CN113577495A - Children attention deficit hyperactivity disorder auxiliary treatment system based on BCI-VR - Google Patents
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Abstract
本发明公开了一种基于BCI‑VR的儿童多动症辅助治疗系统，由脑机接口硬件和虚拟现实游戏两部分组成，硬件部分通过使用脑机接口设备获取脑电EEG信号，将EEG信号传输到计算机应用程序，游戏软件将接收到的脑电信号转换为专注度信号作为驱动参数控制游戏进程，用于多动症儿童的注意力集中训练，该辅助治疗系统在提高多动症儿童的注意力方面发挥着积极的作用。
The invention discloses a BCI-VR-based auxiliary treatment system for children's hyperactivity disorder, which is composed of two parts, a brain-computer interface hardware and a virtual reality game. The application, the game software converts the received EEG signals into concentration signals as driving parameters to control the game process, and is used for attention training in children with ADHD. This adjuvant therapy system plays an active role in improving the attention of children with ADHD. effect.
Description
Technical Field
The invention relates to the technical field of children's Attention Deficit Hyperactivity Disorder (ADHD) adjuvant therapy, in particular to a children's Attention Deficit Hyperactivity Disorder (ADHD) adjuvant therapy system based on BCI-VR.
Background
Attention Deficit Hyperactivity Disorder (ADHD) is a common syndrome of mild brain dysfunction, with prevalence varying from 5% to 7% in the school-age population in childhood. The disease is characterized by inattention, hyperactivity, and difficulty in controlling impulses. Clinical researches find that children suffering from attention deficit hyperactivity disorder have poor learning performance and reduced social functions, and have great influence on the children after the children grow up. At present, the treatment means of the attention deficit hyperactivity disorder is drug treatment. Although the medicine takes effect quickly when treating the attention deficit hyperactivity disorder, the side effects of the medicine are obvious. The drug increases blood pressure, heart rate and the QT interval of the electrocardiogram.
With the development of brain-computer interfaces and virtual reality technologies, researchers propose to introduce BCI-VR into the treatment of hyperactivity, and BCI-VR technology has unique advantages in rehabilitation therapy of children with hyperactivity compared with traditional medical treatment methods. In 6 months 2020, the U.S. Food and Drug Administration (FDA) certified a game EndeavorRx as a prescribed drug for the treatment of childhood hyperkinetic. The game has been tested in a seven year clinical trial with over 600 children, providing effective evidence that the game can improve attention function in children with hyperactivity between 8 and 12 years of age when treating neurological diseases. The game is suggested for the treatment of inattention or combined ADHD which presents attention problems. This is the first game-based treatment device approved by the FDA in the united states for any type of medical condition. However, the game therapy device also has disadvantages. Firstly, the EndeovorX only provides the function of attention training, cannot monitor electroencephalogram signals of a user in real time, intuitively reflects the attention state of the user and feeds the state back to the user; second, the age of the appropriate users of EndeovorX is 8-12 years, and patients with low-age hyperactivity are difficult to train for use due to the difficulty of playing the game.
Disclosure of Invention
The invention aims to provide a BCI-VR-based children hyperkinetic syndrome auxiliary treatment system which combines nerve training with electronic games and provides more fun and participation.
In order to achieve the purpose, the invention is implemented according to the following technical scheme:
a BCI-VR based pediatric attention deficit hyperactivity disorder ("rdi") adjunctive therapy system comprising:
the brain wave signal acquisition and processing module is used for acquiring electroencephalogram signals of children; carrying out noise reduction and filtering on the acquired electroencephalogram signals to obtain effective electroencephalogram signal data, and amplifying the effective electroencephalogram signal data;
the analog-to-digital conversion module is connected with the brain wave signal acquisition and processing module and is used for converting the amplified electroencephalogram signals into digital signals;
through the host computer that bluetooth module and brain electrical signal collection module link to each other, install the virtual reality recreation that Unity3D developed in the host computer, the host computer is used for handling brain electrical signal for concentration degree numerical value, encodes the concentration degree value of acquireing, represents the different states of user to concentration degree data adopts normalized weighted average algorithm to handle, the host computer sends the concentration degree numerical value of normalized weighted average algorithm processing to Unity3D, according to the motion of the role in the concentration degree value control scene.
