High-frequency cold-start visual evoked potential brain-computer interface system, device, equipment and medium

Abstract

The present invention relates to the field of brain-computer interface technology, and discloses a high-frequency cold-start visual evoked potential brain-computer interface system, device, equipment and medium. The system includes an instruction sequence generation module, a local dynamic flashing module, a user display interface and a decoding module, wherein the instruction sequence generation module determines the integer instruction sequence length that makes the flashing frequency within a first preset range according to the instruction target number and the device refresh rate; and determines the target instruction sequence according to the integer instruction sequence length; the local dynamic flashing module sorts the target instruction sequence by local dynamic flashing icons and determines the local dynamic flashing icon display sequence; the user display interface displays the local dynamic flashing icon display sequence to the target user; the decoding module receives the target EEG signal generated by the target user after watching the local dynamic flashing icon display sequence; the decoding module decodes the target EEG signal to obtain the target intention of the target user, thereby improving the practicality of the system.

Description

Technical Field

[0001] The present invention relates to the field of brain-computer interface technology, and in particular to a high-frequency cold-start visual evoked potential brain-computer interface system, device, equipment and medium. Background Art

[0002] Brain-computer interface technology provides users with a way to bypass the neuromuscular pathway and directly utilize the neural pathway to interact with the outside world. With the development of society and brain-computer interface technology, high performance is no longer the only dimension for evaluating brain-computer interfaces. Requirements such as the diversity of application scenarios, user comfort, and convenience have gradually become prominent. The existing stimulation paradigm has low-frequency stimulation that can easily cause user fatigue and even induce epilepsy; the stimulation pattern cannot effectively avoid surrounding interference, resulting in a decrease in decoding accuracy; the phase-encoding-based stimulation paradigm decoding relies on precise triggering information and cannot achieve a cold start of the decoding algorithm. These problems limit the practical application of visual evoked potential brain-computer interface systems, especially in the fields of cognitive training, neurorehabilitation, etc. Therefore, how to improve the performance of visual evoked potential brain-computer interface systems in practical applications has become an urgent problem to be solved. Summary of the Invention

[0003] The present invention provides a high-frequency cold-start visual evoked potential brain-computer interface system, device, equipment and medium, which can solve the technical problem of how to improve the practicality of the visual evoked potential brain-computer interface system.

[0004] In the first aspect, a high-frequency cold-start visual evoked potential brain-computer interface system is provided. The high-frequency cold-start visual evoked potential brain-computer interface system includes an instruction sequence generation module, a local dynamic flashing module, a user display interface and a decoding module, wherein:

[0005] The instruction sequence generation module determines the integer instruction sequence length according to the target number of instructions and the device refresh rate, so that the flickering frequency under the integer instruction sequence length is within a first preset range; wherein the flickering frequency is the quotient of the device refresh rate and the integer instruction sequence length;

[0006] The instruction sequence generation module determines the target instruction sequence according to the integer instruction sequence length;

[0007] The local dynamic flashing module sorts the local dynamic flashing icons of the target instruction sequence and determines the local dynamic flashing icon display sequence;

[0008] The user display interface displays a local dynamic flashing icon display sequence to the target user;

[0009] The decoding module receives the target EEG signal generated by the target user after watching the local dynamic flashing icon display sequence;

[0010] The decoding module decodes the target EEG signal to obtain the target intention of the target user.

[0011] In a second aspect, a high-frequency cold-start visual evoked potential brain-computer interface device is provided, comprising:

[0012] A first determining module is configured to determine the length of the integer instruction sequence based on the target number of instructions and the device refresh rate, so that a flickering frequency under the integer instruction sequence length is within a first preset range; wherein the flickering frequency is the quotient of the device refresh rate and the integer instruction sequence length;

[0013] A second determining module is used to determine a target instruction sequence according to the length of the integer instruction sequence;

[0014] A third determining module is used to sort the local dynamic flashing icons of the target instruction sequence and determine the local dynamic flashing icon display sequence;

[0015] A display module, used to display a local dynamic flashing icon display sequence to the target user;

[0016] A receiving module, configured to receive a target EEG signal generated by a target user after viewing a sequence of local dynamic flashing icons;

[0017] The decoding module is used to decode the target EEG signal to obtain the target intention of the target user.

[0018] In a third aspect, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the processing steps of a brain-computer interface system based on high-frequency cold-start visual evoked potential are implemented.

[0019] In a fourth aspect, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the processing steps of a brain-computer interface system based on high-frequency cold-start visual evoked potential are implemented.

[0020] In the scheme implemented by the above-mentioned high-frequency cold-start visual evoked potential brain-computer interface system, device, equipment and medium, the instruction sequence generation module in the high-frequency cold-start visual evoked potential brain-computer interface system can determine the integer instruction sequence length that makes the flashing frequency within the first preset range based on the instruction target number and the device refresh rate, and determine the target instruction sequence based on the integer instruction sequence length. The local dynamic flashing module sorts the target instruction sequence with local dynamic flashing icons and determines the local dynamic flashing icon display sequence. The user display interface displays the local dynamic flashing icon display sequence to the target user. The decoding module receives the target EEG signal generated after the target user watches the local dynamic flashing icon display sequence, and decodes the target EEG signal to obtain the target user's target intention. While improving the effectiveness of intention recognition, it also improves the comfort of the target user during the use process and improves the practicality of the visual evoked potential brain-computer interface system. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0022] Figure 1 This is a schematic diagram of an application environment of a high-frequency cold-start visual evoked potential brain-computer interface system according to an embodiment of the present invention;

[0023] Figure 2 This is a schematic diagram of a processing flow of a high-frequency cold-start visual evoked potential brain-computer interface system according to one embodiment of the present invention;

[0024] Figure 3 is a schematic diagram of a user display interface in one embodiment of the present invention;

[0025] Figure 4 2 is a schematic structural diagram of a high-frequency cold-start visual evoked potential brain-computer interface device according to an embodiment of the present invention;

[0026] Figure 5 is a structural diagram of a computer device in one embodiment of the present invention;

[0027] Figure 6 FIG. 2 is another structural diagram of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0029] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0030] References to "embodiments" in this application mean that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described in this application may be combined with other embodiments.

[0031] The high-frequency cold-start visual evoked potential brain-computer interface system provided by the embodiment of the present invention can be applied in Figure 1 In the application environment. For example, taking the scenario in which the target user generates a target EEG signal after watching a visual stimulation sequence and identifies the target intention of the target EEG signal as an example, the target user can watch the visual stimulation sequence generated and selected by the high-frequency cold-start visual evoked potential brain-computer interface system through the user display interface (such as the target instruction sequence to be mentioned below), and collect the target EEG signal generated by the target user after watching through the high-frequency cold-start visual evoked potential brain-computer interface system, thereby decoding the target EEG signal through the decoding module to obtain the target intention of the target user. The high-frequency cold-start visual evoked potential brain-computer interface system provided by this application can improve the effectiveness of intention recognition while improving the comfort of the target user's use process, which is conducive to improving the practicality of the visual evoked potential brain-computer interface system. Among them, the target user can be a user of the high-frequency cold-start visual evoked potential brain-computer interface system, that is, a user who needs to perform intention recognition on his target EEG signal, and this application does not impose any restrictions on this.

