round and selects.
The experiment proved that the present invention has the following advantages:
(1) based on the stable state vision inducting current potential, so have the advantage of stable state vision inducting current potential brain-computer interface, that is: the transfer rate height only needs less training, and equipment is simple.
(2) while uses the stimulus modality of two frequencies of left and right view field to construct target on (1) basis, then can significantly improve target numbers.
(3) for the high α frequency range of general people's signal to noise ratio (S/N ratio), the frequency that can use is very limited, and uses this target presentation mode, can construct more target in the high α frequency range of signal to noise ratio (S/N ratio).
(4) adopting the eeg recording method, is not have the harmless method of wound.
Description of drawings
Fig. 1 goal stimulus mode chart;
Fig. 2 selection synoptic diagram that leads;
Fig. 3 Target Recognition strategy block diagram;
Fig. 4 brain wave testing result example;
Fig. 5 canonical correlation analysis coefficient comparison diagram.
Embodiment
The stimulus modality of this paper invention is seen shown in Figure 1.The stimulation piece of spider the right and left respectively is 0.5cm apart from spider, and the stimulation block size is 3cm * 6cm.A stimulates piece to be positioned at the left field of vision that is tried, and B stimulates piece to be positioned at right visual field.Two stimulate piece to be combined into a target, be subjected to injection test to look the spider position, and conventional stable state vision inducting brain-machine interface adopt a frequency scintillation piece to present target.According to the visual field intersection principle of Neuscience, the frequency content in two visuals field will be projected in the left and right sides, brain occipital region two halves respectively, and the letter among the figure ' A ', ' B ' have identified the corresponding relation of projection.
Here with 14, two frequencies of 18Hz are example.Two frequencies make up according to left and right view field, then can form 2 kinds of different targets, add the target that left and right view field uses same frequency to form, finally can present 4 kinds of targets altogether, as shown in table 1, wherein the frequency of stimulation of target 1 and target 2 left and right view fields is inequality, and target 3 has adopted identical frequency of stimulation respectively with target 4 left and right view fields.
The combination of frequency of table 1 target and projection relation
Conventional stable state vision inducting brain-machine interface utilizes two frequencies can only present two targets, that is, and and 4 two targets of target 3 and target.And adopt that two frequencies of left and right view field stimulate simultaneously method, system can have more two targets, that is, the form of target 1 and target 2.
User's EEG signals adopts general eeg recording equipment, and sample frequency is 1000Hz.Adopt 64 during record and lead the position 10-20 system in accordance with international practices of leading.Reference electrode is positioned at mastoid process position, the left and right sides.
As long as the user watches the position that the central cross of the target of all selections is pitched attentively, the EEG signals that its brain rear portion visual zone records will comprise the combination frequency A of user institute fixation object and the information of B.Can detect these information by the following method, and judge the target that the user watches attentively.
(Canonical Correlation Analysis is a kind of statistical method of linear relationship between two groups of multidimensional variables of research CCA) to canonical correlation analysis, and it has been extended to the simple correlation analysis category of two groups of variablees.Its final goal is to seek two linear combinations, makes two groups of multidimensional variables by after this linear combination, its related coefficient maximum.This related coefficient has been described the relation that two group data sets close.CCA has obtained using widely in the research of practical problems, equally also is suitable in the eeg data analysis.Use the CCA method for eeg data, two group data sets close respectively from EEG signals and stimulus signal.The n that note test the is gathered eeg data that leads is
x＝(x
1?x
2?x
3...x
n)，
Stimulus signal is
y＝(cos2πft?sin2πft?cos4πft?sin4πft?cos6πft?sin6πft)，
Then CCA can be defined as following problem: seek vectorial W respectively
xWith W
y, make x, y at vectorial W
xAnd W
yOn projection X=x
TW
x, Y=y
TW
yBetween the correlation maximum, promptly
Maximum.ρ wherein is exactly the needed related coefficient of this method.
