CN110688013A - English keyboard spelling system and method based on SSVEP - Google Patents
English keyboard spelling system and method based on SSVEP Download PDFInfo
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- CN110688013A CN110688013A CN201910961036.9A CN201910961036A CN110688013A CN 110688013 A CN110688013 A CN 110688013A CN 201910961036 A CN201910961036 A CN 201910961036A CN 110688013 A CN110688013 A CN 110688013A
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- 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
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- 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
- G06F3/013—Eye tracking input arrangements
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- 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
- G06F3/02—Input arrangements using manually operated switches, e.g. using keyboards or dials
- G06F3/023—Arrangements for converting discrete items of information into a coded form, e.g. arrangements for interpreting keyboard generated codes as alphanumeric codes, operand codes or instruction codes
Abstract
The invention discloses an English keyboard spelling system and a spelling method based on SSVEP, which comprises a user display module, an EEG acquisition unit and a processing module, wherein a unique key value input is determined through two times of frequency selection, compared with a determinant keyboard spelling system, the English keyboard spelling system has the characteristics of high accuracy and only needs to perform secondary identification, so that the speed of keyboard input is accelerated.
Description
Technical Field
The invention relates to a novel English keyboard input method for performing equipment control and man-machine interaction by using steady-state visual evoked potentials of electroencephalogram signals SSVEP, and belongs to the technical field of digital signal processing.
Background
The Brain-Computer Interface (BCI) is a Brain-Computer communication system independent of the normal output path formed by the peripheral nerves and muscles of the Brain, and is a novel human-Computer interaction system, which establishes connection between the human body and the Computer and realizes human-Computer interaction by controlling the Computer or other external electronic devices by Brain electrical signals collected from the human Brain. The research of the brain-computer interface system makes it possible for the brain to interact with the outside directly, and has wide potential application in the fields of medicine, intelligent control, military and the like.
The brain-computer interface technology mainly comprises five steps: signal acquisition; signal preprocessing, feature extraction, pattern recognition and classification and control of external devices.
Scalp electroencephalography (EEG) was first used for BCI systematic studies, and in the seventies of the last century, the ARPA agency of the united states department of defense began to attach importance to the study of immersive and compact interactive technologies between humans and computers, and in the middle and late nineties, with scientists' deeper understanding of brain functional principles, the rapid development of high-performance electronic products, and the study of BCI technology came to a new climax.
The BCI group of the university of Graz technology, austria, has been in a leading position in related research: they developed applications such as computer games based on the autonomously designed BCI system named Graz, and assisted the recovery of arm function of paralyzed patients with functional electrical stimulation, so this group was the pioneer of the practical BCI technology.
The U.S. Wadworth Center has also been in the lead position in the BCI study, which studies BCI systems that allow users to achieve 2-dimensional mouse movement through autonomic control of the μ rhythm. At present, highly paralyzed patients can automatically complete daily operations such as spelling, e-mail sending and receiving, simple voice communication and the like based on their BCI system.
SSVEP (Steady-State Visual Evoked Potentials) is a kind of current BCI system with higher stability, and the physiological basis is that when human eyes watch periodic flicker with a certain frequency, a continuous response related to the stimulation frequency (fundamental frequency or frequency multiplication of the stimulation frequency) is induced in the Visual zone of the cerebral cortex. This response can be detected in the scalp brain electrical activity of the occipital lobe area of the brain using non-invasive electrodes. The brain-computer interface system has the advantages of non-invasiveness, high accuracy, no need of training and the like.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects in the prior art, the invention provides an English keyboard spelling system and a spelling method based on SSVEP, which adopt a real-time electroencephalogram feature extraction and mode recognition method to improve the performance of a brain-computer interface, and acquire electroencephalogram signals of the brain to external stimulation by analyzing the subjective will of electroencephalogram data acquired on the scalp in an off-line manner to be used as driving signals of external equipment to realize character input.