[0032] The high-frequency cold-start visual evoked potential brain-computer interface system includes an instruction sequence generation module, a local dynamic flicker module, a user display interface and a decoding module. Among them, the instruction sequence generation module can be used to generate a visual stimulus sequence (i.e., an instruction sequence) presented to the user. Since different instruction sequences can trigger EEG signals of different characteristics, the present application can generate and select an instruction sequence that meets the requirements by the instruction sequence generation module, such as a high-frequency periodic instruction sequence can be generated, so that the required EEG signals can be more effectively stimulated to improve the accuracy and stability of the high-frequency cold-start visual evoked potential brain-computer interface system. The local dynamic flicker module can be used to guide the user's attention to focus on specific visual stimuli. The local dynamic flicker module can help the user accurately focus on key stimulus elements in a complex visual environment, thereby reducing the influence of surrounding interference information and improving the interaction effect of the brain-computer interface. The user display interface can be a direct window for the user to interact with the brain-computer interface system. The user display interface can show the user the visual stimuli selected by the instruction sequence generation module and designed by the local dynamic flicker module. The decoding module can analyze and decode the collected EEG signals through a decoding algorithm to extract information related to the user's intention, thereby accurately determining the user's target intention.

[0033] See also Figure 2 As shown, Figure 2 A schematic diagram of a processing flow of a high-frequency cold-start visual evoked potential brain-computer interface system provided in an embodiment of the present invention includes the following steps:

[0034] S10: The instruction sequence generation module determines the integer instruction sequence length according to the target number of instructions and the device refresh rate, so that the flickering frequency under the integer instruction sequence length is within a first preset range; wherein the flickering frequency is the quotient of the device refresh rate and the integer instruction sequence length.

[0035] The instruction target number may be used to indicate the number of display icons corresponding to the target instruction sequence. For example, if the target instruction sequence corresponds to 12 display icons, the instruction target number may be 12. Optionally, the instruction target number may be pre-set by the user or determined by the system by default, and this application does not impose any restrictions on this.

[0036] The device refresh rate can be used to indicate the refresh rate of the display device being used at the time. It is understood that a device with a high refresh rate can more clearly display subtle visual changes and may be more advantageous for presenting high-frequency stimuli. For example, when the device refresh rate is very high, the high refresh rate device can accurately display rapidly flickering high-frequency visual stimuli. In this case, the instruction sequence generation module can select a relatively high flicker frequency value, and this application does not impose any restrictions on this.

[0037] The integer instruction sequence length can be used to indicate the length of the integer value to which the initial instruction sequence can correspond. The initial instruction sequence can be understood as a predetermined or system-default instruction sequence. For example, the instruction sequence generation module can determine that the integer instruction sequence length of the initial instruction sequence is 4 based on the target number of instructions and the device refresh rate, and this application does not impose a limitation on this. Optionally, the initial instruction sequence can be one or more, and the integer instruction sequence length corresponding to each initial instruction sequence can be the same or different, and this application does not impose a limitation on this.

[0038] The initial instruction sequence can be an instruction sequence under the traditional Steady-State Visual Evoked Potential (SSVEP) stimulation paradigm. It is understandable that although the SSVEP stimulation paradigm has been widely used in brain-computer interface research and some visual neuroscience research, it also has some limitations, such as visual fatigue and limitation on the stimulation frequency range. Therefore, this application makes corresponding adjustments to the initial instruction sequence to obtain a target instruction sequence that is more in line with research needs, thereby achieving a brain-computer interaction process that is more comfortable, more convenient to use, and more accurate.

[0039] In the relevant field of high-frequency cold-start visual evoked potential brain-computer interface involved in this application, the flicker frequency mainly refers to a specific frequency that can effectively induce visual evoked potential (VEP) and has certain characteristics that can be used to distinguish different states or convey specific information. In other words, the instruction sequence generation module can determine a flicker frequency that has certain characteristics, can convey characteristic information, can effectively induce visual evoked potential, and meets the current device refresh rate based on relevant factors such as the number of instruction targets, the length of the integer instruction sequence, and the device refresh rate, so that in subsequent steps, a more accurate and appropriate target instruction sequence can be determined based on the flicker frequency.

[0040] Optionally, the instruction sequence generation module determines the flashing frequency according to the integer instruction sequence length and the device refresh rate, as shown in the following formula:

[0041]

[0042] Among them, f N Can be used to indicate flicker frequency, such as the basic flicker frequency, i.e. fundamental frequency; f r It can be used to indicate the device refresh rate; N1 can be used to indicate the length of an integer instruction sequence.

[0043] According to the flicker frequency, SSVEP can be divided into a low frequency band (8-15Hz), a medium frequency band (15-30Hz), and a high frequency band (≥30Hz). Therefore, in order to efficiently induce more appropriate EEG signals and accurately detect and interpret the EEG signals, the present application can control the flicker frequency under the integer instruction sequence length within a first preset range. Optionally, the first preset range can be 4Hz-30Hz, which is not limited by the present application.

[0044] S20: The instruction sequence generation module determines the target instruction sequence according to the integer instruction sequence length.

[0045] This application proposes a new stimulation paradigm in which a pseudo-random binary sequence (PRBS) is superimposed within each cycle of the basic flicker frequency of the SSVEP stimulation paradigm, so that the resulting visual stimulation still has the periodic characteristics of the basic flicker frequency and has specific frequency characteristics in the frequency domain, which can facilitate subsequent decoding work. At the same time, the superimposed PRBS sequence in each cycle visually appears as a high-frequency flicker signal, thereby improving the comfort and safety of the system.

[0046] The essence of superimposing PRBS on SSVEP is to make the PRBS sequence appear periodically, so that what is observed within one cycle is the broadband characteristic of the PRBS sequence, while it still exhibits periodic characteristics over a long period of time. Finally, the frequency corresponding to the cycle and its multiples can be obtained from frequency domain analysis, and this application does not impose any restrictions on this.

[0047] Therefore, after the instruction sequence generation module determines the integer instruction sequence length, the PRBS sequence to be superimposed can be optimized and designed according to the integer instruction sequence length to obtain the target instruction sequence.

[0048] This application selects alternative PRBS sequences to ensure that each selected PRBS sequence has the best EEG response performance at that length. The stimulation paradigm of this study needs to maximize the amplitude at each frequency multiple, or maximize the signal-to-noise ratio of the EEG evaluation signal generated after the maximum EEG response evaluation, and use this as the characteristic value to traverse all possible PRBS sequences of the specified length, and finally obtain the optimal PRBS sequence, which is not limited by this application.

[0049] On the basis of the traditional steady-state visual evoked potential stimulation paradigm, a new stimulation paradigm that superimposes a PRBS sequence in each stimulation cycle can improve user comfort by introducing high-frequency components in each cycle. Moreover, since this stimulation paradigm still retains a periodicity similar to that of the steady-state visual evoked potential stimulation paradigm, its final induced potential is similar to the steady-state visual evoked potential and has obvious frequency characteristics, which is conducive to selecting a suitable template in the decoding process and directly using an unsupervised algorithm to perform decoding and analysis. This application does not impose any restrictions on this.

[0050] S30: The local dynamic flashing module sorts the local dynamic flashing icons on the target instruction sequence and determines a local dynamic flashing icon display sequence.

[0051] Among them, the local dynamic flashing icon display sequence can be used to indicate that the icons corresponding to the target instruction sequence are localized and the instruction icon content is gradually displayed, thereby obtaining an icon display sequence. It is understandable that after using the VEP brain-computer interface system for a long time, users' vigilance (or attention) decreases due to fatigue. One of the important reasons is that the current visual stimulation image display format is too simple. The overall display of the icon content causes users to not pay attention to the flashing content, but only to the flashing itself.

[0052] This application improves the new stimulation paradigm by adding a local dynamic flicker module (also referred to as a local dynamic micro-flicker module) to guide the user's attention when viewing the instruction sequence, so that the user focuses on the semantic information of the flickering content, thereby maintaining the user's vigilance at a high level, thereby effectively improving the final performance of the system. This application does not impose any restrictions on this. Optionally, the operation of the local dynamic flicker module performing local dynamic flicker icon sorting on the target instruction sequence can also be referred to as the operation of the local dynamic flicker module performing local dynamic micro-flicker processing on the target instruction sequence. This application does not impose any restrictions on this.