EEG signals is led from 16 that are positioned near the occipital region in this method.Consider this stimulation rendering method relate to about two visuals field, so be axis of symmetry with 16 data of leading with Pz, POz, OZ, by about be divided into two groups, lead for every group 8, note is made EEG-L8, EEG-R8 (Fig. 2).In like manner, because there is the stimulus signal of two frequencies (A=14, B=18Hz) simultaneously in left and right view field, so the stimulus signal data acquisition is divided into two groups according to the frequency difference, note is made stimA, stimB.Data set cooperation CCA to two groups of EEG signals and twice different frequency analyzes, and obtains 4 related coefficients at last altogether, and note is made ρ LA, ρ LB, ρ RA, ρ RB respectively.
After obtaining these 4 related coefficients, the rule of intersecting according to left and right view field, which target what can judge that the user watches attentively by following form is.
Table 2 target identification method
With this method 5 users are tested.Compare with traditional target presentation mode.Step following (process flow diagram is referring to Fig. 4):
(1) get 8 flicker luminous plaques, two flicker frequencies form in twos 4 targets (AB, BA, AA, BB);
(2) user selects the central authorities of the fixation object of wanting;
(3) the record brain wave uses the canonical correlation analysis method, extracts the frequency content of part about the occipital region;
Synchronous recording electroencephalogram during test is got P1～P8, PO3～PO8, O1, O2 electrode in the standard brain electricity 10-20 lead system, and ear is a reference electrode, and ground electrode is at the forehead place.The pre-service of EEG signals mainly comprises filtering, removes baseline wander and reduces the electric artefact of eye.The purpose of filtering is that the power frequency of getting rid of 50Hz is disturbed, and other noises, adopts bandpass filtering usually, and high pass is generally 0.1-1Hz, and low pass is generally 10-20Hz.Next step is each time stimuli responsive to be made canonical correlation handle, to detect frequency content.With time reference point (stimulating the zero hour) alignment, each sample data and frequency of stimulation template data set that again will be corresponding with the same time be done canonical correlation analysis, can obtain 4 related coefficients with a plurality of measured signals.
(4) relatively, judge the target that the experimenter watches attentively according to table 1 according to related coefficient.
For judging the Target Recognition situation, need more final related coefficient size, Fig. 5 has shown the comparative result of related coefficient.A small amount of test result showed mean test time 4 seconds (not comprising the electrode set-up time), and verification and measurement ratio and false drop rate all are satisfied with and are reached request for utilization.Accuracy is more lower slightly than conventional target presentation mode, but still meets request for utilization, and number of targets becomes frequency number multiple to increase, and has significantly improved target numbers.
Claims (1)
1. based on the stable state vision inducting brain-machine interface method of two frequency stimulation of left and right view field, it is characterized in that this method contains following steps successively:
Step 1, brain electrical testing electrode is placed on user's head occipital region P1～P8, PO3～PO8, O1, O2 position, reference electrode is placed on this user's ear, ground-electrode ground connection, the EEG signals that described test electrode obtains through amplify and mould/transformation of variables after be sent to USB interface of computer;
Step 2, two flicker light-emitting blocks that are of moderate size are positioned over following position, user the place ahead: with the eyes center is focus, about ornaments, spacing is about 1cm, the centre position of flicker light-emitting block is watched focus attentively for the user, selected frequency A and frequency B, frequency range all is 8～13Hz, the flicker light-emitting block constantly glimmers with described frequency A or B respectively about described, with this, described two flicker light-emitting blocks are combined into a target, present 4 kinds of targets according to left and right sides various combination, that is: the frequency of two flicker light-emitting blocks is A or B all about, and is respectively frequency A or frequency B;
Step 3, according to step 2, use described two the flicker light-emitting blocks according to combination, form 4 kinds of targets after, carry out according to the following steps:
Step 31, the user watches any in described 4 kinds of targets attentively;