Furthermore, the electroencephalogram signal acquisition and processing module adopts a Neurosky neural thought technology TGAM module.
Further, the concentration value is encoded as follows:
| encoding | Concentration degree value | Description of the case |
| A1 | 1-20 | The attention is not concentrated very much |
| A2 | 21-40 | Attention is less concentrated |
| A3 | 41-60 | Normal attention |
| A4 | 61-80 | Attention is focused on |
| A5 | 81-100 | Attention is very focused |
。
Further, the specific steps of applying the normalized weighted average algorithm to the concentration data are:
grouping the concentration degree value and the relaxation degree value of the front 3s after the feedback processing, respectively weighting, and sampling in 3s to obtain a group of data lambdaiThe data length is N, i.e. i belongs to [1, N ∈]Normalized weighted mean method, weighted value of
i∈[1,N]；
(a) By an array λi，i∈[1,N]The average of the concentration values of the first 3s can be obtained
(b) Calculating each concentration value lambdaiRelative to the mean value
Deviation value of (a) Δ λi：
(c) Deviation value delta lambdaiCarry-in weight function
Is normalized to obtain
i∈[1,N]：
(d) Deriving weight values from normalized deviation values
i∈[1,N]：
(e) The final average value is obtained from the weighted value
Namely:
(f) obtaining the normalized weighted average value of the power looseness numerical value in 3s in the same way
(g) Finally, averaging the two values to obtain a concentration degree value A of the system control character:
compared with the prior art, the brain-computer interface based intelligent brain-computer training system comprises two parts, namely brain-computer interface hardware and a virtual reality game, wherein the hardware part acquires electroencephalogram EEG signals by using brain-computer interface equipment, the EEG signals are transmitted to a computer application program, game software converts the received electroencephalogram signals into concentration degree signals to be used as driving parameters to control a game process, the concentration degree signals are used for the attention concentration training of the children with the hyperactivity, and the auxiliary treatment system plays an active role in improving the attention of the children with the hyperactivity.
Drawings
FIG. 1 is a schematic diagram of the system of the present invention.
Fig. 2 is a game operation scenario according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. The specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
As shown in fig. 1, this embodiment specifically provides a BCI-VR based adjuvant therapy system for attention deficit hyperactivity disorder in children, which includes:
the brain wave signal acquisition and processing module is used for acquiring electroencephalogram signals of children; carrying out noise reduction and filtering on the acquired electroencephalogram signals to obtain effective electroencephalogram signal data, and amplifying the effective electroencephalogram signal data; in this embodiment, the brain wave signal acquisition processing module adopts a TGAM module of NeuroSky science and technology, and the sampling frequency of the module is 512 Hz. The module uses 3 electrodes to collect electroencephalogram data, wherein two electrodes are respectively attached to the protruding positions of the left ear and the right ear and serve as reference electrodes, so that the two electrodes can conveniently and synchronously detect electroencephalogram signals and carry out weighted average, the quality of the signals can be adjusted, and the collected signals are more accurate; the other electrode is used near the forehead of the eye and is used as an electrode for signal acquisition, and because the electroencephalogram forehead belongs to a mental control area, the acquisition of electroencephalogram signals is carried out at the mental control area; because the electroencephalogram signal is weak, the amplitude of the electroencephalogram signal is usually within 100uv, the electroencephalogram signal is easily influenced by self signals such as electrooculogram, skin electricity, body temperature and the like, and the frequency of the electroencephalogram signal is between 1Hz and 100Hz, so that the electroencephalogram signal is easily influenced by power frequency interference. In general, the acquired electroencephalogram signal is usually accompanied by a noisy weak signal, and in order to analyze the signal, denoising and amplification processing must be performed first. Two-stage filtering and amplifying circuits are arranged in the TGAM module, and filtering of 50Hz power frequency interference signals and amplification of collected electroencephalogram data can be achieved. And finally, observing the acquired data of the TGAM through an upper computer, then, effectively processing the electroencephalogram signals, extracting the signals from complex noise, and amplifying the signals to the size required by the system through circuit modulation.
The analog-to-digital conversion module is connected with the brain wave signal acquisition and processing module and is used for converting the amplified electroencephalogram signals into digital signals; the signals passing through the pre-stage amplifying circuit and the subsequent signal conditioning circuit are electroencephalogram analog signals without noise interference, and the signals need to be processed and analyzed, and need to be subjected to analog-to-digital conversion and then are analyzed after being converted into digital signals.
The system comprises an upper computer, an electroencephalogram signal acquisition module and a power supply module, wherein the upper computer is connected with the electroencephalogram signal acquisition module through a Bluetooth module, a virtual reality game developed by Unity3D is installed in the upper computer, and the upper computer is used for processing electroencephalogram signals into concentration values and coding the obtained concentration values to represent different states of a user, as shown in table 1;
TABLE 1
| Encoding | Concentration degree value | Description of the case |
| A1 | 1-20 | The attention is not concentrated very much |
| A2 | 21-40 | Attention is less concentrated |
| A3 | 41-60 | Normal attention |
| A4 | 61-80 | Attention is focused on |
| A5 | 81-100 | Attention is very focused |
Because the precision of the data measured by the TGAM module has certain error, the deviation of the system operation can be caused by adopting the instantaneous value to control the game process, the concentration data is processed by adopting a normalized weighted average algorithm, and the specific steps are as follows:
grouping the concentration degree value and the relaxation degree value of the front 3s after the feedback processing, respectively weighting, and sampling in 3s to obtain a group of data lambdaiThe data length is N, i.e. i belongs to [1, N ∈]. Normalized weighted mean method, weighted value of
i∈[1,N]。
(a) By an array λi，i∈[1,N]The average of the concentration values of the first 3s can be obtained
(b) Calculating each concentration value lambdaiRelative to the mean value
Deviation value of (a) Δ λi：
(c) Deviation value delta lambdaiCarry-in weight function
Is normalized to obtain
i∈[1,N]：
(d) Deriving weight values from normalized deviation values
i∈[1,N]：
(e) The final average value is obtained from the weighted value
Namely:
(f) obtaining the normalized weighted average value of the power looseness numerical value in 3s in the same way
(g) Finally, averaging the two values to obtain a concentration degree value A of the system control character:
the BCI hardware system is communicated with an upper computer through Bluetooth, the computer system loads a thinGear SDK facing NET to transmit information, the computer system is connected with the BCI hardware system, the obtained concentration degree value A is transmitted to Unity3D, a role and a scene model required by a game are modeled and manufactured by 3D Max, picture materials such as pictures are drawn by Photoshop, and animation design and realization are completed by utilizing Maya. The manufactured model is imported into a Unity material library in an FBX file format, a Unity3D engine is used for game development, and scene design and optimization are performed, wherein the game logic design adopts multitask and feedback mechanism design. The interactive function is realized by writing C # through Visual Studio, the immersive experience of the game is realized through HTC Visual equipment, and as shown in FIG. 2, the game is a cool game designed for the embodiment, the game controls the movement of the character in the scene according to the concentration value, and the starting of the game and the advancing direction of the character are controlled through the range of the concentration value. Concentration threshold is set as follows: when the concentration value is between 0 and 40, the game is not started; when the concentration value is greater than 40, the game is started. When the concentration value is 40-60, the player advances on both sides of the center of the runway, and when the concentration value reaches 60-100, the game character runs in the center of the runway, and the gold coins at the center of the runway can be collected. The user must keep his attention above 40 to keep the game character from dying, the higher the concentration, the closer the direction of the player's progress is to the center, and the faster the player character is. When the concentration degree is less than 40, the game is ended, and the system displays the score condition of the user.
To gain a quality of the user's attention, we set the task of collecting the coins, obtaining behavioural data by collecting the number of coins placed in the centre of the runway. The content of the task is: when playing games, a user needs to keep high attention to ensure that the center of the character runway advances and collects gold coins in the center of the runway. The higher the number of coins, the higher the concentration quality. The collection of the gold coin task and the crunch task constitute a multi-tasking of the system. At the end of the game, the user can obtain the game time and the amount of the collected gold coins.
The technical solution of the present invention is not limited to the limitations of the above specific embodiments, and all technical modifications made according to the technical solution of the present invention fall within the protection scope of the present invention.
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