[0053] Optionally, the local dynamic flashing module generates a local dynamic flashing icon sequence for the target instruction sequence, and generates a local dynamic flashing icon display sequence corresponding to the target instruction sequence, which may be performed in the following steps:

[0054] 1. Segment each initial icon corresponding to the target instruction sequence to obtain a local dynamic flashing icon subset sequence corresponding to each initial icon.

[0055] Among them, the target instruction sequence may include one or more initial icons, for example, the target instruction sequence includes initial icon 1, initial icon 2, initial icon 3 and initial icon 4. Each initial icon can be divided into one or more local dynamic flashing icons, and each local dynamic flashing icon is a part of its corresponding initial icon. For example, initial icon 1 can be divided into 4 local dynamic flashing icons, namely local dynamic flashing icon 1-1, local dynamic flashing icon 1-2, local dynamic flashing icon 1-3 and local dynamic flashing icon 1-4; initial icon 2 can be divided into 4 local dynamic flashing icons, namely local dynamic flashing icon 2-1, local dynamic flashing icon 2-2, local dynamic flashing icon 2-3 and local dynamic flashing icon 2-4, etc., and this application does not impose any restrictions on this.

[0056] 2. Establish an associated index between each local dynamic flashing icon in the local dynamic flashing icon subset and the corresponding initial icon to obtain an associated index subset.

[0057] For example, based on the example in step 1, the server can establish associated indexes for local dynamic flashing icon 1-1, local dynamic flashing icon 1-2, local dynamic flashing icon 1-3, and local dynamic flashing icon 1-4 with initial icon 1. For example, after establishing an associated index for local dynamic flashing icon 1, local dynamic flashing icon 1-1 is obtained, and after establishing an associated index for local dynamic flashing icon 2, local dynamic flashing icon 1-2 is obtained. This application does not impose any restrictions on this. Local dynamic flashing icon 2-1, local dynamic flashing icon 2-2, local dynamic flashing icon 2-3, and local dynamic flashing icon 2-4 can be associated with initial icon 2, etc. This application does not impose any restrictions on this.

[0058] 3. Randomly shuffle each associated index in the associated index subset corresponding to each of the initial icons to obtain a random associated index subset corresponding to each of the initial icons.

[0059] Before displaying the instruction sequence to the target user, the server can use a random scrambling algorithm to randomly scramble the associated indexes in each associated index subset to obtain a random associated index subset. Optionally, the server can also use other methods to randomly scramble the associated indexes, which is not limited by this application.

[0060] 4. Determine a local dynamic flashing icon display sequence based on the random associated index subset corresponding to each of the initial icons.

[0061] For example, taking the random associated index subset of the initial icon 1 as local dynamic flashing icon 1-3, local dynamic flashing icon 1-2, local dynamic flashing icon 1-4 and local dynamic flashing icon 1-1, and the random associated index subset of the initial icon 2 as local dynamic flashing icon 2-4, local dynamic flashing icon 2-2, local dynamic flashing icon 2-1 and local dynamic flashing icon 2-3 as an example, the server can determine the corresponding local dynamic flashing icon display sequence, and display the local dynamic flashing icons to the target user in sequence according to the arrangement order of the random associated indexes in each random associated index subset. This application does not impose any restrictions on this.

[0062] It is understandable that after the local dynamic flashing icons of the local dynamic flashing module are sorted, the target user can look at the position corresponding to the initial icon that needs to be focused on according to the guidance (that is, the randomly lit local dynamic flashing icon). When a certain initial icon needs to be displayed, the corresponding local dynamic flashing icons will be displayed in sequence according to the order of the random association index. For example, the initial icon 1 includes 4 local dynamic flashing icons, and the random association indexes of the 4 local dynamic flashing icons are 1-3, 1-4, 1-2, and 1-1. When the initial icon 1 is displayed to the target user, it will be displayed in the order of local dynamic flashing icon 3, focus group 4, local dynamic flashing icon 2 and local dynamic flashing icon 1. This application does not impose any restrictions on this.

[0063] Optionally, if all local dynamic flashing icons have appeared once, the local dynamic flashing module can use the random shuffling algorithm to randomly shuffle the associated indexes again to obtain re-shuffled random associated indexes, thereby further achieving the random appearance of the user's focus guide position. Optionally, if all possible index sequences corresponding to the local dynamic flashing icons have appeared once, the initial icons can be re-segmented and the associated indexes re-established to maximize the diversity of the focus guide positions.

[0064] In order to address the defect that there is no focus guidance in the traditional icon display process, which leads to the user's unstable gaze position, the present application uses a local dynamic flashing module to sort the target instruction sequence with local dynamic flashing icons, which can effectively focus the target user's focus on the required position. During the stimulation process, it can improve the target user's vigilance, which is conducive to enhancing the performance of the high-frequency cold-start visual evoked potential brain-computer interface system.

[0065] S40: The user display interface displays a local dynamic flashing icon display sequence to the target user.

[0066] After the local dynamic flashing module determines the local dynamic flashing icon display sequence, the user display interface can obtain the local dynamic flashing icon display sequence and flash the local dynamic flashing icon display sequence in sequence according to the display order indicated in the local dynamic flashing icon display sequence.

[0067] For example, Figure 3 As shown, Figure 3 An example of a user display interface arranged in a 4*3 manner is shown. After considering the equivalent neighbor principle, Figure 3 The arrangement in can increase the distance between each stimulation target (the initial icon of the target instruction sequence) as much as possible. Figure 3 Taking the target instruction sequence including 12 initial icons as an example, the 12 icons can be arranged from large to small according to the selected frequency, and the viewing angle size of each icon can be a square of 6°×6° pixels. Under the premise of being 50 cm away from the screen, the viewing angle between each row can be 4.53°, and the viewing angle between each column can be 8°.

[0068] Research has shown that the higher the contrast between the icon and the background in the user interface, the higher the visual evoked potential (VEP) generated. Therefore, to increase the electroencephalogram (EEG) amplitude generated by stimulation, the initial icon and background can be in high-contrast colors such as black and white, and the binary sequence 0 and 1 can be used to represent off or on, with 0 representing completely off and 1 representing completely on. This application does not impose any restrictions on this.

[0069] S50: The decoding module receives a target EEG signal generated by the target user after watching the local dynamic flashing icon display sequence.

[0070] The target EEG signal can be collected by the acquisition system. The acquisition system can transmit the collected target EEG signal to the decoding module, and correspondingly, the decoding module can receive the target EEG signal from the acquisition system. Optionally, the acquisition system involved in this application can be composed of a Boricon EEG acquisition device and a personal computer with an LCD display. Among them, the personal computer can be used to present the stimulation paradigm, as well as to collect and analyze data. Its screen refresh rate is 120Hz and the original resolution is 1920×1080, which is not limited in this application.

[0071] The stimulation paradigm of this application can be developed in the Matlab environment, and the monitor and the EEG acquisition program can be synchronized through a synchronizer. This application adopted a 32-bit electrode layout during the research process. There are 5 usable electrode channels in the occipital lobe area, namely "PO3", "PO4", "Oz", "O1", and "O2". The sampling rate of the EEG amplifier is 1000Hz. The stimulation paradigm will send a trigger signal to the EEG acquisition program at the beginning of each round of stimulation, and will be stored or sent together with the EEG data as the sixth channel data for locating the stimulation starting point in the subsequent decoding algorithm process, thereby achieving more accurate decoding and classification.

[0072] S60: The decoding module decodes the target EEG signal to obtain the target intention of the target user.

[0073] The target intention can be used to indicate the intention of the target user determined after the decoding module decodes the target EEG signal. Due to the particularity of the new stimulation paradigm in the selection of stimulation frequency and stimulation form, it is impossible to directly use existing decoding algorithms for decoding, such as traditional filter bank design and traditional templates. Therefore, the present application provides a decoding algorithm that can be modified on the basis of the traditional decoding algorithm to make it more compatible with the frequency characteristics and distribution form of the data obtained by the new stimulation paradigm.

[0074] Optionally, the present application can analyze and design multiple filters with different passbands based on the flickering frequency of the stimulation target to perform sub-band decomposition on the received EEG signal; after the decomposition obtains each sub-band, a correlation value calculation process is performed on each sub-band to obtain its correlation value with each harmonic frequency reference signal; thereby further taking the weighted square of the correlation value of all sub-band components as the correlation eigenvalue of the stimulation target; and then outputting the stimulation target with the largest correlation eigenvalue in all sub-bands as the final output result (i.e., target intention), which is not restricted in the present application.

[0075] This application can redesign the filter group subband division and reference signal weight calculation based on the unsupervised algorithm of filter group canonical correlation analysis, so as to obtain the calibration-free visual evoked potential brain-computer interface system provided by this application, which can not only achieve cold start, but also improve the user comfort during the use process while maintaining the performance of the traditional paradigm.

[0076] It should be noted that, in the case of multiple initial instruction sequences, the instruction sequence generation module can determine a more appropriate target instruction sequence based on each initial instruction sequence, and the local dynamic flashing module can perform a local dynamic flashing icon sequence on each target instruction sequence, thereby displaying the local dynamic flashing icon display sequence corresponding to each target instruction sequence on the user display interface, so that the target user can generate a corresponding target EEG signal, and then decode each target EEG signal through the decoding module to further determine which local dynamic flashing icon corresponds to which target instruction sequence the target user is currently viewing, and then determine the target user's accurate target intention. This application does not impose any restrictions on this.

[0077] In this example, the instruction sequence generation module determines the integer instruction sequence length that makes the flashing frequency within the first preset range based on the instruction target number and the device refresh rate, and determines the target instruction sequence based on the integer instruction sequence length. The local dynamic flashing module sorts the target instruction sequence into local dynamic flashing icons and determines the local dynamic flashing icon display sequence. The user display interface displays the local dynamic flashing icon display sequence to the target user. The decoding module receives the target EEG signal generated after the target user watches the local dynamic flashing icon display sequence, and decodes the target EEG signal to obtain the target intention of the target user. While improving the effectiveness of intention recognition, it also improves the comfort of the target user during the use process, which is conducive to improving the practicality of the visual evoked potential brain-computer interface system.

[0078] In one embodiment, the instruction sequence generation module determines the target instruction sequence according to the integer instruction sequence length, which may include the following steps:

[0079] A1: The instruction sequence generation module determines the set of pseudo-random sequences to be traversed based on the integer instruction sequence length;

[0080] A2: The instruction sequence generation module performs EEG maximum response evaluation on each pseudo-random sequence to be traversed in the set of pseudo-random sequences to be traversed, and obtains an EEG evaluation signal set;

[0081] A3: The instruction sequence generation module calculates the signal-to-noise ratio of each EEG evaluation signal in the EEG evaluation signal set to obtain a signal-to-noise ratio set;

[0082] A4: The instruction sequence generation module obtains the maximum signal-to-noise ratio from the signal-to-noise ratio set;

[0083] A5: The instruction sequence generation module determines the decimal number corresponding to the maximum signal-to-noise ratio as the optimal flashing frequency value;

[0084] A6: The instruction sequence generation module determines the optimal pseudo-random sequence based on the optimal flashing frequency value;

[0085] A7: The instruction sequence generation module performs periodic repetition processing on the optimal pseudo-random sequence to obtain the target instruction sequence.

[0086] The set of pseudo-random sequences to be traversed may include one or more pseudo-random sequences to be traversed, and the pseudo-random sequences to be traversed may be used to indicate pseudo-random sequences to be traversed that have a length equal to the length of an integer instruction sequence. It is understood that the pseudo-random sequences to be traversed may be understood as PRBS sequences to be selected, and this application does not impose any limitation on this.

[0087] For example, taking the integer instruction sequence length as N1, in order to compare all potential pseudo-random sequences of length N1, it is necessary to set the value of the decimal number D to be equal to 1 to All corresponding pseudo-random sequences (excluding binary sequences of all 1s and all 0s) are compared. After determining the decimal number D, the decimal number D can be converted into a binary pseudo-random sequence to obtain the set of pseudo-random sequences to be traversed. Optionally, the process of converting the decimal number D into a binary pseudo-random sequence can be referred to the following formula:

[0088]

[0089] Among them, d n It can be used to represent the value of the nth bit in a binary pseudo-random sequence. The nth number in the binary pseudo-random sequence from right to left is the nth bit of the sequence. For example, if the first number from right to left is 1, then the first bit of the binary pseudo-random sequence is 1, that is, d n =1; D can be used to represent a decimal number that needs to be converted; mod can be used to represent the remainder after a division operation; Dmod2 n Can be used to represent dividing D by 2 n Then take the remainder; It can be used to indicate that the number in the brackets is rounded down.

[0090] For example, taking D as 5, the first digit of the pseudo-random sequence to be traversed is calculated. Dividing 5 by 2 leaves a remainder of 1. Dividing the remainder by 1 yields 1, and rounding 1 down still yields 1, so d1 = 1. Then, d2 = 0, d3 = 1, and d4 = 0 are calculated, yielding the pseudo-random sequence to be traversed as 1010. This is not a limitation of this application.

[0091] The EEG evaluation signal set may include one or more EEG evaluation signals, which may be understood as EEG signals obtained after performing EEG maximum response evaluation on each pseudo-random sequence to be traversed in the pseudo-random sequence set to be traversed.

[0092] Optionally, the server may perform periodic repetition processing on each pseudo-random sequence to be traversed to obtain a complete set of instruction sequences. For example, the instruction sequence generation module may perform periodic repetition filling on the first pseudo-random sequence to be traversed, such as expanding the length of the first pseudo-random sequence to be traversed to 125. Optionally, if the complete repetition is insufficient, the sequences may be periodically repeated in descending order until the length of the first pseudo-random sequence to be traversed reaches the target length (e.g., 125), which is not limited in this application.

[0093] The first pseudo-random sequence to be traversed may be any pseudo-random sequence to be traversed in the set of pseudo-random sequences to be traversed. It is understood that the set of pseudo-random sequences to be traversed may further include other pseudo-random sequences to be traversed, such as a second pseudo-random sequence to be traversed, a third pseudo-random sequence to be traversed, and the like. The instruction sequence generation module may use the above steps to periodically and repeatedly fill in the other pseudo-random sequences to be traversed, thereby obtaining a complete instruction sequence set.

[0094] It is understandable that the EEG evaluation signal can be understood as the EEG signal generated by the target user in response to each complete instruction sequence in the complete instruction sequence set after the target user is presented with the complete instruction sequence. In other words, after the instruction sequence generation model obtains the complete instruction sequence corresponding to each pseudo-random sequence to be traversed, each complete instruction sequence can be presented to the target user, thereby collecting the EEG evaluation signal generated by the target user after viewing each complete instruction sequence. Further analysis is then performed based on the EEG evaluation signal to select the pseudo-random sequence with the best EEG evaluation signal characteristics in the frequency domain.

[0095] The signal-to-noise ratio set may include one or more signal-to-noise ratios, which can be understood as the signal-to-noise ratio obtained after performing signal-to-noise ratio calculation on each EEG evaluation signal. The specific calculation process can be found in the detailed description of the embodiments below, and this application will not repeat it here.

[0096] The maximum signal-to-noise ratio can be used to indicate the maximum value in the signal-to-noise ratio set. The optimal flicker frequency value can be used to indicate the flicker frequency value corresponding to the maximum signal-to-noise ratio. The optimal pseudo-random sequence can be used to indicate the pseudo-random sequence corresponding to the maximum signal-to-noise ratio. Optionally, the instruction sequence generation module can further specify the optimal pseudo-random sequence as the instruction code, so that the instruction code can be periodically repeated to obtain the target instruction sequence, which is not limited in this application. For example, with an integer instruction sequence length of l i The instruction sequence generation module can divide the device refresh rate by the integer instruction sequence length (i.e. f r / l i) is processed periodically to achieve the purpose of superimposing high-frequency flicker on the low-frequency periodic signal.

[0097] It can be understood that the maximum signal-to-noise ratio can be the signal-to-noise ratio that is most advantageous and best meets specific requirements under the current given conditions, such as the current device refresh rate, the current flicker frequency and the current integer instruction sequence length. The pseudo-random sequence corresponding to the maximum signal-to-noise ratio (i.e., the optimal pseudo-random sequence) can be the most effective sequence for inducing specific EEG signals (related to the flicker frequency) under the current integer instruction sequence length, and the sequence with the best effect under the current device refresh rate. This application does not impose any restrictions on this.

[0098] The instruction sequence generation module can determine the decimal number corresponding to the maximum signal-to-noise ratio as the optimal flicker frequency value, so as to determine the optimal pseudo-random sequence according to the optimal flicker frequency value, that is, the selected optimal PRBS sequence. Optionally, for initial instruction sequences with different integer instruction sequence lengths, the instruction sequence generation module can respectively determine the maximum signal-to-noise ratio that best matches and is most consistent with the current device refresh rate under each integer instruction sequence length, that is, obtain the optimal pseudo-random sequence corresponding to each integer instruction sequence length, so that each optimal pseudo-random sequence can be further periodically repeated and superimposed according to the total length required by each target instruction sequence to obtain each target instruction sequence.

[0099] Optionally, the instruction sequence generation module may combine the optimal flicker frequency value with a specific visual stimulation pattern generation algorithm to obtain a more appropriate and desired target instruction sequence. For example, the instruction sequence generation module may determine the flicker frequency of the visual stimulation, the icon change period, and so on based on the optimal flicker frequency value, and generate a more desired target instruction sequence based on these parameters. This application does not impose any limitation on this.

[0100] In this example, the instruction sequence generation module determines the set of pseudo-random sequences to be traversed based on the length of the integer instruction sequence, performs EEG maximum response evaluation on each pseudo-random sequence to be traversed in the set of pseudo-random sequences to be traversed, and obtains an EEG evaluation signal set, thereby performing signal-to-noise ratio calculation on each EEG evaluation signal in the set of EEG evaluation signals to obtain a signal-to-noise ratio set, and obtains the maximum signal-to-noise ratio from the signal-to-noise ratio set, and further determines the decimal number corresponding to the maximum signal-to-noise ratio as the optimal flickering frequency value, thereby determining the optimal pseudo-random sequence based on the optimal flickering frequency value, and then performing periodic repetition processing on the optimal pseudo-random sequence to obtain a target instruction sequence that better meets the requirements, which is conducive to more effectively inducing the desired EEG signal and achieving the expected brain-computer interaction effect.

[0101] In one embodiment, taking the example of performing an EEG maximum response evaluation on a first to-be-traversed pseudo-random sequence in a set of to-be-traversed pseudo-random sequences to obtain a first EEG evaluation signal, the instruction sequence generation module performs a signal-to-noise ratio calculation on the first EEG evaluation signal to obtain a signal-to-noise ratio corresponding to the first EEG evaluation signal, which may include the following steps:

[0102] B1: The instruction sequence generation module obtains a first EEG evaluation signal from the EEG evaluation signal set;

[0103] B2: The instruction sequence generation module performs discrete Fourier transform processing on the first EEG evaluation signal to obtain a target spectrum;

[0104] B3: The instruction sequence generation module determines the signal-to-noise ratio corresponding to the first EEG evaluation signal according to the amplitude of the target spectrum, the sampling rate of the EEG device, the flicker frequency and the length of the first EEG evaluation signal.

[0105] The first EEG evaluation signal may be the EEG evaluation signal obtained after performing an EEG maximum response evaluation on the first pseudo-random sequence to be traversed in the set of pseudo-random sequences to be traversed. It is understood that the instruction sequence generation module may perform an EEG maximum response evaluation on other pseudo-random sequences to be traversed, such as the second pseudo-random sequence to be traversed, the third pseudo-random sequence to be traversed, and the like, to obtain other EEG evaluation signals, such as the second EEG evaluation signal, the third EEG evaluation signal, and the like, and this application does not impose any limitation on this.

[0106] The target spectrum is the spectrum obtained by performing a discrete Fourier transform on the first EEG assessment signal. It is understood that the instruction sequence generation module can convert the first EEG assessment signal from the time domain to the frequency domain by performing a discrete Fourier transform on the first EEG assessment signal. In other words, the target spectrum can be the frequency domain representation of the first EEG assessment signal.

[0107] Optionally, the instruction sequence generation module performs discrete Fourier transform processing on the first EEG evaluation signal to obtain a target spectrum, which can be seen in the following formula:

[0108]

[0109] Among them, X k can be used to represent the target spectrum, k can be used to represent the frequency index in the frequency domain; N1 can be used to represent the integer instruction sequence length of the first instruction sequence; n can be used to represent the index of the integer instruction sequence length; x n It can be used to represent the first EEG evaluation signal; e can be used to represent a natural constant; and j can be used to represent an imaginary unit.

[0110] Through the above formula, we can clearly see the relative strength or proportion of different frequency components in the first EEG evaluation signal. For example, if the amplitude of a certain frequency in the frequency domain is large, it means that the frequency is relatively important in the first EEG evaluation signal and contributes more to the sequence; conversely, if the amplitude of a certain frequency is small, then its proportion in the first EEG evaluation signal is relatively small. When considering factors such as flicker frequency, frequency domain information is very critical. By converting the sequence to the frequency domain, we can more directly observe the relationship between the sequence and the flicker frequency, as well as the signal-to-noise ratio distribution at different frequencies, which is conducive to better determining the maximum signal-to-noise ratio in subsequent steps.

[0111] Optionally, the instruction sequence generation module determines the signal-to-noise ratio corresponding to the first EEG evaluation signal based on the amplitude of the target spectrum, the sampling rate of the EEG device, the flicker frequency, and the length of the first EEG evaluation signal. The following formula can be used:

[0112]

[0113] Among them, SNR1 can be used to represent the signal-to-noise ratio corresponding to the first EEG evaluation signal; log 10 It can be used to represent the logarithmic operation with base 10; y(f) can be used to represent the amplitude of the flicker frequency in the target spectrum; f can be used to represent the flicker frequency, which can be the basic flicker frequency, that is, the fundamental frequency; k can be used to represent the index of the frequency point; f s It can be used to indicate the sampling rate of the EEG device; N2 can be used to indicate the length of the first EEG evaluation signal.

[0114] Optionally, the instruction sequence generation module obtains a second EEG evaluation signal from the EEG evaluation signal set, and performs discrete Fourier transform processing on the second EEG evaluation signal to obtain a second spectrum, so as to determine a second signal-to-noise ratio corresponding to the second EEG evaluation signal based on the second amplitude of the second spectrum, the sampling rate of the EEG device, the flicker frequency, and the length of the second EEG evaluation signal. It is understandable that the instruction sequence generation module can determine the signal-to-noise ratio corresponding to each EEG evaluation signal by performing a signal-to-noise ratio calculation on each EEG evaluation signal to obtain a signal-to-noise ratio set.