Step 32, described computing machine is sending the parallel port synchronizing signal simultaneously, by described test electrode record brain wave, use correlation analysis method to calculate the related coefficient of brain occipital region left and right sides two halves and frequency of stimulation respectively, comprise following operation successively: at first brain wave is made Filtering Processing, disturb to get rid of the 50Hz power frequency; Next is made baseline and handles, and removes baseline wander each brain wave is all become stably, and average is close to 0 signal; Once more, reduce the electric artefact of eye, remove the pairing data of the stimulation that comprises eye movement; Then, the stimulation of each brain electric potential is alignd constantly, choose the left and right sides, occipital region two halves EEG signals respectively as two set; At last, described computing machine is done correlation analysis extraction frequency content information according to frequency of stimulation and left and right sides two halves EEG signals;
Step 33 according to the size of the described related coefficient of calculating gained, is judged the target that the user watches attentively;
Step 34: described computing machine carries out corresponding visual feedback prompting according to detected target;
Step 35: if finish target selection smoothly, then return step 32, repeating step 32-35 carries out next round and selects.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2009100762095A CN101477405B (en) | 2009-01-05 | 2009-01-05 | Stable state vision inducting brain-machine interface method based on two frequency stimulation of left and right view field |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2009100762095A CN101477405B (en) | 2009-01-05 | 2009-01-05 | Stable state vision inducting brain-machine interface method based on two frequency stimulation of left and right view field |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN101477405A CN101477405A (en) | 2009-07-08 |
| CN101477405B true CN101477405B (en) | 2010-11-24 |
Family
ID=40838130
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2009100762095A Expired - Fee Related CN101477405B (en) | 2009-01-05 | 2009-01-05 | Stable state vision inducting brain-machine interface method based on two frequency stimulation of left and right view field |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN101477405B (en) |
Families Citing this family (24)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101887307B (en) * | 2010-06-03 | 2011-12-07 | 西安交通大学 | Multi-frequency time sequence combined steady-stage visual evoked potential brain-computer interface method |
| US8516568B2 (en) | 2011-06-17 | 2013-08-20 | Elliot D. Cohen | Neural network data filtering and monitoring systems and methods |
| CN102512164A (en) * | 2011-11-23 | 2012-06-27 | 电子科技大学 | Coding and identification method for multi-frequency arrangements of SSVEP-BCI (steady state visual evoked potential-brain computer interface) system |
| CN102609618B (en) * | 2012-02-07 | 2015-01-14 | 清华大学 | Method for calculating brain asymmetric index based on information flow gain |
| CN102722244B (en) * | 2012-05-25 | 2014-10-15 | 西安交通大学 | Steady-state evoked potential brain-computer interface method based on motion turning vision sensing |
| CN102799274B (en) * | 2012-07-17 | 2015-04-22 | 华南理工大学 | Method of asynchronous brain switch based on steady state visual evoked potentials |
| KR101354321B1 (en) * | 2012-11-27 | 2014-02-05 | 현대자동차주식회사 | Apparatus for inputting command using movement of pupil and method thereof |
| CN103268149B (en) * | 2013-04-19 | 2016-06-15 | 杭州电子科技大学 | A kind of real-time proactive system control method based on brain-computer interface |
| CN103405230B (en) * | 2013-08-07 | 2015-04-15 | 清华大学 | System and method for detecting color perception based on electroencephalogram evoked potential |
| CN111419179B (en) * | 2013-11-07 | 2023-04-07 | 赛佛欧普手术有限公司 | System for detecting neurological function |
| CN105242784B (en) * | 2015-10-12 | 2018-02-09 | 中国医学科学院生物医学工程研究所 | Steady State Visual Evoked Potential brain-machine interface method based on crossmodulation frequency |
| CN105868562A (en) * | 2016-03-31 | 2016-08-17 | 天津大学 | Nerve work efficiency enhancing method and device based on steady state visual evoked potential modulation |
| CN105942975B (en) * | 2016-04-20 | 2018-07-06 | 西安电子科技大学 | Brain-electrical signal processing method based on stable state vision inducting |
| CN106249895B (en) * | 2016-08-09 | 2019-03-05 | 清华大学 | Man-machine interaction method and system when a kind of robot environment-identification changes |
| CN106155329B (en) * | 2016-09-06 | 2019-01-08 | 西安交通大学 | Steady-state induced current potential brain-computer interface method based on reciprocally swinging visual perception |
