CN115188447A - Memory training method and device based on electroencephalogram signals - Google Patents

Memory training method and device based on electroencephalogram signals Download PDF

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CN115188447A
CN115188447A CN202211095181.1A CN202211095181A CN115188447A CN 115188447 A CN115188447 A CN 115188447A CN 202211095181 A CN202211095181 A CN 202211095181A CN 115188447 A CN115188447 A CN 115188447A
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tasks
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trained user
memory
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CN115188447B (en
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韩璧丞
周超前
苏度
张杨
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Zhejiang Qiangnao Technology Co ltd
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Abstract

The invention relates to the technical field of memory training, in particular to a memory training method and a memory training device based on electroencephalogram signals. The invention marks a part of original tasks and then trains users to remember the marked original tasks. And then, according to the concentration of the trained user, adjusting the number of the original tasks, namely, newly adding a part of tasks without marks in the original tasks and then removing the marks of the original tasks. The trained user is required to sort out previously tagged tasks from the new tasks to achieve training of his memory. And the trained user can rapidly improve the memory of the trained user by repeatedly doing tasks. In addition, the number of the tasks is set according to the concentration, and the greater the concentration is, the training user can concentrate on training, so that the number of the tasks can be properly increased, and the memory training effect can be rapidly improved.

Description

Memory training method and device based on electroencephalogram signals
Technical Field
The invention relates to the technical field of memory training, in particular to a memory training method and a memory training device based on electroencephalogram signals.
Background
The quality of memory has important influence on work, life and study, and people with poor memory need to carry out acquired training to improve the memory of the people. The prior art only depends on a person to consciously remember some things, and the training mode lacks reproducibility, so that the memory can not be effectively improved.
In summary, the prior art is poor in memory training effect.
Thus, there is a need for improvements and enhancements in the art.
Disclosure of Invention
In order to solve the technical problems, the invention provides a memory training method and a training device based on electroencephalogram signals, and solves the problem of poor memory training effect in the prior art.
In order to realize the purpose, the invention adopts the following technical scheme:
in a first aspect, the present invention provides a memory training method based on electroencephalogram signals, wherein the method comprises:
marking an original task;
calculating the concentration of the trained user according to the electroencephalogram signals of the trained user;
according to the special attention, the task quantity of the original task is adjusted to obtain an adjusted task which is recorded as a new task, and the new task is not marked;
collecting a target task selected from the new tasks by the trained user;
and training the memory of the trained user according to the matching degree of the target task and the marked original task.
In one implementation, the calculating the concentration of the trained user according to the electroencephalogram signal of the trained user includes:
calculating the mean square error of the electroencephalogram historical signal of the trained user;
determining the preset time length of the electroencephalogram signal to be acquired according to the mean square error, wherein the preset time length is the time length from the original task to remove the mark;
and calculating the attention of the trained user according to the electroencephalogram signals of the trained user within the preset time.
In one implementation, the adjusting the number of tasks of the original task according to the concentration force to obtain an adjusted task, and marking as a new task, where the new task is unmarked and includes:
counting the number of marked tasks in the original tasks;
adjusting the task quantity of the original task according to the special attention and the marked task quantity to obtain an initial adjustment task;
adjusting the position of the initial adjustment task;
and removing the mark of the initial adjustment task after the position is adjusted to obtain a new task.
In one implementation, the adjusting the task number of the original task according to the attention and the marked task number to obtain an initial task includes:
obtaining a grid composed of squares in the original task according to the original task;
and when the attention force is greater than the threshold value and the marked task number is less than the set number, increasing squares with preset number around the grid to obtain an initial adjustment task.
In one implementation, the collecting the target task selected by the trained user from the new tasks includes:
determining a preset execution time length for doing a task according to the special attention;
and acquiring a target task selected from the new task by the trained user within the preset execution time after the mark of the original task is removed.
In one implementation, the training the memory of the trained user according to the matching degree of the target task and the labeled original task comprises:
counting the number of the original tasks before the marks are removed in the target tasks, and recording the number as the matching number;
and adjusting the number of the marked original tasks according to the matching number, and continuing to train the memory of the trained user.
In one implementation, the marking of the original task includes:
obtaining a grid composed of squares in the original task according to the original task;
obtaining the number of marks corresponding to the blocks to be marked according to the ages of the trained users;
selecting squares with the number of marks from the grid, and marking the squares as squares needing to be marked, wherein the squares needing to be marked form a set shape;
and marking the square blocks to be marked with set colors.
In a second aspect, an embodiment of the present invention further provides a memory training device based on an electroencephalogram signal, where the device includes the following components:
the marking module is used for marking the original task;
the attention-focusing calculation module is used for calculating the attention of the trained user according to the electroencephalogram signals of the trained user;
the task generation module is used for adjusting the task quantity of the original task according to the special attention to obtain an adjusted task which is recorded as a new task, and the new task is unmarked;
the acquisition module is used for acquiring a target task selected from the new tasks by the trained user;
and the training module is used for training the memory of the trained user according to the matching degree of the target task and the marked original task.
In one implementation, a concentration calculation module includes:
the variance calculation unit is used for calculating the mean square error of the electroencephalogram historical signal of the trained user;
the duration calculation unit is used for determining the preset duration of the electroencephalogram signal to be acquired according to the mean square error, wherein the preset duration is the duration of removing the mark from the original task;
and the attention calculating unit is used for calculating the attention of the trained user according to the electroencephalogram signals of the trained user within the preset time.
In one implementation, a task generation module includes:
the quantity counting unit is used for counting the quantity of the marked tasks in the original tasks;
the task quantity adjusting unit is used for adjusting the task quantity of the original task according to the special attention and the marked task quantity to obtain an initial adjustment task;
the task position adjusting unit is used for adjusting the position of the initial adjustment task;
and the mark removing unit is used for removing the mark of the initial adjustment task after the position is adjusted to obtain a new task.
In one implementation, the task number adjusting unit includes:
the grid generation component is used for obtaining a grid formed by squares in the original task according to the original task;
and the task adjusting component is used for increasing squares with preset number around the grid to obtain an initial adjustment task when the concentration force is greater than a threshold value and the number of marked tasks is less than a set number.
In one implementation, an acquisition module includes:
the preset duration calculation unit is used for determining the preset execution duration of the task according to the attention force;
and the acquisition unit is used for acquiring a target task selected from the new tasks by the trained user within the preset execution time after the mark of the original task is removed.
In one implementation, a training module includes:
the matching unit is used for counting the number of the original tasks before the marks are removed in the target tasks and recording the number as the matching number;
and the training unit is used for adjusting the number of the marked original tasks according to the matching number and continuing to train the memory of the trained user.
In one implementation, a tagging module includes:
the grid generation unit is used for obtaining grids formed by squares in the original task according to the original task;
the label quantity calculating unit is used for obtaining the label quantity corresponding to the square block to be labeled according to the age of the trained user;
the selecting unit is used for selecting the marked squares from the grids and marking the marked squares as squares needing to be marked, and the squares needing to be marked form a set shape;
and the marking unit is used for marking the square needing to be marked into a set color.
In a third aspect, an embodiment of the present invention further provides a terminal device, where the terminal device includes a memory, a processor, and a memory training program based on an electroencephalogram signal, where the memory training program based on an electroencephalogram signal is stored in the memory and is executable on the processor, and when the processor executes the memory training program based on an electroencephalogram signal, the steps of the memory training method based on an electroencephalogram signal are implemented.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, where a memory training program based on an electroencephalogram signal is stored on the computer-readable storage medium, and when the memory training program based on the electroencephalogram signal is executed by a processor, the steps of the memory training method based on the electroencephalogram signal are implemented.
Has the beneficial effects that: the invention marks a part of original tasks and then is trained to remember the marked original tasks. And then, adjusting the number of the original tasks according to the concentration of the trained user, namely newly adding a part of tasks without marks in the original tasks and then removing the marks of the original tasks. The trained user is required to sort out previously tagged tasks from the new tasks to achieve training of his memory. And the trained user can rapidly improve the memory of the trained user by repeatedly doing tasks. In addition, the invention sets the number of tasks according to the concentration, and the greater the concentration, the training user can concentrate on the training, so the number of tasks can be properly increased, and the memory training effect can be more quickly improved.
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FIG. 1 is an overall flow chart of the present invention;
fig. 2 is a schematic block diagram of an internal structure of a terminal device according to an embodiment of the present invention.
Detailed Description
The technical solution of the present invention is clearly and completely described below with reference to the embodiments and the drawings. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The quality of memory has important influence on work, life and study, and people with poor memory need to carry out acquired training to improve the memory. The prior art only depends on a person to remember something consciously, and the training mode lacks reproducibility, so that the memory can not be effectively improved.
In order to solve the technical problems, the invention provides a memory training method and a training device based on electroencephalogram signals, and solves the problem of poor memory training effect in the prior art. When the method is specifically implemented, an original task is marked, and then the attention of a trained user is calculated according to an electroencephalogram signal of the trained user; then, according to the special attention, the task quantity of the original task is adjusted to obtain an adjusted task (a new unmarked task), and then a target task selected from the new task by a trained user is collected; and finally, training the memory of the trained user according to the matching degree of the target task and the marked original task. The memory training method can effectively improve the memory of the trained user.
In a possible application scenario, the memory training method provided by the invention can be applied to the elderly with memory impairment. The original tasks are marked, then the number of the tasks is increased according to the concentration of the old people in training, all the marks are removed, and the old people are prompted to select the tasks marked before from the new tasks according to the memory, so that the memory of the old people is trained. Therefore, the memory training method can accurately provide a better memory training effect for the old with hypomnesis.
In another possible application scenario, the memory training method provided by the invention can be applied to children with hyperactivity disorder (ADHD). The original tasks are marked, then the number of the tasks is increased according to the concentration of the hyperkinetic child in training, all the marks are removed, and the child is prompted to select the previously marked tasks from the new tasks according to the memory, so that the memory of the child is trained.
For example, the original task is a four-square grid composed of four squares, wherein the squares at the upper left corner and the lower right corner are marked with red color, if the concentration of the trained user is high, the four-square grid is changed into a nine-square grid (the number of squares is increased, namely the number of tasks is increased, the nine-square grid is a new task), and then the red color on the squares is removed. The trained user is prompted to pick the previously red-labeled box out of the squared boxes, indicating that the trained user's memory is good if both boxes picked by the trained user are previously red-labeled boxes. If only one of the two blocks selected by the trained user is the block previously marked red or neither of the two blocks is the block previously marked red, it indicates that the trained user has poor memory and further training of his memory needs to be enhanced. The embodiment adopts the attention of the trained user, and controls the number of the squares to be increased, because the attention of the trained user indicates that the trained user is performing training seriously, the number of the squares can be increased more to improve the training difficulty, thereby improving the memory of the trained user quickly. If the concentration is small, it indicates that the trained user is not performing training seriously, if the number of the blocks is continuously increased to increase the training difficulty, and if the trained user does not select the previously marked blocks according to the memory, the method cannot distinguish whether the blocks are selected wrongly because the trained user has poor memory, or the blocks are selected wrongly because the trained user is not concentrated enough. And this embodiment has increased the factor of concentration on, can effectively formulate the training scheme that is applicable to the user current state that is trained to help promoting user's memory.
Exemplary method
The memory training method based on the electroencephalogram signals can be applied to terminal equipment, and the terminal equipment can be terminal products with a video playing function, such as televisions, computers, mobile phones and the like. In this embodiment, as shown in fig. 1, the memory training method based on electroencephalogram signals specifically includes the following steps:
and S100, marking the original task.
The original task of the embodiment is to form a four-square grid by four squares, and the marking is to coat a part or all of the squares with green color, so that the marking is realized by selecting the green color, because the green color is obvious, the recognition degree can be increased, and the memory is convenient. The final memory error caused by the error recognition can be prevented, namely, the selection of the green color mark can ensure that the final memory training result reflects the real memory quality of the user. Step S100 includes the steps of:
s101, obtaining a grid composed of squares in the original task according to the original task.
In this example, the original task is Sudoku.
S102, obtaining the number of marks corresponding to the square blocks to be marked according to the ages of the trained users.
In this embodiment, the smaller the age of the trained user, the larger the number of marked squares. Because the young age has the memory of the young age as it is, better squares are marked to increase the difficulty of memory to match the current memory so as to better indicate the memory.
S103, selecting the marked squares from the grids, and marking the marked squares as squares needing to be marked, wherein the squares needing to be marked form a set shape.
For example, if the number of the marks in step S102 is three, three blocks are selected from the squared figure for marking, and the three blocks communicate with each other in an L shape (set shape), so that the shape formed by the marked blocks is limited, which is to train the user to learn scientific memory.
And S104, marking the square needing to be marked into a set color.
In another embodiment, the color is set to green, and numbers are marked on the blocks to be marked, so as to train the sensitivity of the user to the numbers.
S200, calculating the attention of the trained user according to the electroencephalogram signals of the trained user.
In one embodiment, step S200 includes steps S201, S202, and S203 as follows:
s201, calculating the mean square error of the electroencephalogram historical signal of the trained user.
In the embodiment, electroencephalogram historical signals of a trained user within one month before the training is started are collected, and then the mean square error of the electroencephalogram historical signals is calculated.
S202, determining the preset time length of the electroencephalogram signal to be acquired according to the mean square error, wherein the preset time length is the time length from the original task to remove the mark.
S203, calculating the attention of the trained user according to the electroencephalogram signals of the trained user within the preset time.
If the mean square error is too large, the electroencephalogram signal of the trained user fluctuates too much, so that the preset time needs to be increased, the collected current electroencephalogram signal can sufficiently reflect the current attention, and if the electroencephalogram signal of the trained user fluctuates greatly, the attention is calculated according to the electroencephalogram signal in a short time, the attention calculated by the instantaneous electroencephalogram signal is large or small, and the real attention of the trained user cannot be represented. On the contrary, if the mean square error is small, the instantaneous electroencephalogram signals can also represent the real attention of the trained user, and the acquisition of the electroencephalogram signals in a short time can reduce the calculation amount.
And S300, adjusting the task quantity of the original task according to the attention force to obtain an adjusted task, and recording the adjusted task as a new task, wherein the new task is unmarked.
Step S300 includes steps S301 to S305 as follows:
s301, counting the number of marked tasks in the original tasks.
S302, obtaining a grid composed of squares in the original task according to the original task.
For example, if the original task is a squared figure consisting of three squares marked green, the number of marked tasks is three.
And S303, when the attention force is greater than a threshold value and the number of marked tasks is less than a set number, increasing squares with preset number around the grid to obtain an initial adjustment task.
When the concentration of the trained user is high and the number of marked tasks is small, the memory training difficulty is low, and the trained user is not suitable, so that the nine-grid is changed into the sixteen-grid by increasing the number of the squares in the grid to increase the training difficulty, and the method is suitable for the trained user.
S304, adjusting the position of the initial tuning task.
S305, removing the mark of the initial tuning task after the position is adjusted to obtain a new task.
In this embodiment, the position of the task is adjusted by rotating the sixteen grids ninety degrees clockwise, for example, if the previously marked square is located at the lower right corner of the sixteen grids, the previously marked square is now located at the lower left corner of the sixteen grids. And adjusting the positions of the blocks marked before to increase the difficulty of memory, wherein if the trained user can still remember which block is marked after the difficulty is increased, the memory of the trained user is good enough. Therefore, the present embodiment further increases the training difficulty, and can identify the real degree of memory of the remembered user.
S400, collecting the target task selected by the trained user from the new tasks.
Step S400 includes steps S401 and S402 as follows:
s401, according to the special attention, determining a preset execution time length for doing a task.
In this embodiment, the greater the attentiveness of the trained user, the shorter the preset execution time for the task, i.e. the greater the attentiveness of the trained user, the shorter the time it is required to find the previously labeled square.
S402, collecting the target task selected from the new task by the trained user within the preset execution time after the mark of the original task is removed.
The original task is to display nine-square grids on the interface, wherein part of the squares are marked as green, then expand the nine-square grids into sixteen squares, remove the green on the squares, and then require the trained user to select the squares marked with green before according to the memory within the preset execution time.
S500, training the memory of the trained user according to the matching degree of the target task and the marked original task.
The specific process of step S500: and counting the number of the original tasks before the marks are removed in the target tasks, and recording the number as the matching number. And adjusting the number of the marked original tasks according to the matching number, and continuing to train the memory of the trained user.
In this embodiment, when there are three marked blocks in the original task and the trained user finds the three blocks again after removing the mark, the matching degree is one hundred percent. The memory of the trained user is good, so that the block description can be continuously increased to enable the trained user to find the previously marked block in more blocks according to the memory. In the embodiment, the number of the squares is continuously increased, so that the training difficulty is dynamically adjusted, the trained user is helped to gradually improve the memory, and a better training effect is realized.
In another embodiment, a head ring is worn on the head of a trained user, a gyroscope is arranged in the head ring, three squares in the nine-square grid are filled with colors, the trained user requires to remember the squares filled with the colors and then removes the colors, and the trained user moves the external squares to the positions where the squares filled with the colors are located in a manner of adding concentration force values through head motions (up, down, left and right) so as to train the memory of the trained user.
In summary, the present invention labels a portion of the original tasks and then is trained to remember the labeled original tasks. And then, adjusting the number of the original tasks according to the concentration of the trained user, namely newly adding a part of tasks without marks in the original tasks and then removing the marks of the original tasks. The trained user is required to sort out previously tagged tasks from the new tasks to achieve training of his memory. And the trained user can rapidly improve the memory of the trained user by repeatedly doing tasks. In addition, the number of the tasks is set according to the concentration, and the greater the concentration is, the training user can concentrate on training, so that the number of the tasks can be properly increased, and the memory training effect can be rapidly improved. In addition, the memory training method is also beneficial to improving the memory of ADHD children.
Exemplary devices
The embodiment also provides a memory training device based on the electroencephalogram signal, which comprises the following components:
the marking module is used for marking the original task;
the attention-focusing calculation module is used for calculating the attention of the trained user according to the electroencephalogram signals of the trained user;
the task generating module is used for adjusting the task quantity of the original task according to the special attention to obtain an adjusted task which is marked as a new task, and the new task is unmarked;
the acquisition module is used for acquiring a target task selected from the new tasks by the trained user;
and the training module is used for training the memory of the trained user according to the matching degree of the target task and the marked original task.
Based on the above embodiments, the present invention further provides a terminal device, and a schematic block diagram thereof may be as shown in fig. 2. The terminal equipment comprises a processor, a memory, a network interface, a display screen and a temperature sensor which are connected through a system bus. Wherein the processor of the terminal device is configured to provide computing and control capabilities. The memory of the terminal equipment comprises a nonvolatile 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 an operating system and computer programs in the non-volatile storage medium. The network interface of the terminal device is used for connecting and communicating with an external terminal through a network. The computer program is executed by a processor to implement a brain electrical signal-based memory training method. The display screen of the terminal equipment can be a liquid crystal display screen or an electronic ink display screen, and the temperature sensor of the terminal equipment is arranged in the terminal equipment in advance and used for detecting the operating temperature of the internal equipment.
It will be understood by those skilled in the art that the block diagram of fig. 2 is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation to the terminal device to which the solution of the present invention is applied, and a specific terminal device may include more or less components than those shown in the figure, or may combine some components, or have different arrangements of components.