[0115] It should be noted that the instruction sequence generation module can perform EEG maximum response evaluation on the fundamental frequency and the frequency multiples (such as the first frequency multiple, the second frequency multiple, etc.) corresponding to the first pseudo-random sequence to be traversed in the set of pseudo-random sequences to be traversed, so as to obtain the signal-to-noise ratio corresponding to the fundamental frequency (that is, the signal-to-noise ratio corresponding to the above-mentioned first EEG evaluation signal) and the signal-to-noise ratio corresponding to the frequency multiple, and then further perform a weighted sum operation on the signal-to-noise ratio corresponding to the fundamental frequency and the signal-to-noise ratio corresponding to the frequency multiple to obtain a fused signal-to-noise ratio that integrates the features at the fundamental frequency and the features at the frequency multiple, and then compare the fused signal-to-noise ratio with the fused signal-to-noise ratio of the second pseudo-random sequence to be traversed and the fused signal-to-noise ratio of the third pseudo-random sequence to be traversed to obtain the maximum fused signal-to-noise ratio, so as to find the optimal pseudo-random sequence from the set of pseudo-random sequences to be traversed, and determine a target instruction sequence that better meets the requirements. Among them, the instruction sequence generation module can adopt the existing weighted summation method to perform a weighted sum operation on the signal-to-noise ratio corresponding to the base frequency and the signal-to-noise ratio corresponding to the multiplier, such as using filter bank canonical correlation analysis (Filter Bank Canonical Correlation Analysis, FBCCA) to perform the weighted summation operation, and this application does not impose any restrictions on this.

[0116] In this example, the instruction sequence generation module obtains the first EEG evaluation signal from the EEG evaluation signal set, and performs discrete Fourier transform processing on the first EEG evaluation signal to obtain a target spectrum, so as to further determine the signal-to-noise ratio corresponding to the first EEG evaluation signal based on the amplitude of the flickering frequency in the target spectrum, the sampling rate of the EEG device, the flickering frequency and the length of the first EEG evaluation signal. This is conducive to comparing and screening the signal-to-noise ratios of the EEG evaluation signals corresponding to other pseudo-random sequences to be traversed, so as to find the optimal pseudo-random sequence from the pseudo-random sequence set to be traversed, and to determine a target instruction sequence that better meets the requirements.

[0117] In one embodiment, the decoding module decodes the target EEG signal to obtain the target intention of the target user, which may include the following steps:

[0118] C1: The decoding module filters the target EEG signal through a filter bank to obtain a set of sub-band EEG signals;

[0119] C2: The decoding module calculates the correlation value between each sub-band EEG signal in the sub-band EEG signal set and the reference signal corresponding to each reference intent in the reference intent set to obtain a correlation value vector set;

[0120] C3: The decoding module performs weighted summation on the correlation value vectors corresponding to each reference intent in the correlation value vector set to obtain a set of related feature values;

[0121] C4: The decoding module obtains the maximum relevant eigenvalue from the relevant eigenvalue set;

[0122] C5: The decoding module takes the reference intention corresponding to the maximum correlation eigenvalue as the target intention of the target user.

[0123] The sub-band EEG signal set can include one or more sub-band EEG signals, which can be used to indicate the EEG signal obtained after filtering the target EEG signal through the filter bank. Each sub-band EEG signal retains the EEG signal within a specific frequency range, allowing for a more detailed analysis of the relationship between different frequency components and user intent, helping to more accurately identify the user's target intent during subsequent intent recognition.

[0124] The correlation value vector set may include one or more correlation value vectors, which may be used to indicate the correlation between the reference signal corresponding to the reference intention and the sub-band EEG signal. The reference intention set may include one or more reference intentions, which may be used to indicate the intention corresponding to the stimulation target (i.e., the display icon of the target instruction sequence).

[0125] Optionally, each display icon in the target instruction sequence can flash at a different frequency, and the acquired target EEG signal can also have a corresponding flashing frequency. Therefore, the decoding module can display a sine function of the frequency corresponding to the icon as a reference signal corresponding to the reference intention. Optionally, the process of determining the reference signal by the decoding module can be referred to the following formula:

[0126]

[0127] in, Can be used to represent the reference signal; f k It can be used to represent a specific frequency in the frequency domain; k can be used to represent the frequency index in the frequency domain; m can be used to represent the subband index in the subband EEG signal set, which corresponds to the multiple of the flicker frequency, such as the first subband corresponds to the single multiple of the flicker frequency, the second subband corresponds to the double multiple of the flicker frequency, etc.; t can be used to represent time.

[0128] It is understandable that when the decoding module generates a reference signal, it needs to generate a reference signal with the same length as the target EEG signal in the time domain to facilitate subsequent correlation value calculations. Therefore, the decoding module can generate a reference signal with a specific frequency (such as a flicker frequency) in the time domain according to the above formula.

[0129] Optionally, the decoding module calculates the correlation value of each sub-band EEG signal in the sub-band EEG signal set with the reference signal corresponding to each reference intent in the reference intent set to obtain a correlation value vector set, which can be seen in the following formula:

[0130]

[0131] in, It can be used to represent a correlation value vector in the correlation value vector set, which can be used to represent the correlation value between the first sub-band EEG signal and the frequency doubling of the reference signal corresponding to the kth reference intention; k can be used to represent the index of the reference signal, which corresponds to the frequency index in the frequency domain; ρ(x,y) can be used to represent the operation of performing a typical correlation analysis on x and y, that is, and Perform canonical correlation analysis; It can be used to represent the first sub-band EEG signal; Can be used to represent the transpose of the first sub-band EEG signal; W X Can be used to represent the weight vector for the input signal; It can be used to represent the frequency doubling of the reference signal corresponding to the k-th reference intention; It can be used to represent the transpose of the reference signal corresponding to the kth reference intention at one frequency; W Y Can be used to represent the weight vector for the output signal; It can be used to represent a correlation value vector in the correlation value vector set, and the correlation value vector can be used to represent the correlation value between the first sub-band EEG signal and the double frequency of the reference signal corresponding to the k-th reference intention; It can be used to represent the second sub-band EEG signal; It can be used to represent the transpose of the second sub-band EEG signal; It can be used to represent the double frequency of the reference signal corresponding to the kth reference intention; It can be used to represent the transpose of the double frequency of the reference signal corresponding to the k-th reference intention; It can be used to represent a correlation value vector in the correlation value vector set, and the correlation value vector can be used to represent the correlation value between the Z-th sub-band EEG signal and the Z-frequency multiple of the reference signal corresponding to the k-th reference intention; Z can be used to represent the index of the sub-band EEG signal; It can be used to represent the Zth sub-band EEG signal; It can be used to represent the transpose of the Zth sub-band EEG signal; It can be used to represent the Z-multiple frequency of the reference signal corresponding to the k-th reference intention; It can be used to represent the Z-multiplied transpose of the reference signal corresponding to the k-th reference intent.

[0132] The relevant feature value set may include one or more relevant feature values, which may be used to indicate the relevant feature value obtained after weighted summation of the relevant value vectors in the relevant value vector set. Optionally, the decoding module performs weighted summation of the relevant value vectors corresponding to each reference intent in the relevant value vector set to obtain the relevant feature value set, as shown in the following formula:

[0133]

[0134] w(z)=z -a +b,z∈[1,Z]

[0135] in, It can be used to represent a relevant eigenvalue in the relevant eigenvalue set, and the relevant eigenvalue can be used to represent the relevant eigenvalue obtained after weighted summation of the relevant value vectors corresponding to the k-th reference intention in the relevant value vector set; Z can be used to represent the index of the sub-band EEG signal; w(z) can be used to represent the operation of calculating the weight component of the z-th sub-band EEG signal; It can be used to represent the correlation value between the z-th sub-band EEG signal and the reference signal corresponding to the k-th reference intention in the correlation value vector set; a and b can be used to represent constants that maximize the classification performance.

[0136] By performing weighted summation processing on the correlation value vectors in the correlation value vector set, the correlation between multiple sub-band EEG signals and a reference intention can be comprehensively considered to obtain a correlation feature value that more comprehensively reflects the correlation between the reference intention and the current EEG signal.