| CN107296586A (en) * | 2017-06-20 | 2017-10-27 | 黄涌 | Collimation error detection device/method and writing system/method based on the equipment |
| CN108919947B (en) * | 2018-06-20 | 2021-01-29 | 北京航空航天大学 | Brain-computer interface system and method realized through visual evoked potential |
| CN109271020B (en) * | 2018-08-23 | 2020-09-01 | 西安交通大学 | Eye tracking-based steady-state vision-evoked brain-computer interface performance evaluation method |
| CN110393527B (en) * | 2019-08-12 | 2021-12-28 | 东南大学 | Steady-state visual evoked potential detection method based on beamforming and CCA |
| CN110811644B (en) * | 2019-10-15 | 2022-07-12 | 南方科技大学 | Visual fatigue measuring method and system, storage medium and electronic equipment |
| CN111543986B (en) * | 2020-05-12 | 2021-03-02 | 清华大学 | Electroencephalogram event synchronization method without hardware connection |
| CN112631173B (en) * | 2020-12-11 | 2022-08-23 | 中国人民解放军国防科技大学 | Brain-controlled unmanned platform cooperative control system |
| CN114115547B (en) * | 2022-01-27 | 2022-05-13 | 中国医学科学院生物医学工程研究所 | Target presentation method and device of hybrid brain-computer interface |
| CN115309272B (en) * | 2022-10-11 | 2023-03-24 | 季华实验室 | Multi-agent control method and device and electronic equipment |
-
2009
- 2009-01-05 CN CN2009100762095A patent/CN101477405B/en not_active Expired - Fee Related
Also Published As
| Publication number | Publication date |
|---|---|
| CN101477405A (en) | 2009-07-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101477405B (en) | Stable state vision inducting brain-machine interface method based on two frequency stimulation of left and right view field | |
| Guo et al. | Heart rate variability signal features for emotion recognition by using principal component analysis and support vectors machine | |
| Volosyak | SSVEP-based Bremen–BCI interface—boosting information transfer rates | |
| CN101464728B (en) | Human-machine interaction method with vision movement related neural signal as carrier | |
| CN106267514B (en) | Feeling control system based on brain electricity feedback | |
| CN102609090B (en) | Electrocerebral time-frequency component dual positioning normal form quick character input method | |
| CN105824418B (en) | A kind of brain-computer interface communication system based on asymmetric visual evoked potential | |
| CN103793058A (en) | Method and device for classifying active brain-computer interaction system motor imagery tasks | |
| Ko et al. | Enhancing the hybrid BCI performance with the common frequency pattern in dual-channel EEG | |
| WO2019144776A1 (en) | Coding-decoding method for brain-machine interface system based on asymmetric electroencephalographic features | |
| CN103472922A (en) | Destination selecting system based on P300 and SSVEP (Steady State Visual Evoked Potential) hybrid brain-computer interface | |
| Ge et al. | Training-free steady-state visual evoked potential brain–computer interface based on filter bank canonical correlation analysis and spatiotemporal beamforming decoding | |
| CN102722728A (en) | Motion image electroencephalogram classification method based on channel weighting supporting vector | |
| CN108681391A (en) | A kind of EEG signals dummy keyboard design method based on multi-mode | |
| CN111580643A (en) | Brain-computer interface method based on steady-state asymmetric visual evoked potential | |
| CN102654793A (en) | Electrocerebral-drive high-reliability control system based on dual-mode check mechanism | |
| Jianfeng et al. | Multi-feature authentication system based on event evoked electroencephalogram | |
| CN106484106A (en) | The non-attention event related potential brain-machine interface method of visual acuity automatic identification | |
| CN105843377A (en) | Hybrid brain-computer interface based on asynchronous parallel induction strategy | |
| CN105242784B (en) | Steady State Visual Evoked Potential brain-machine interface method based on crossmodulation frequency | |
| Nwachukwu et al. | An SSVEP recognition method by combining individual template with CCA | |
| CN112783314B (en) | Brain-computer interface stimulation paradigm generating and detecting method, system, medium and terminal based on SSVEP | |
| CN103440039B (en) | Brain machine communication speed self-adaptation control method based on feedback of status | |
| Zhang et al. | FPGA implementation of visual noise optimized online steady-state motion visual evoked potential BCI System | |
| Webster et al. | An unsupervised channel-selection method for SSVEP-based BCI systems |
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 | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20101124 Termination date: 20130105 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |