CN114415833A - Electroencephalogram asynchronous control software design method based on time-space frequency conversion SSVEP - Google Patents
Electroencephalogram asynchronous control software design method based on time-space frequency conversion SSVEP Download PDFInfo
- Publication number
- CN114415833A CN114415833A CN202210021741.2A CN202210021741A CN114415833A CN 114415833 A CN114415833 A CN 114415833A CN 202210021741 A CN202210021741 A CN 202210021741A CN 114415833 A CN114415833 A CN 114415833A
- Authority
- CN
- China
- Prior art keywords
- electroencephalogram
- module
- ssvep
- time
- stimulation
- 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.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
- G06F3/015—Input arrangements based on nervous system activity detection, e.g. brain waves [EEG] detection, electromyograms [EMG] detection, electrodermal response detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2203/00—Indexing scheme relating to G06F3/00 - G06F3/048
- G06F2203/01—Indexing scheme relating to G06F3/01
- G06F2203/011—Emotion or mood input determined on the basis of sensed human body parameters such as pulse, heart rate or beat, temperature of skin, facial expressions, iris, voice pitch, brain activity patterns
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Health & Medical Sciences (AREA)
- Evolutionary Computation (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Biology (AREA)
- Biomedical Technology (AREA)
- Dermatology (AREA)
- General Health & Medical Sciences (AREA)
- Neurology (AREA)
- Neurosurgery (AREA)
- Human Computer Interaction (AREA)
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
The invention belongs to the technical field of human-computer interaction, and particularly relates to a brain electrical asynchronous control software design method based on time-space frequency conversion SSVEP, which comprises the following steps: the liquid crystal display displays an asynchronous switch and a time-space frequency conversion SSVEP experimental paradigm in real time; a user wears an electroencephalogram signal acquisition cap to acquire data in real time; preprocessing the screened electroencephalogram data of the leads; classifying the electroencephalogram data; the asynchronous control module controls and converts the SVM algorithm classification result and controls the stimulation paradigm to be started; the control software module carries out instruction conversion on the FBCCA algorithm classification result, and each instruction controls one operation of the software; and the character output module performs character coding on the FBCCA algorithm classification result and is combined with the control software module to complete a character output function. The invention utilizes the stimulation paradigm based on the time-space frequency conversion SSVEP and combines the FBCCA algorithm, thereby greatly reducing the complexity of the brain control task analysis and obviously improving the efficiency of completing the brain control task.
Description
Technical Field
The invention belongs to the technical field of human-computer interaction, and particularly relates to a brain electrical asynchronous control software design method based on space-time frequency conversion SSVEP.
Background
With the development of artificial intelligence and machine learning, a brain-computer interface can be one of the most promising man-machine interaction systems in the 21 st century. Brain-Computer Interface (BCI) refers to a direct connection created between the Brain of a human or animal and an external device to exchange information between the Brain and the device. As a new, non-muscular communication channel, BCI enables a person to express ideas or manipulate devices directly through the brain without the aid of language or body movements. For severely motor disabled patients, BCI can communicate their intent to external devices, such as computers, home appliances, etc., thereby improving their quality of life.
Most of the current researches are to realize a brain control system with a single task based on P300 and a Steady-State Visual Evoked Potentials (SSVEP) experimental paradigm. P300 is one of ERP (event-related potentials) and is a specific evoked potential endogenous to and associated with cognitive function. The brain control system based on P300 has long command time because the evoked potential appears about 300ms after the stimulation. SSVEP is the phenomenon that when subjected to a visual stimulus of a fixed frequency, the visual cortex of the human brain produces a continuous response that is related to the stimulus frequency (at the fundamental frequency or at multiples of the stimulus frequency). The SSVEP based brain control system has a limited number of output instructions and cannot be used to control software that requires more instructions. In addition, the control equipment in the current brain control system needs the user to synchronously execute along with stimulation, and the stimulation cannot be controlled by the user after the system is started, so that the efficiency is low. In summary, the following problems mainly exist in the existing research of the brain control system: firstly, the instruction output is less or the time consumption is longer; secondly, the control equipment has a single mode, and only some simple mechanical equipment with less instruction requirements can be controlled; thirdly, the user must perform the stimulation synchronously, and the flexibility is not high.
Disclosure of Invention
Aiming at the technical problem, the invention provides a brain wave asynchronous control software design method based on the time-space frequency conversion SSVEP, and various kinds of software can be directly operated by adopting the multi-layer design of the time-space frequency conversion SSVEP paradigm; the SSVEP paradigm stimulation is asynchronously started based on the components of the eye electricity in the brain electricity, so that the user can flexibly control the software; in addition, a typewriter designed by using the space-time frequency conversion SSVEP paradigm basically meets the requirements of users for typewriting and punctuation marks at ordinary times, and the functions of the typewriter can be richer by combining with control software.
In order to solve the technical problems, the invention adopts the technical scheme that:
a brain wave asynchronous control software design method based on time-space frequency conversion SSVEP comprises the following steps:
s1, displaying the asynchronous switch and the time-space frequency conversion SSVEP experimental stimulation in real time by the liquid crystal display;
s2, the user wears an electroencephalogram signal acquisition cap to acquire data in real time and sends the data to an electroencephalogram preprocessing module at a computer end in real time;
s3, conducting lead screening on the data, and preprocessing the electroencephalogram data after the lead screening by the electroencephalogram preprocessing module;
s4, the electroencephalogram signal classification module classifies electroencephalogram data respectively by adopting a Support Vector Machine (SVM) algorithm and a filter bank correlation analysis (FBCCA) algorithm;
s5, the asynchronous control module controls and converts the SVM algorithm classification result and controls the stimulation paradigm to be started;
s6, the control software module carries out instruction conversion on the FBCCA algorithm classification result, and each instruction controls one operation of software;
and S7, the character output module performs character coding on the FBCCA algorithm classification result and completes the character output function by combining with the control software module.
The asynchronous switch in the S1 is two red marks in the middle of the screen, and the two red marks are randomly displayed according to set time; the time-space frequency conversion SSVEP stimulation in the S1 is used for activating electroencephalogram signals and is divided into two types: firstly, controlling software stimulation, and secondly, outputting stimulation by characters.
The time-space frequency conversion SSVEP stimulation duration in the S1 is 2S, all stimulation blocks are spatially divided into different frequencies in 0-1S, the frequencies correspond to the frequency range of 8-15Hz of the sine wave, and the spatial range watched by the user can be determined by analyzing the watching frequency in the 1S; each space range from 1s to 2s is divided into small stimulation blocks, each small stimulation block corresponds to the frequency in the range of 8-15Hz, and the specific stimulation block watched by the user can be determined by analyzing the watching frequency in the 2 s.
The method for preprocessing the screened electroencephalogram data of the screened leads by the electroencephalogram preprocessing module in the S3 comprises the following steps: the brain electricity preprocessing module is divided into two types: screening a frontal lobe lead channel, and filtering frontal lobe lead signals by a second-order Chebyshev high-pass filter with the cut-off frequency of 3Hz, a fourth-order Chebyshev low-pass filter with the cut-off frequency of 20Hz and a Chebyshev 50Hz trap respectively; and secondly, screening occipital lead channels, respectively filtering occipital lead signals by a second-order Chebyshev high-pass filter with the cutoff frequency of 4Hz, a fourth-order Chebyshev low-pass filter with the cutoff frequency of 80Hz and a Chebyshev 50Hz trap, and removing artifacts of the signals by using an Independent Component Analysis (ICA) method.
The signal classification module in S4 includes: firstly, classifying the electroencephalogram data after frontal lead preprocessing by using an SVM algorithm; and secondly, if the SVM classification result is that SSVEP stimulation is started, classifying the electroencephalogram data after the pillow leaf lead preprocessing by using an FBCCA algorithm.
And the software control module in the S6 combines the FBCCA classification result with the number of layers where the current interface is located, formulates a coding instruction, and sends the coding instruction to corresponding software through a UDP protocol.
The method for classifying the electroencephalogram data by the FBCCA algorithm in the S4 comprises the following steps: the FBCCA algorithm firstly decomposes the preprocessed brain electrical signals into N sub-band components, and then carries out the CCA algorithm on the N sub-components; performing weighted average on the obtained correlation coefficients on the N sub-frequency bands, and obtaining an overall correlation coefficient value corresponding to each stimulation frequency fk, wherein k is 1, 2.., and S; and finally, selecting the maximum correlation coefficient value from the S phase relation values, wherein the corresponding frequency is used as the final identification result.
The system comprises an asynchronous switch starting time-space frequency conversion SSVEP module, an electroencephalogram signal acquisition module, an electroencephalogram preprocessing module, an electroencephalogram signal classification module, a control software module and a character output module, wherein the asynchronous switch starting time-space frequency conversion SSVEP module is connected with the electroencephalogram signal acquisition module after stimulating the human brain through a light source, the electroencephalogram signal acquisition module is connected with the electroencephalogram preprocessing module, the electroencephalogram preprocessing module is connected with the electroencephalogram signal classification module, the electroencephalogram signal classification module is connected with the control software module, and the control software module is connected with the character output module.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention utilizes the stimulation paradigm based on the time-space frequency conversion SSVEP and combines the FBCCA algorithm, thereby greatly reducing the complexity of the brain control task analysis and obviously improving the efficiency of completing the brain control task.
2. The paradigm stimulation is controlled by adopting an asynchronous mode, so that the degree of freedom and operability of a user can be greatly improved, and the method is more flexible compared with the traditional synchronous control method.
3. The control software stimulation of the invention can effectively and flexibly control a plurality of different functional software through the idea of layered design.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It should be apparent that the drawings in the following description are merely exemplary, and that other embodiments can be derived from the drawings provided by those of ordinary skill in the art without inventive effort.
FIG. 1 is a schematic diagram of the placement of an electroencephalogram cap electrode worn by a user in accordance with the present invention;
FIG. 2 is a schematic diagram of the connection of the system of the present invention;
FIG. 3 is a front page interface diagram of an experimental paradigm of control software according to the present invention;
FIG. 4 is a diagram of a control music software stimulus interface of the present invention;
FIG. 5 is a diagram of a control WeChat software stimulation interface according to the present invention;
FIG. 6 is a diagram of a space-time frequency SSVEP character output paradigm interface in accordance with the present invention;
FIG. 7 is a flow chart of the process of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it is to be noted that, unless otherwise explicitly specified or limited, the terms "connected" and "connected" are to be interpreted broadly, e.g., as being fixed or detachable or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
Examples
A brain wave asynchronous control software design method based on space-time frequency conversion SSVEP comprises the following steps:
as shown in fig. 7, according to the flow shown in fig. 7, the following steps are adopted to describe the software for operating WeChat and QQ music by the user in detail:
step 1: a user wears an electroencephalogram signal acquisition cap according to the lead position as shown in figure 1, only needs to sit on a comfortable chair, a liquid crystal display is fixed at a position 60cm in front of the sight line, a time-space frequency conversion SSVEP stimulation paradigm interface is controlled and displayed by adopting an asynchronous switch, namely, an initialization stimulation block does not flicker, two asynchronous switches in the middle of a screen randomly appear, and only the ocular electrical components in the electroencephalogram are analyzed at the moment;
step 2: as shown in fig. 2, the electroencephalogram signal acquisition module transmits the electroencephalogram signal acquired by the electroencephalogram cap to the electroencephalogram preprocessing module in real time through a TCP protocol;
and step 3: conducting lead screening on the data, and conducting preprocessing such as band-pass filtering, 50Hz trap and artifact removal on the screened EEG data by a preprocessing module;
and 4, step 4: the electroencephalogram signal classification module classifies electroencephalogram data by adopting an SVM algorithm and an FBCCA algorithm respectively;
and 5: the asynchronous control module controls and converts an SVM classification result in the electroencephalogram classification module and starts a stimulation paradigm;
step 6: the control software module carries out instruction conversion on the FBCCA classification result in the electroencephalogram signal classification module, and each instruction controls one operation of software;
and 7: and the character output module performs character coding on the FBCCA classification result and is combined with the control software module to complete a character output function.
Further, in step 1, the asynchronous switch is two red marks in the middle of the screen, and the two marks are randomly displayed according to set time; because two types of software need to be controlled, the time-space frequency conversion SSVEP stimulation is divided into three layers, which are respectively: the first layer is the home control interface, as shown in FIG. 3; the second layer is a QQ music control interface, as shown in FIG. 4; the third layer is the WeChat control interface, as shown in FIG. 5.
When the stimulation starts to flash, the first layer home page interface is entered, as shown in fig. 3, the interface is divided into three stimulation blocks, the first stimulation block is a switching WeChat control interface, the second stimulation block is a switching QQ music control interface, and the third stimulation block is stimulation stopping; if the user watches the stimulation block marked with music at the moment, the interface is switched to a second layer of music control interface, as shown in fig. 4, the music control interface is divided into 12 stimulation blocks, the backward stimulation block returns to the previous layer of home page interface, the stop stimulation block stops stimulation, the search stimulation block enters a character output interface, and other stimulation blocks correspond to basic functions of QQ music software division; if the user watches the stimulation block marked with the WeChat on the home page interface, the interface is switched to a third layer of WeChat control interface, as shown in FIG. 5, the WeChat control interface is similar to the music control interface, and repeated description is omitted here;
as shown in fig. 6, the character output interface is divided into 44 stimulation blocks, including 40 common characters and punctuation marks and 4 event processing stimulation blocks, the process of backing back is to return to the previous layer of interface, SHIFT is to turn on the capital letter switch, determine that all characters in the character cache are output to the software search bar, and rewrite is to empty the character cache;
the time-space frequency conversion SSVEP stimulation duration is 2s, all stimulation blocks are spatially divided into different frequencies in 0-1s, the frequencies correspond to the frequency of a sine wave range of 8-15Hz, and the spatial range watched by a user can be determined by analyzing the watching frequency in 1 s; each space range from 1s to 2s is divided into small stimulation blocks, each small stimulation block corresponds to the frequency in the range of 8-15Hz, and the specific stimulation block watched by the user can be determined by analyzing the watching frequency in the 2 s.
Further, in step 3, the electroencephalogram preprocessing modules are divided into two types: screening a frontal lobe lead channel, and filtering frontal lobe lead signals by a second-order Chebyshev high-pass filter with the cut-off frequency of 3Hz, a fourth-order Chebyshev low-pass filter with the cut-off frequency of 20Hz and a Chebyshev 50Hz trap respectively; screening occipital leaf lead channels, respectively filtering occipital leaf lead signals by a second-order Chebyshev high-pass filter with the cutoff frequency of 4Hz, a fourth-order Chebyshev low-pass filter with the cutoff frequency of 80Hz and a Chebyshev 50Hz trap, and removing artifacts of the signals by an ICA (independent component analysis) method;
in fig. 1, the frontal lobe lead electrodes screened in the preprocessing module are: fp1, Fp2, F3, F7, F4, F8, Fz; the screened occipital lead electrodes were: o1, O2, Oz; the grounding electrode is: a1, A2, the lead position conforms to the international 10-20 standard, and the electrode impedance is kept below 30K ohms in the test.
Further, in step 4, when the user blinks purposefully during the asynchronous switch display, the stimulation interface can be turned on, and the signal classification module is divided into two types: firstly, classifying the brain electric data after frontal lobe lead preprocessing by using an SVM algorithm, and analyzing three conditions of no blink, no target blink and target blink; secondly, based on SVM classification results, if the classification results are purposeful blinks, stimulation is started, and then classification analysis is carried out on the electroencephalogram data after pillow leaf lead preprocessing by using FBCCA;
the method for classifying the electroencephalogram data by the filter bank correlation analysis FBCCA algorithm comprises the following steps: the FBCCA algorithm firstly decomposes the preprocessed brain electrical signals into N sub-band components, and then carries out the CCA algorithm on the N sub-components; performing weighted average on the obtained correlation coefficients on the N sub-frequency bands, and obtaining an overall correlation coefficient value corresponding to each stimulation frequency fk, wherein k is 1, 2.., and S; and finally, selecting the maximum correlation coefficient value from the S phase relation values, wherein the corresponding frequency is used as the final identification result.
Further, in step 6, as shown in fig. 2, the software control module combines the number of layers where the interface is located with the result of FBCCA classification in the signal classification module to formulate a coding instruction, and sends the coding instruction to the corresponding WeChat or QQ music software through a UDP protocol to complete control;
in summary, the user turns on the stimulus by two red markers and looks at the stimulus to flexibly and autonomously complete the control of the various software programs. Compared with the traditional SSVEP brain control system, the brain electrical asynchronous control software design method based on the time-space frequency conversion SSVEP provided by the invention is used for controlling a plurality of software and character outputs for the first time, and is effectively combined with an asynchronous switch, so that the flexibility and operability of a user are effectively improved, and an idea is developed for a medical auxiliary system of a disabled person in the future.
Although only the preferred embodiments of the present invention have been described in detail, the present invention is not limited to the above embodiments, and various changes can be made without departing from the spirit of the present invention within the knowledge of those skilled in the art, and all changes are encompassed in the scope of the present invention.
Claims (8)
1. A brain wave asynchronous control software design method based on time-space frequency conversion SSVEP is characterized in that: comprises the following steps:
s1, displaying the asynchronous switch and the time-space frequency conversion SSVEP experimental stimulation in real time by the liquid crystal display;
s2, the user wears an electroencephalogram signal acquisition cap to acquire data in real time and sends the data to an electroencephalogram preprocessing module at a computer end in real time;
s3, conducting lead screening on the data, and preprocessing the electroencephalogram data after the lead screening by the electroencephalogram preprocessing module;
s4, the electroencephalogram signal classification module classifies electroencephalogram data respectively by adopting a Support Vector Machine (SVM) algorithm and a filter bank correlation analysis (FBCCA) algorithm;
s5, the asynchronous control module controls and converts the SVM algorithm classification result and controls the stimulation paradigm to be started;
s6, the control software module carries out instruction conversion on the FBCCA algorithm classification result, and each instruction controls one operation of software;
and S7, the character output module performs character coding on the FBCCA algorithm classification result and completes the character output function by combining with the control software module.
2. The electroencephalogram asynchronous control software design method based on the space-time frequency conversion SSVEP, which is characterized by comprising the following steps of: the asynchronous switch in the S1 is two red marks in the middle of the screen, and the two red marks are randomly displayed according to set time; the time-space frequency conversion SSVEP stimulation in the S1 is used for activating electroencephalogram signals and is divided into two types: firstly, controlling software stimulation, and secondly, outputting stimulation by characters.
3. The electroencephalogram asynchronous control software design method based on the space-time frequency conversion SSVEP, which is characterized by comprising the following steps of: the time-space frequency conversion SSVEP stimulation duration in the S1 is 2S, all stimulation blocks are spatially divided into different frequencies in 0-1S, the frequencies correspond to the frequency range of 8-15Hz of the sine wave, and the spatial range watched by the user can be determined by analyzing the watching frequency in the 1S; each space range from 1s to 2s is divided into small stimulation blocks, each small stimulation block corresponds to the frequency in the range of 8-15Hz, and the specific stimulation block watched by the user can be determined by analyzing the watching frequency in the 2 s.
4. The electroencephalogram asynchronous control software design method based on the space-time frequency conversion SSVEP, which is characterized by comprising the following steps of: the method for preprocessing the screened electroencephalogram data of the screened leads by the electroencephalogram preprocessing module in the S3 comprises the following steps: the brain electricity preprocessing module is divided into two types: screening a frontal lobe lead channel, and filtering frontal lobe lead signals by a second-order Chebyshev high-pass filter with the cut-off frequency of 3Hz, a fourth-order Chebyshev low-pass filter with the cut-off frequency of 20Hz and a Chebyshev 50Hz trap respectively; and secondly, screening occipital lead channels, respectively filtering occipital lead signals by a second-order Chebyshev high-pass filter with the cutoff frequency of 4Hz, a fourth-order Chebyshev low-pass filter with the cutoff frequency of 80Hz and a Chebyshev 50Hz trap, and removing artifacts of the signals by using an Independent Component Analysis (ICA) method.