[0137] Furthermore, the decoding module obtains the maximum relevant eigenvalue from the set of relevant eigenvalues ​​and uses the reference intention corresponding to the maximum relevant eigenvalue as the target intention of the target user. It is understood that the maximum relevant eigenvalue can represent the intention eigenvalue with the strongest correlation with the current EEG signal among all reference intentions. In this case, the reference intention corresponding to the maximum relevant eigenvalue can be used as the target intention of the target user, that is, the decoding module decodes the user's target intention.

[0138] In this example, the decoding module filters the target EEG signal through a filter group to obtain a sub-band EEG signal set, and calculates the correlation value of each sub-band EEG signal in the sub-band EEG signal set with the reference signal corresponding to each reference intention in the reference intention set to obtain a correlation value vector set, and further performs weighted summation on the correlation value vector corresponding to each reference intention in the correlation value vector set to obtain a correlation eigenvalue set, so that the maximum correlation eigenvalue can be obtained from the correlation eigenvalue set, and then the reference intention corresponding to the maximum correlation eigenvalue can be used as the target intention of the target user, which improves the effectiveness of the target intention recognition process and is conducive to improving the practicality of the visual evoked potential brain-computer interface system.

[0139] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0140] In one embodiment, a high-frequency cold-start visual evoked potential brain-computer interface device is provided, and the high-frequency cold-start visual evoked potential brain-computer interface device corresponds one-to-one with the high-frequency cold-start visual evoked potential brain-computer interface system in the above embodiment. Figure 4 As shown, the high-frequency cold-start visual evoked potential brain-computer interface device includes a first determination module 101, a second determination module 102, a third determination module 103, a display module 104, a receiving module 105 and a decoding module 106. The functional modules are described in detail as follows:

[0141] A first determining module 101 is configured to determine an integer instruction sequence length based on a target number of instructions and a device refresh rate, such that a flickering frequency under the integer instruction sequence length is within a first preset range; wherein the flickering frequency is a quotient of the device refresh rate and the integer instruction sequence length;

[0142] A second determining module 102 is configured to determine a target instruction sequence according to the length of the integer instruction sequence;

[0143] The third determining module 103 is used to sort the local dynamic flashing icons of the target instruction sequence and determine the local dynamic flashing icon display sequence;

[0144] Display module 104, used to display a local dynamic flashing icon display sequence to the target user;

[0145] The receiving module 105 is used to receive the target EEG signal generated by the target user after watching the local dynamic flashing icon display sequence;

[0146] The decoding module 106 is used to decode the target EEG signal to obtain the target intention of the target user.

[0147] In one embodiment, the second determining module 102 is specifically configured to:

[0148] Determine the set of pseudo-random sequences to be traversed according to the length of the integer instruction sequence;

[0149] Performing an EEG maximum response evaluation on each pseudo-random sequence to be traversed in the set of pseudo-random sequences to be traversed, to obtain an EEG evaluation signal set;

[0150] Calculating a signal-to-noise ratio for each EEG evaluation signal in the EEG evaluation signal set to obtain a signal-to-noise ratio set;

[0151] Get the maximum signal-to-noise ratio from the signal-to-noise ratio set;

[0152] The decimal number corresponding to the maximum signal-to-noise ratio is determined as the optimal flicker frequency value;

[0153] Determine the optimal pseudo-random sequence according to the optimal flashing frequency value;

[0154] The optimal pseudo-random sequence is subjected to periodic repetition processing to obtain a target instruction sequence.

[0155] In one embodiment, the second determining module 102 is specifically configured to:

[0156] Acquire a first EEG evaluation signal from the EEG evaluation signal set;

[0157] Performing discrete Fourier transform processing on the first EEG evaluation signal to obtain a target spectrum;

[0158] The signal-to-noise ratio corresponding to the first EEG evaluation signal is determined according to the amplitude of the flicker frequency in the target spectrum, the sampling rate of the EEG device, the flicker frequency, and the length of the first EEG evaluation signal.

[0159] In one embodiment, the third determining module 103 is specifically configured to:

[0160] Segmenting each initial icon corresponding to the target instruction sequence to obtain a local dynamic flashing icon subset corresponding to each initial icon;

[0161] Establishing an associated index between each local dynamic flashing icon in the local dynamic flashing icon subset and the corresponding initial icon to obtain an associated index subset;

[0162] Randomly shuffle each associated index in the associated index subset corresponding to each initial icon to obtain a random associated index subset corresponding to each initial icon;

[0163] A local dynamic flashing icon display sequence is determined according to a random associated index subset corresponding to each initial icon.

[0164] In one embodiment, the decoding module 106 is specifically configured to:

[0165] The target EEG signal is filtered through a filter bank to obtain a set of sub-band EEG signals;

[0166] Calculate the correlation value of each sub-band EEG signal in the sub-band EEG signal set and the reference signal corresponding to each reference intention in the reference intention set to obtain a correlation value vector set;

[0167] Perform weighted sum processing on the correlation value vector corresponding to each reference intention in the correlation value vector set to obtain a correlation eigenvalue set;

[0168] Obtain the maximum relevant eigenvalue from the relevant eigenvalue set;

[0169] The reference intention corresponding to the maximum relevant eigenvalue is taken as the target intention of the target user.

[0170] The present invention provides a high-frequency cold-start visual evoked potential brain-computer interface device. According to the number of instruction targets and the device refresh rate, the length of an integer instruction sequence that makes the flashing frequency within a first preset range is determined, and according to the length of the integer instruction sequence, the target instruction sequence is determined, so as to further sort the target instruction sequence into local dynamic flashing icons, determine the local dynamic flashing icon display sequence, and display the local dynamic flashing icon display sequence to the target user, so as to receive the target EEG signal generated by the target user after watching the local dynamic flashing icon display sequence, and then decode the target EEG signal to obtain the target intention of the target user. While improving the effectiveness of intention recognition, the comfort of the target user during use is improved, which is conducive to improving the practicality of the visual evoked potential brain-computer interface system.

[0171] For the specific limitations of the high-frequency cold-start visual evoked potential brain-computer interface device, please refer to the limitations of the high-frequency cold-start visual evoked potential brain-computer interface system above, which will not be repeated here. The various modules in the above-mentioned high-frequency cold-start visual evoked potential brain-computer interface device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0172] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 5As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile and / or volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external client via a network connection. When the computer program is executed by the processor, it realizes a function or step on the server side of a high-frequency cold-start visual evoked potential brain-computer interface system.

[0173] In one embodiment, a computer device is provided. The computer device may be a client, and its internal structure diagram may be as follows: Figure 6 As shown. The computer device includes a processor, memory, network interface, display screen and input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it realizes the functions or steps on the client side of a high-frequency cold-start visual evoked potential brain-computer interface system.

[0174] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are performed:

[0175] Determining an integer instruction sequence length based on a target number of instructions and a device refresh rate, so that a flickering frequency under the integer instruction sequence length is within a first preset range; wherein the flickering frequency is a quotient of the device refresh rate and the integer instruction sequence length;

[0176] determining a target instruction sequence according to the length of the integer instruction sequence;

[0177] Sorting the local dynamic flashing icons of the target instruction sequence to determine the local dynamic flashing icon display sequence;

[0178] Display a partial dynamic flashing icon display sequence to the target users;

[0179] receiving a target EEG signal generated by a target user after viewing a local dynamic flashing icon display sequence;

[0180] Decode the target EEG signal to obtain the target user's target intention.

[0181] The present invention provides a computer device, which determines the length of an integer instruction sequence that makes the flashing frequency within a first preset range according to the number of instruction targets and the device refresh rate, and determines the target instruction sequence according to the integer instruction sequence length, so as to further sort the target instruction sequence into local dynamic flashing icons, determine the local dynamic flashing icon display sequence, and display the local dynamic flashing icon display sequence to the target user, so as to receive the target EEG signal generated by the target user after watching the local dynamic flashing icon display sequence, and then decode the target EEG signal to obtain the target intention of the target user. While improving the effectiveness of intention recognition, it also improves the comfort of the target user during the use process, which is conducive to improving the practicality of the visual evoked potential brain-computer interface system.

[0182] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0183] Determining an integer instruction sequence length based on a target number of instructions and a device refresh rate, so that a flickering frequency under the integer instruction sequence length is within a first preset range; wherein the flickering frequency is a quotient of the device refresh rate and the integer instruction sequence length;

[0184] determining a target instruction sequence according to the length of the integer instruction sequence;

[0185] Sorting the local dynamic flashing icons of the target instruction sequence to determine the local dynamic flashing icon display sequence;

[0186] Display a partial dynamic flashing icon display sequence to the target users;

[0187] receiving a target EEG signal generated by a target user after viewing a local dynamic flashing icon display sequence;

[0188] Decode the target EEG signal to obtain the target user's target intention.

[0189] The present invention provides a computer-readable storage medium, which determines the length of an integer instruction sequence that makes the flashing frequency within a first preset range according to the target number of instructions and the refresh rate of the device, and determines the target instruction sequence according to the length of the integer instruction sequence, so as to further sort the target instruction sequence into local dynamic flashing icons, determine the local dynamic flashing icon display sequence, and display the local dynamic flashing icon display sequence to the target user, so as to receive the target EEG signal generated by the target user after watching the local dynamic flashing icon display sequence, and then decode the target EEG signal to obtain the target intention of the target user. While improving the effectiveness of intention recognition, it also improves the comfort of the target user during the use process, which is conducive to improving the practicality of the visual evoked potential brain-computer interface system.

[0190] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or computer device can be found in the relevant descriptions of the server side and the client side in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0191] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0192] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0193] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A high-frequency cold-start visual evoked potential brain-computer interface system, characterized in that: The high-frequency cold-start visual evoked potential brain-computer interface system includes an instruction sequence generation module, a local dynamic flashing module, a user display interface and a decoding module, wherein: The instruction sequence generation module determines an integer instruction sequence length based on a target number of instructions and a device refresh rate, so that a flickering frequency under the integer instruction sequence length is within a first preset range; wherein the flickering frequency is a quotient of the device refresh rate and the integer instruction sequence length; The instruction sequence generation module determines a target instruction sequence according to the integer instruction sequence length; The local dynamic flashing module sorts the target instruction sequence by local dynamic flashing icons to determine a local dynamic flashing icon display sequence; The user display interface displays the local dynamic flashing icon display sequence to the target user; The decoding module receives a target EEG signal generated by the target user after watching the local dynamic flashing icon display sequence; The decoding module decodes the target EEG signal to obtain the target intention of the target user; The instruction sequence generation module determines a set of pseudo-random sequences to be traversed according to the length of the integer instruction sequence; The instruction sequence generation module performs an EEG maximum response evaluation on each pseudo-random sequence to be traversed in the set of pseudo-random sequences to be traversed to obtain an EEG evaluation signal set; The instruction sequence generation module calculates the signal-to-noise ratio of each EEG evaluation signal in the EEG evaluation signal set to obtain a signal-to-noise ratio set; The instruction sequence generation module obtains a maximum signal-to-noise ratio from the signal-to-noise ratio set; The instruction sequence generation module determines the decimal number corresponding to the maximum signal-to-noise ratio as the optimal flicker frequency value; The instruction sequence generation module determines an optimal pseudo-random sequence according to the optimal flashing frequency value; The instruction sequence generation module performs periodic repetition processing on the optimal pseudo-random sequence to obtain a target instruction sequence; The instruction sequence generation module obtains a first EEG evaluation signal from the EEG evaluation signal set; The instruction sequence generation module performs discrete Fourier transform processing on the first EEG evaluation signal to obtain a target spectrum; The instruction sequence generation module determines the signal-to-noise ratio corresponding to the first EEG evaluation signal based on the amplitude of the flicker frequency in the target spectrum, the sampling rate of the EEG device, the flicker frequency and the length of the first EEG evaluation signal.

2. The high-frequency cold-start visual evoked potential brain-computer interface system according to claim 1, characterized in that: in, The local dynamic flashing module performs segmentation processing on each initial icon corresponding to the target instruction sequence to obtain a local dynamic flashing icon subset corresponding to each initial icon; The local dynamic flashing module establishes an association index between each local dynamic flashing icon in the local dynamic flashing icon subset and the corresponding initial icon to obtain an association index subset; The local dynamic flashing module randomly shuffles each associated index in the associated index subset corresponding to each of the initial icons to obtain a random associated index subset corresponding to each of the initial icons; The local dynamic flashing module determines a local dynamic flashing icon display sequence according to the random associated index subset corresponding to each of the initial icons.

3. The high-frequency cold-start visual evoked potential brain-computer interface system according to claim 2, characterized in that: in, The decoding module filters the target EEG signal through a filter bank to obtain a sub-band EEG signal set; The decoding module calculates a correlation value between each sub-band EEG signal in the sub-band EEG signal set and a reference signal corresponding to each reference intent in the reference intent set to obtain a correlation value vector set; The decoding module performs weighted summation processing on the correlation value vector corresponding to each reference intent in the correlation value vector set to obtain a correlation feature value set; The decoding module obtains a maximum relevant eigenvalue from the relevant eigenvalue set; The decoding module uses the reference intention corresponding to the maximum relevant eigenvalue as the target intention of the target user.

4. A high-frequency cold-start visual evoked potential brain-computer interface device, characterized in that: The high-frequency cold-start visual evoked potential brain-computer interface device comprises: a first determining module, configured to determine a length of an integer instruction sequence based on a target number of instructions and a device refresh rate, such that a flickering frequency under the integer instruction sequence length is within a first preset range; wherein the flickering frequency is a quotient of the device refresh rate and the integer instruction sequence length; A second determining module is used to determine a target instruction sequence according to the length of the integer instruction sequence; a third determining module, configured to sort the target instruction sequence by local dynamic flashing icons and determine a local dynamic flashing icon display sequence; A display module, configured to display the local dynamic flashing icon display sequence to a target user; A receiving module, configured to receive a target electroencephalogram signal generated by the target user after viewing the local dynamic flashing icon display sequence; A decoding module, configured to decode the target EEG signal to obtain the target intention of the target user; The second determination module is specifically configured to: Determining a set of pseudo-random sequences to be traversed according to the length of the integer instruction sequence; Performing an EEG maximum response evaluation on each pseudo-random sequence to be traversed in the set of pseudo-random sequences to be traversed to obtain an EEG evaluation signal set; Calculating a signal-to-noise ratio for each EEG evaluation signal in the EEG evaluation signal set to obtain a signal-to-noise ratio set; Obtaining a maximum signal-to-noise ratio from the signal-to-noise ratio set; Determine the decimal number corresponding to the maximum signal-to-noise ratio as the optimal flicker frequency value; Determining an optimal pseudo-random sequence according to the optimal flicker frequency value; Performing a periodic repetitive sequence processing on the optimal pseudo-random sequence to obtain a target instruction sequence; The second determining module is specifically configured to: Acquire a first EEG evaluation signal from the EEG evaluation signal set; Performing discrete Fourier transform processing on the first EEG evaluation signal to obtain a target spectrum; The signal-to-noise ratio corresponding to the first EEG evaluation signal is determined according to the amplitude of the flicker frequency in the target spectrum, the sampling rate of the EEG device, the flicker frequency and the length of the first EEG evaluation signal.

5. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method of the high-frequency cold-start visual evoked potential brain-computer interface system according to any one of claims 1 to 3 is implemented.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method of the high-frequency cold-start visual evoked potential brain-computer interface system according to any one of claims 1 to 3 is implemented.

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