In one embodiment, a terminal device is provided, where the terminal device includes a memory, a processor, and a memory training program based on electroencephalogram signals, the memory training program based on electroencephalogram signals being stored in the memory and being executable on the processor, and when the processor executes the memory training program based on electroencephalogram signals, the following operation instructions are implemented:
marking an original task;
calculating the concentration of the trained user according to the electroencephalogram signals of the trained user;
according to the special attention, the task quantity of the original task is adjusted to obtain an adjusted task which is recorded as a new task, and the new task is not marked;
collecting a target task selected from the new tasks by the trained user;
and training the memory of the trained user according to the matching degree of the target task and the marked original task.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, databases, or other media used in embodiments provided herein may 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 a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), rambus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. A memory training method based on electroencephalogram signals is characterized by comprising the following steps:
marking an original task;
calculating the concentration of the trained user according to the electroencephalogram signals of the trained user;
according to the special attention, the task quantity of the original task is adjusted to obtain an adjusted task which is recorded as a new task, and the new task is not marked;
collecting a target task selected from the new tasks by the trained user;
and training the memory of the trained user according to the matching degree of the target task and the marked original task.
2. The brain-electrical-signal-based memory training method according to claim 1, wherein said calculating the concentration of the trained user based on the brain electrical signal of the trained user comprises:
calculating the mean square error of the electroencephalogram historical signal of the trained user;
determining the preset time length of the electroencephalogram signal to be acquired according to the mean square error, wherein the preset time length is the time length from the original task to the mark removal;
and calculating the attention of the trained user according to the electroencephalogram signals of the trained user within the preset time.
3. The brain electrical signal-based memory training method of claim 1, wherein said adjusting the number of tasks of said original task according to said attention to obtain an adjusted task, which is recorded as a new task, said new task being label-free, comprises:
counting the number of marked tasks in the original tasks;
adjusting the task number of the original task according to the special attention and the marked task number to obtain an initial adjustment task;
adjusting the position of the initial adjustment task;
and removing the mark of the initial tuning task after the position is adjusted to obtain a new task.
4. The brain electrical signal-based memory training method of claim 3, wherein said adjusting the number of tasks of said original task according to said attention and the number of labeled tasks to obtain an initial task comprises:
obtaining a grid composed of squares in the original task according to the original task;
and when the attention force is greater than the threshold value and the marked task number is less than the set number, increasing squares with preset number around the grid to obtain an initial adjustment task.
5. The brain electrical signal-based memory training method of claim 1, wherein said acquiring a target task selected by said trained user from said new tasks comprises:
determining a preset execution time length for doing a task according to the attention force;
and acquiring a target task selected from the new task by the trained user within the preset execution time after the mark of the original task is removed.
6. The brain electrical signal-based memory training method of claim 1, wherein said training the memory of the trained user according to the matching degree of the target task and the labeled primitive task comprises:
counting the number of the original tasks before the marks are removed in the target tasks, and recording the number as the matching number;
and adjusting the number of the marked original tasks according to the matching number, and continuing to train the memory of the trained user.
7. The brain electrical signal-based memory training method of claim 1, wherein said labeling of primitive tasks comprises:
obtaining a grid composed of squares in the original task according to the original task;
obtaining the number of marks corresponding to the square blocks to be marked according to the age of the trained user;
selecting squares with the number of marks from the grid, and marking the squares as squares needing to be marked, wherein the squares needing to be marked form a set shape;
and marking the square blocks to be marked with set colors.
8. The utility model provides a memory training device based on brain electrical signal which characterized in that, the device includes following component parts:
the marking module is used for marking the original task;
the attention-focusing calculation module is used for calculating the attention of the trained user according to the electroencephalogram signals of the trained user;
the task generation module is used for adjusting the task quantity of the original task according to the special attention to obtain an adjusted task which is recorded as a new task, and the new task is unmarked;
the acquisition module is used for acquiring a target task selected from the new tasks by the trained user;
and the training module is used for training the memory of the trained user according to the matching degree of the target task and the marked original task.
9. A terminal device, characterized in that the terminal device comprises a memory, a processor and a brain electrical signal-based memory training program stored in the memory and operable on the processor, and when the processor executes the brain electrical signal-based memory training program, the steps of the brain electrical signal-based memory training method according to any one of claims 1 to 7 are implemented.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a brain electrical signal-based memory training program, which when executed by a processor, implements the steps of the brain electrical signal-based memory training method according to any one of claims 1 to 7.
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