5. The electroencephalogram asynchronous control software design method based on the space-time frequency conversion SSVEP, which is characterized by comprising the following steps of: the signal classification module in S4 includes: firstly, classifying the electroencephalogram data after frontal lead preprocessing by using an SVM algorithm; and secondly, if the SVM classification result is that SSVEP stimulation is started, classifying the electroencephalogram data after the pillow leaf lead preprocessing by using an FBCCA algorithm.
6. The electroencephalogram asynchronous control software design method based on the space-time frequency conversion SSVEP, which is characterized by comprising the following steps of: and the software control module in the S6 combines the FBCCA classification result with the number of layers where the current interface is located, formulates a coding instruction, and sends the coding instruction to corresponding software through a UDP protocol.
7. The electroencephalogram asynchronous control software design method based on the space-time frequency conversion SSVEP, which is characterized by comprising the following steps of: the method for classifying the electroencephalogram data by the FBCCA algorithm in the S4 comprises the following steps: the FBCCA algorithm firstly decomposes the preprocessed brain electrical signals into N sub-band components, and then carries out the CCA algorithm on the N sub-components; performing weighted average on the obtained correlation coefficients on the N sub-frequency bands, and obtaining an overall correlation coefficient value corresponding to each stimulation frequency fk, wherein k is 1, 2.., and S; and finally, selecting the maximum correlation coefficient value from the S phase relation values, wherein the corresponding frequency is used as the final identification result.
8. The system of the brain electrical asynchronous control software design method based on the space-time frequency conversion SSVEP as claimed in any one of claims 1-7, wherein: the device comprises an asynchronous switch, a time-space frequency conversion SSVEP module, an electroencephalogram signal acquisition module, an electroencephalogram preprocessing module, an electroencephalogram signal classification module, a control software module and a character output module, wherein the asynchronous switch is used for opening the time-space frequency conversion SSVEP module, the electroencephalogram signal acquisition module is connected with the electroencephalogram signal acquisition module after stimulating the human brain through a light source, the electroencephalogram signal acquisition module is connected with the electroencephalogram preprocessing module, the electroencephalogram preprocessing module is connected with the electroencephalogram signal classification module, the electroencephalogram signal classification module is connected with the control software module, and the control software module is connected with the character output module.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210021741.2A CN114415833B (en) | 2022-01-10 | 2022-01-10 | Electroencephalogram asynchronous control software design method based on time-space frequency conversion SSVEP |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210021741.2A CN114415833B (en) | 2022-01-10 | 2022-01-10 | Electroencephalogram asynchronous control software design method based on time-space frequency conversion SSVEP |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114415833A true CN114415833A (en) | 2022-04-29 |
| CN114415833B CN114415833B (en) | 2022-08-12 |
Family
ID=81272152
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210021741.2A Active CN114415833B (en) | 2022-01-10 | 2022-01-10 | Electroencephalogram asynchronous control software design method based on time-space frequency conversion SSVEP |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114415833B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116400800A (en) * | 2023-03-13 | 2023-07-07 | 中国医学科学院北京协和医院 | ALS patient human-computer interaction system and method based on brain-computer interface and artificial intelligence algorithm |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160210552A1 (en) * | 2013-08-26 | 2016-07-21 | Auckland University Of Technology | Improved Method And System For Predicting Outcomes Based On Spatio/Spectro-Temporal Data |
| CN107037889A (en) * | 2017-03-06 | 2017-08-11 | 南昌大学 | The natural written character input method and system of a kind of view-based access control model brain-computer interface |