The technical scheme is as follows: in order to achieve the purpose, the invention adopts the technical scheme that:
an English keyboard spelling system based on SSVEP comprises a user display module, an electroencephalogram acquisition unit and a processing module;
the user display module comprises a display interface and an SSVEP evoked interface;
the display interface is used for displaying the inputted letters;
the SSVEP inducing interface comprises 9 virtual key frames, wherein the 9 virtual key frames comprise 1 symbol key frame and 8 letter key frames, and 26 letters are distributed in the 8 letter key frames; the 9 virtual key frames flash at different frequencies; flashing each letter in the virtual key frame at different frequencies;
the electroencephalogram acquisition unit is worn on the head of a user; when a user watches a virtual key frame needing to be selected in an SSVEP (secure visual evoked potential) evoked interface, acquiring an electroencephalogram signal I evoked by the user at the moment; when a user watches letters needing to be selected in a virtual key frame which is amplified and adjusted in an SSVEP (simple visual evoked potential) evoked interface, acquiring an electroencephalogram signal II which is evoked by the user at the moment;
the processing module is used for extracting a characteristic frequency signal according to the generated electroencephalogram signal I to obtain a characteristic frequency signal I, determining a virtual key frame to be selected according to the characteristic frequency signal I, and controlling the SSVEP inducing interface to amplify and adjust the selected virtual key frame; the character input method is used for extracting a characteristic frequency signal according to the generated electroencephalogram signal II to obtain a characteristic frequency signal II, determining letters in a virtual key frame to be selected according to the characteristic frequency signal II, and displaying the letters to be selected through a display interface to finish character input.
Preferably: the electroencephalogram acquisition unit is an electroencephalogram electrode cap.
An English keyboard spelling method based on SSVEP comprises the following steps:
step 1, a user wears an electroencephalogram acquisition unit and directly faces an SSVEP (steady state visual evoked potential) evoked interface, and 9 virtual key frames flicker at different frequencies;
step 2, selecting a virtual key frame:
step 21, enabling human eyes of a user to watch a virtual key frame to be selected, stimulating human brain to generate a first SSVEP signal, and enabling the user to simultaneously perform left-hand and right-hand motor imagery to generate a first motor imagery electroencephalogram signal;
step 22, acquiring a SSVEP signal I and a motor imagery electroencephalogram signal I by an electroencephalogram acquisition unit to serve as the electroencephalogram signal I;
step 23, filtering the first electroencephalogram signal to remove ocular artifacts; extracting and classifying SSVEP characteristic frequency signals of the first electroencephalogram signal to obtain a first SSVEP characteristic frequency signal;
step 24, outputting a specific number one according to the SSVEP characteristic frequency signal one, then writing the specific number one into a newly-built file of the background, obtaining the virtual key frame characteristic to be fed back according to the number one, inputting the virtual key frame characteristic into a page JavaScript to change the page of the SSVEP inducing interface, and enabling the SSVEP inducing interface to amplify, adjust and flicker the selected virtual key frame so as to finish the selection of the virtual key frame;
step 3, character selection:
step 31, watching letters to be selected in the virtual key frame selected in the step 24, flashing the letters in the selected virtual key frame at different frequencies to stimulate the brain of a human to generate a second SSVEP signal, and simultaneously performing motor imagery of the left hand and the right hand by a user to generate a second motor imagery electroencephalogram signal;
step 32, acquiring an SSVEP signal II and a motor imagery electroencephalogram signal II by an electroencephalogram acquisition unit to serve as the electroencephalogram signal II;
step 23, filtering the electroencephalogram signal II to remove ocular artifacts; extracting and classifying SSVEP characteristic frequency signals of the electroencephalogram signal II to obtain an SSVEP characteristic frequency signal II;
step 24, outputting a specific number II according to the SSVEP characteristic frequency signal II, then writing the specific number II into a newly-built file of the background, obtaining character characteristics to be fed back according to the number II, inputting the character characteristics into a webpage JavaScript to change a webpage of the SSVEP induction interface, and enabling the SSVEP induction interface to amplify, adjust and flicker the selected character so as to finish character selection;
step 4, inputting the selected character into a display interface to finish inputting; the SSVEP-induced interface is refreshed.
Preferably: the method for acquiring the first SSVEP characteristic frequency signal and the second SSVEP characteristic frequency signal comprises the following steps: converting the electroencephalogram signal I and the electroencephalogram signal II from time-frequency discrete signals into frequency-domain continuous signals through a fast Fourier transform method, and calculating the power spectral density of the signals as characteristics to further obtain an SSVEP characteristic frequency signal I and an SSVEP characteristic frequency signal II.
Compared with the prior art, the invention has the following beneficial effects:
the invention selects specific keys by using frequency, determines a unique key value input by selecting frequency twice, is convenient and quick, has low learning cost and can be quickly applied.
Drawings
FIG. 1 is an overall block diagram of electroencephalogram acquisition and analysis
FIG. 2 is a block diagram of the functional flow of EEG signal processing
FIG. 3 is a schematic diagram of a virtual keyboard according to the present invention
FIG. 4 is a diagram showing a virtual keyboard according to the present invention after a virtual key is selected for the first time
FIG. 5 is a front-end display interface after selecting specific letters by the virtual keyboard according to the present invention
FIG. 6 is an overall block diagram of the present invention
Detailed Description
The present invention is further illustrated by the following description in conjunction with the accompanying drawings and the specific embodiments, it is to be understood that these examples are given solely for the purpose of illustration and are not intended as a definition of the limits of the invention, since various equivalent modifications will occur to those skilled in the art upon reading the present invention and fall within the limits of the appended claims.
An SSVEP-based English keyboard spelling system is characterized in that a user controls English input through a user interface to induce SSVEP electroencephalogram components of the user, electroencephalogram signals pass through electroencephalogram acquisition equipment and an electroencephalogram signal analysis module to convert identified frequency into specific numerical values, the specific numerical values are transmitted to the rear end to be processed, and the specific numerical values are fed back to the user interface and converted into specific letters to be displayed in a display device, as shown in figure 6, the system comprises a user display module (a human-computer interaction interface), an electroencephalogram acquisition unit and a processing module;
the user display module comprises a display interface and an SSVEP evoked interface;
the display interface is used for displaying the inputted letters;
the SSVEP inducing interface comprises 9 virtual key frames, wherein the 9 virtual key frames comprise 1 symbol key frame and 8 letter key frames, and 26 letters are distributed in the 8 letter key frames; the 9 virtual key frames flash at different frequencies; flashing each letter in the virtual key frame at different frequencies; the SSVEP inducing interface is developed based on a Pycharm platform under the MacOS operating system environment, a timer is adopted to realize flicker stimulation, 9 virtual key frames are endowed with different flicker frequencies of 4-30 Hz, and different characters in the virtual key frames are endowed with different flicker frequencies;
typing is achieved as the keyboard is controlled in fig. 3:
1. displaying an interface:
and a character display area for displaying the inputted letters.
2. Visually-evoked input interface:
the visual induction interface has 9 virtual keys which can stimulate the human brain to generate SSVEP, and the functions of the virtual keys are as follows:
characters ABC/DEF/GHI/JKL/MNO/PQRS/TUV/WXYZ/and symbol keys correspond to respective frequencies.
The electroencephalogram acquisition unit is worn on the head of a user; the electroencephalogram acquisition unit acquires original electroencephalogram signals by using Open-BCI equipment, enhances the signals, and digitizes the signals with a certain sampling rate after filtering; when a user watches a virtual key frame needing to be selected in an SSVEP (secure visual evoked potential) evoked interface, acquiring an electroencephalogram signal I evoked by the user at the moment; when a user watches letters needing to be selected in a virtual key frame which is amplified and adjusted in an SSVEP (simple visual evoked potential) evoked interface, acquiring an electroencephalogram signal II which is evoked by the user at the moment;
the processing module is used for extracting a characteristic frequency signal according to the generated electroencephalogram signal I to obtain a characteristic frequency signal I, determining a virtual key frame to be selected according to the characteristic frequency signal I, and controlling the SSVEP inducing interface to amplify and adjust the selected virtual key frame; the character input method is used for extracting a characteristic frequency signal according to the generated electroencephalogram signal II to obtain a characteristic frequency signal II, determining letters in a virtual key frame to be selected according to the characteristic frequency signal II, and displaying the letters to be selected through a display interface to finish character input.
2. The implementation method comprises the following steps:
(1) after the task is started, a user wears an electroencephalogram electrode cap just opposite to a visual evoked input interface, virtual keys on the interface flicker at different frequencies, and the human brain is stimulated to generate SSVEP potential;
(2) the user watches the flicker frequency, and the electroencephalogram electrode cap starts to acquire SSVEP;
(3) after the signals are preprocessed, the processed signals are transmitted to a digital signal processor through serial port communication;
(4) the feature extraction algorithm and the classification algorithm in the digital signal processor realize the feature extraction of the SSVEP, and the control result is communicated to the virtual serial port of the PC end through the serial port in real time to realize the control of a human-computer interaction interface, thereby realizing the functions of character display and the like.
An SSVEP-based english keyboard spelling method, as shown in fig. 1, includes the following steps:
step 1, a user wears an electroencephalogram acquisition unit and directly faces an SSVEP (steady state visual evoked potential) evoked interface, and 9 virtual key frames flicker at different frequencies; as shown in FIG. 3, each virtual key box flashes at a respective frequency, thereby evoking SSVEP, at which point the acquisition task begins.
Step 2, selecting a virtual key frame:
step 21, enabling human eyes of a user to watch a virtual key frame to be selected, stimulating human brain to generate a first SSVEP signal, and enabling the user to simultaneously perform left-hand and right-hand motor imagery to generate a first motor imagery electroencephalogram signal;
step 22, acquiring a SSVEP signal I and a motor imagery electroencephalogram signal I by an electroencephalogram acquisition unit to serve as the electroencephalogram signal I;
step 23, filtering the first electroencephalogram signal to remove ocular artifacts; the acquired electroencephalogram signals are preprocessed, so that the interference of the electro-oculogram and the myoelectricity is reduced, and the classification recognition rate is improved. The preprocessing mainly comprises intercepting effective data of the SSVEP, reducing sampling frequency and removing baseline data; extracting and classifying SSVEP characteristic frequency signals of the first electroencephalogram signal to obtain a first SSVEP characteristic frequency signal;
and performing eemd and csp feature extraction on the preprocessed SSVEP, extracting transient spectrum features, and classifying to determine frequency. The inherent mode IMF function after the decomposition of the three channels EEMD is utilized to carry out CSP filtering, and the frequency domain information of the EEMD is added on the basis of the CSP, so that the problem that the CSP lacks the frequency domain information is well solved. When the signal is added to a uniformly distributed white noise background, the signal regions of different scales will automatically map to the appropriate scale associated with the background white noise. The IMF after EEMD decomposition is taken as an input signal, so that EEMD decomposition is realized, three channels are used, a better feature classification result is obtained, and the problem that a large number of input channels are needed in a common CSP algorithm is solved.
And converting the identified frequency into a specific key value input back end so as to determine the selected key value.
As shown in fig. 2, the online electroencephalogram signal processing steps are as follows:
1) receiving an electroencephalogram signal;
2) converting the EEG signal from a time-frequency discrete signal into a frequency-domain continuous signal through a fast Fourier transform algorithm embedded in a digital signal processor, and calculating the power spectral density of the signal as a characteristic;
3) forming feature vectors from the feature results, and performing pattern recognition on the electroencephalogram features;
4) through programming, matching the mode recognition result with each key value, and outputting a control instruction to a virtual serial port of a PC (personal computer) end through serial port communication, so that the functions of character display and the like of a man-machine interaction interface are realized, and man-machine interaction closed-loop control is completed;
step 24, outputting a specific number one according to the SSVEP characteristic frequency signal one, then writing the specific number one into a newly-built file of the background, obtaining the virtual key frame characteristic to be fed back according to the number one, inputting the virtual key frame characteristic into a page JavaScript to change the page of the SSVEP inducing interface, and enabling the SSVEP inducing interface to amplify, adjust and flicker the selected virtual key frame so as to finish the selection of the virtual key frame;
step 3, character selection:
step 31, watching letters to be selected in the virtual key frame selected in the step 24, flashing the letters in the selected virtual key frame at different frequencies to stimulate the brain of a human to generate a second SSVEP signal, and simultaneously performing motor imagery of the left hand and the right hand by a user to generate a second motor imagery electroencephalogram signal;
step 32, acquiring an SSVEP signal II and a motor imagery electroencephalogram signal II by an electroencephalogram acquisition unit to serve as the electroencephalogram signal II;
step 23, filtering the electroencephalogram signal II to remove ocular artifacts; extracting and classifying SSVEP characteristic frequency signals of the electroencephalogram signal II to obtain an SSVEP characteristic frequency signal II;
step 24, outputting a specific number II according to the SSVEP characteristic frequency signal II, then writing the specific number II into a newly-built file of the background, obtaining character characteristics to be fed back according to the number II, inputting the character characteristics into a webpage JavaScript to change a webpage of the SSVEP induction interface, and enabling the SSVEP induction interface to amplify, adjust and flicker the selected character so as to finish character selection;
step 4, inputting the selected character into a display interface to finish inputting; the SSVEP-induced interface is refreshed.
The implementation method comprises the following steps:
1. after the task is started, the user is over against the visual induction interface, the interface flickers at different frequencies, and the human brain is stimulated to generate
The SSVEP potential.
2. And the electroencephalogram acquisition equipment acquires the SSVEP signal.
3. The signals are preprocessed, the processed signals are converted into specific numbers, and the specific numbers are input to the rear end, so that letters watched by a user are obtained.
The description of the input word AE is as follows:
1. after the task starts, the user watches the virtual keys to flash at different frequencies.
2. If the user needs to input the word AE, the user needs to look at the ABC key box, wait for the ABC key box to become larger,
as shown in fig. 4, three letters ABC flash at different frequencies, and the user looks at letter a and waits for the letter a to appear in the input box, so that the input of the first letter is completed, as shown in fig. 5, the input of the letter a is completed.
3. After the input A is input, the page is refreshed, a user watches a DEF key box, after the DEF key box is changed to be large, three DEF letters flicker at different frequencies, the user watches E, and when E appears in the input box, the input of the second letter is finished
4. This time of input is finished
Compared with a determinant keyboard spelling system, the method provides a more perfect function of adding, deleting and modifying, can correct and perfect in time, and meanwhile, the system is friendly to the addition of new characters in the later period, flexible and easy to use, low in learning cost, emphatic in experience, and solves the problems of single rigidity and low flexibility of the existing spelling system. SSVEP is the periodic response of the brain to external visual stimuli modulated at a frequency (greater than 6Hz) in which the EEG of the cerebral visual cortex exhibits distinct peak characteristics at the stimulation frequency and its harmonics. The device comprises a key flashing according to specific frequency, an electroencephalogram acquisition module and an electroencephalogram signal analysis module, and the key is used for acquiring an electroencephalogram signal of a user and identifying SSVEP potential so as to judge the input intention of the user. A direct interaction way between the human brain and the mobile intelligent device is provided. The method comprises the following steps: collecting electroencephalogram signals, processing the electroencephalogram signals on line, and controlling a virtual keyboard to realize English input. The virtual keyboard interface is divided into: and displaying an interface, wherein the letter display area is used for displaying the input letters. Visually-evoked input interface: the 9 flashing keys in the visual evoked input interface can stimulate the human brain to generate SSVEP potential, and simultaneously, the selection of the virtual keys is controlled by watching the virtual keys to stimulate the cerebral cortex to generate SSVEP signals.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.
Claims (4)
1. An English keyboard spelling system based on SSVEP is characterized in that: the electroencephalogram acquisition system comprises a user display module, an electroencephalogram acquisition unit and a processing module;
the user display module comprises a display interface and an SSVEP evoked interface;
the display interface is used for displaying the inputted letters;
the SSVEP inducing interface comprises 9 virtual key frames, wherein the 9 virtual key frames comprise 1 symbol key frame and 8 letter key frames, and 26 letters are distributed in the 8 letter key frames; the 9 virtual key frames flash at different frequencies; flashing each letter in the virtual key frame at different frequencies;
the electroencephalogram acquisition unit is worn on the head of a user; when a user watches a virtual key frame needing to be selected in an SSVEP (secure visual evoked potential) evoked interface, acquiring an electroencephalogram signal I evoked by the user at the moment; when a user watches letters needing to be selected in a virtual key frame which is amplified and adjusted in an SSVEP (simple visual evoked potential) evoked interface, acquiring an electroencephalogram signal II which is evoked by the user at the moment;
the processing module is used for extracting a characteristic frequency signal according to the generated electroencephalogram signal I to obtain a characteristic frequency signal I, determining a virtual key frame to be selected according to the characteristic frequency signal I, and controlling the SSVEP inducing interface to amplify and adjust the selected virtual key frame; the character input method is used for extracting a characteristic frequency signal according to the generated electroencephalogram signal II to obtain a characteristic frequency signal II, determining letters in a virtual key frame to be selected according to the characteristic frequency signal II, and displaying the letters to be selected through a display interface to finish character input.
2. The SSVEP-based english keyboard spelling system of claim 1, wherein: the electroencephalogram acquisition unit is an electroencephalogram electrode cap.
3. A spelling method based on the SSVEP-based english keyboard spelling system of claim 1, comprising the steps of:
step 1, a user wears an electroencephalogram acquisition unit and directly faces an SSVEP (steady state visual evoked potential) evoked interface, and 9 virtual key frames flicker at different frequencies;
step 2, selecting a virtual key frame:
step 21, enabling human eyes of a user to watch a virtual key frame to be selected, stimulating human brain to generate a first SSVEP signal, and enabling the user to simultaneously perform left-hand and right-hand motor imagery to generate a first motor imagery electroencephalogram signal;
step 22, acquiring a SSVEP signal I and a motor imagery electroencephalogram signal I by an electroencephalogram acquisition unit to serve as the electroencephalogram signal I;
step 23, filtering the first electroencephalogram signal to remove ocular artifacts; extracting and classifying SSVEP characteristic frequency signals of the first electroencephalogram signal to obtain a first SSVEP characteristic frequency signal;
step 24, outputting a specific number one according to the SSVEP characteristic frequency signal one, then writing the specific number one into a newly-built file of the background, obtaining the virtual key frame characteristic to be fed back according to the number one, inputting the virtual key frame characteristic into a page JavaScript to change the page of the SSVEP inducing interface, and enabling the SSVEP inducing interface to amplify, adjust and flicker the selected virtual key frame so as to finish the selection of the virtual key frame;
step 3, character selection:
step 31, watching letters to be selected in the virtual key frame selected in the step 24, flashing the letters in the selected virtual key frame at different frequencies to stimulate the brain of a human to generate a second SSVEP signal, and simultaneously performing motor imagery of the left hand and the right hand by a user to generate a second motor imagery electroencephalogram signal;
step 32, acquiring an SSVEP signal II and a motor imagery electroencephalogram signal II by an electroencephalogram acquisition unit to serve as the electroencephalogram signal II;
step 23, filtering the electroencephalogram signal II to remove ocular artifacts; extracting and classifying SSVEP characteristic frequency signals of the electroencephalogram signal II to obtain an SSVEP characteristic frequency signal II;
step 24, outputting a specific number II according to the SSVEP characteristic frequency signal II, then writing the specific number II into a newly-built file of the background, obtaining character characteristics to be fed back according to the number II, inputting the character characteristics into a webpage JavaScript to change a webpage of the SSVEP induction interface, and enabling the SSVEP induction interface to amplify, adjust and flicker the selected character so as to finish character selection;
step 4, inputting the selected character into a display interface to finish inputting; the SSVEP-induced interface is refreshed.
4. The SSVEP-based english keyboard spelling input method of claim 3, wherein: the method for acquiring the first SSVEP characteristic frequency signal and the second SSVEP characteristic frequency signal comprises the following steps: converting the electroencephalogram signal I and the electroencephalogram signal II from time-frequency discrete signals into frequency-domain continuous signals through a fast Fourier transform method, and calculating the power spectral density of the signals as characteristics to further obtain an SSVEP characteristic frequency signal I and an SSVEP characteristic frequency signal II.
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| CN112764532A (en) * | 2020-12-31 | 2021-05-07 | 北京信息科技大学 | Keyboard and mouse control system and control method based on brain electricity, eye electricity and electricity combination |
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