| CN109656356A (en) * | 2018-11-13 | 2019-04-19 | 天津大学 | A kind of asynchronous control system of SSVEP brain-computer interface |
-
2022
- 2022-01-10 CN CN202210021741.2A patent/CN114415833B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160210552A1 (en) * | 2013-08-26 | 2016-07-21 | Auckland University Of Technology | Improved Method And System For Predicting Outcomes Based On Spatio/Spectro-Temporal Data |
| CN107037889A (en) * | 2017-03-06 | 2017-08-11 | 南昌大学 | The natural written character input method and system of a kind of view-based access control model brain-computer interface |
| CN109656356A (en) * | 2018-11-13 | 2019-04-19 | 天津大学 | A kind of asynchronous control system of SSVEP brain-computer interface |
Non-Patent Citations (3)
| Title |
|---|
| YU JIANG等: "Signal classification algorithm in motor imagery based on asynchronous brain-computer interface", 《2019 IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE (I2MTC)》 * |
| 伏云发等: "基于SSVEP直接脑控机器人方向和速度研究", 《自动化学报》 * |
| 王工书等: "脑激活任务区分度的分析及应用研究", 《计算机工程与应用》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116400800A (en) * | 2023-03-13 | 2023-07-07 | 中国医学科学院北京协和医院 | ALS patient human-computer interaction system and method based on brain-computer interface and artificial intelligence algorithm |
| CN116400800B (en) * | 2023-03-13 | 2024-01-02 | 中国医学科学院北京协和医院 | ALS patient human-computer interaction system and method based on brain-computer interface and artificial intelligence algorithm |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114415833B (en) | 2022-08-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Yao et al. | Combining motor imagery with selective sensation toward a hybrid-modality BCI | |
| CN102793540B (en) | Method for optimizing audio-visual cognitive event-related potential experimental paradigm | |
| CN110347242A (en) | Audio visual brain-computer interface spelling system and its method based on space and semantic congruence | |
| Yao et al. | A novel calibration and task guidance framework for motor imagery BCI via a tendon vibration induced sensation with kinesthesia illusion | |
| CN108294748A (en) | A kind of eeg signal acquisition and sorting technique based on stable state vision inducting | |
| CN114415833B (en) | Electroencephalogram asynchronous control software design method based on time-space frequency conversion SSVEP | |
| CN110442244A (en) | A kind of reality-virtualizing game exchange method and system based on brain-computer interface | |
| EP4062889A1 (en) | Intelligent control apparatus and control method therefor, and intelligent wearable device | |
| CN112617863B (en) | Hybrid online computer-computer interface method for identifying lateral direction of left and right foot movement intention | |
| CN109326341A (en) | A kind of rehabilitation motion guiding method and apparatus | |
| CN109144238B (en) | Human-computer interaction system based on electro-oculogram coding and interaction method thereof | |
| CN116360600A (en) | Space positioning system based on steady-state visual evoked potential | |
| CN106708273A (en) | Switch device based on EOG and switch key implementing method | |
| CN110688013A (en) | English keyboard spelling system and method based on SSVEP | |
| CN113359991B (en) | Intelligent brain-controlled mechanical arm auxiliary feeding system and method for disabled people | |
| CN112783314B (en) | Brain-computer interface stimulation paradigm generating and detecting method, system, medium and terminal based on SSVEP | |
| RU2725782C2 (en) | System for communication of users without using muscular movements and speech | |
| CN113101021B (en) | Mechanical arm control method based on MI-SSVEP hybrid brain-computer interface | |
| CN116382481A (en) | Man-machine interaction glasses based on brain-computer interface technology | |
| Li et al. | A new directional intention identification approach for intelligent wheelchair based on fusion of EOG signal and eye movement signal | |
| CN206563944U (en) | A kind of switching device based on EOG | |
| CN206249101U (en) | Unmanned plane actuation means based on Steady State Visual Evoked Potential | |
| Dietrich et al. | Towards EEG-based eye-tracking for interaction design in head-mounted devices | |
| Wu et al. | Intelligent command and control of uav based on brain computer and eye tracking combined technology | |
| Bi et al. | A SSVEP brain-computer interface with the hybrid stimuli of SSVEP and P300 |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |