CN103116405A - Real-time detection and control device and method for brain and muscle electricity in tooth movement states - Google Patents
Real-time detection and control device and method for brain and muscle electricity in tooth movement states Download PDFInfo
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Abstract
The invention relates to a real-time detection and control device and a method for brain and muscle electricity in a plurality of states produced by a tooth movement. By using a signal collection device, the brain and muscle electricity signals produced by the tooth movement states are collected, and the collected signals are processed and converted into various control orders. According to the real-time detection and control device and the method for the brain and muscle electricity in the tooth movement states, by using a simplified collection system, and combining a real-time and high-efficiency algorithm, the different movement states of the tooth can be distinguished effectively. Moreover, the different movement states of the tooth can serve as the various control orders, so that the practicality of the real-time detection and control device and the method for the brain and muscle electricity in the tooth movement states is extensive. Due to the fact the movement of the tooth is rapid and covert, the orders obtained by the detection and control device and the method are direct, reliable, real time, accurate and abundant. The real-time detection and control device and the method for the brain and muscle electricity in the tooth movement states can be widely applied to the fields such as industry, business, communication, transportation, medical treatment, education, military affairs and entertainment and have a very broad range of practicality and application popularization prospect.
Description
Technical field
The invention belongs to cognitive and intelligence computation field, particularly field of human-computer interaction specifically refers to a kind of method of real-time detection of brain myoelectricity of various states of tooth action.
Background technology
At present, known field of human-computer interaction, human-computer interactive control means comprise craft (containing the trick action), voice, the mutual control mode of idea.The manual interaction control mode is main flow, is widely used and maturation; The mutual control mode of speech recognition is also perfect at development; The mutual control mode of idea also is in experimental study to the primary stage of practicality transformation.
Known, it is very ripe that manual interaction is controlled, but taken by the control task in trick or the situation such as trick deformity, just in the urgent need to additional other mutual control devices.State is fuzzy, identification is difficult, accuracy is not high because individual difference, outside noise etc. causes for the speech recognition controlled means, although through years of researches and popularization, still can't satisfy practical application request.Idea is compared the control Study of recognition of voice, also is in very elementary conceptual phase.At present, the mutual control device of idea, perhaps the identification maneuver state is single, accuracy is not high (such as the motion imagination), and perhaps accuracy is high, recognition time is long (such as P300, i.e. after lower 300 milliseconds of visual stimulus, eeg signal is strengthened).Outside the manual interaction control device, voice, the mutual control mode of idea can not realize the various states of crucial occasion, reliable, control in real time.
Known, to the understanding of idea control mode, specifically see also with Publication about Document:
Gerwin Schalk(is beautiful), Jurgen.Mellinger(is beautiful) work, Hu Sanqing translates, " BCI2000 and brain-computer interface ", National Defense Industry Press in June, 2011.
Summary of the invention
The objective of the invention is to overcome above-mentioned shortcoming of the prior art, a kind of signal characteristic is obvious, real-time, reliability is high, data are processed easy, low-cost, as to be convenient to extensively popularization tooth action various states real-time detection control device and method are provided.
The technical solution adopted for the present invention to solve the technical problems is:
The detection control apparatus of the brain myoelectricity of this tooth operating state, comprise acquisition module (1) and control module (2), be characterized in, also comprise the sensing module (3) of the brain electromyographic signal of tooth operating state in described device, the output terminal of described sensing module is connected with the input end of acquisition module.
The sensing module of this device comprises reference electrode, C3 and C4 electrode.
According to the signal characteristic of tooth action, utilize respectively neural network and variable window method to identify the different conditions of tooth action.Tooth action can be divided into that tooth moves up and down, tangential movement and aggregate motion.Tooth moves up and down, referred to as gritting one's teeth; The tooth tangential movement is referred to as grinding one's teeth in sleep; The set of teeth resultant motion is ground one's teeth in sleep referred to as stinging.According to the signal characteristic of tooth action, produce discernible direct command a lot (such as 12 kinds of the actions of gritting one's teeth), compound command is even more.
This real-time detection control device to the brain myoelectricity of above-mentioned tooth operating state detects the method for control, and its principal feature is that described method comprises the following steps:
Step (1) gathers the brain electromyographic signal that the tooth action produces.Can adopt the obvious acquisition electrode of several relevant signal intensities (such as, according to international 10-20 normal electrode compartment system, grit one's teeth and adopt 3 electrodes: reference electrode, C3 and C4) collecting device, thereby reduce data processing amount, improve the efficient of the real-time Processing Algorithm of data.
The signal analysis of step (2) tooth action.Concrete steps are as follows:
A. at first carry out bandpass filtering treatment for the data that gather, remove the signal base line drift;
B. locate the action of clock signal Tooth and produce the position of signal, and extract this signal;
C. the signal that quantize to extract, and utilize neural network to carry out proper classification is such as, the active region of the gritting one's teeth three kinds of states that are divided into that gritting one's teeth in the left side, grits one's teeth in the right, the right and left is grited one's teeth simultaneously; The active region of grinding one's teeth in sleep is divided into that grinding one's teeth in sleep in the left side, grinds one's teeth in sleep in the right, grind one's teeth in sleep in the centre three kinds of states;
D. analyze the time-domain signal duration, such as, when gritting one's teeth action, if duration surpasses 1 second, expression length is stung state; If duration is no more than 1 second, utilize the variable window method, initialization number of windows N counts 1, adjusts gradually length of window, if length of window greater than threshold value T, N adds 1 automatically; After traveling through the whole clock signal of gritting one's teeth, if N is 1, expression is stung 1 time, if N is 2, expression has been stung 2 times, if N is 3, expression has been stung 3 times.Grind one's teeth in sleep, sting the Incident Duration Analysis step of the action of grinding one's teeth in sleep and grit one's teeth action roughly the same.
Step (3) is integrated the classification results of above-mentioned steps c and d, can distinguish different tooth operating states.Such as, the action of gritting one's teeth specifically can be divided into: sting once on a left side, a left side stings that twice, a left side sting that three times, left length are stung, sting once on the right side, the right side stings that twice, the right side sting that three times, right length are stung, sting once simultaneously the left and right, the left and right is stung simultaneously twice, left and right and stung simultaneously three times, left and right and sting with duration.Grinding one's teeth in sleep, sting the operating state of the action of grinding one's teeth in sleep distinguishes step and grits one's teeth action roughly the same.
Order completing steps (1), step (2), step (3) can realize the real-time detection of the brain electromyographic signal that action produces to the various states tooth.
Adopt the real-time detection control device method of the brain myoelectricity of tooth operating state of the present invention, due to its tooth be swift in motion, hidden, the control command that obtains is direct, reliable, real-time, accurate, abundant, can be used as effectively replenishing outside the hand-guided mode, easily obtain the approval of the public, manufacturer, user, government, military affairs, can be widely used in the fields such as industry, business, communication, traffic, medical treatment, education, military affairs, amusement, have boundless practicality and application prospect.
Description of drawings
Fig. 1 tooth action (to grit one's teeth as example) signals collecting schematic diagram
Fig. 2 tooth action (to grit one's teeth as example) signal analysis and processing process flow diagram
Fig. 3 stings on a left side oscillogram one time
Fig. 4 stings on a left side oscillogram twice
Fig. 5 stings on a left side tertiary wave shape figure
The left length of Fig. 6 is stung oscillogram
Fig. 7 stings on the right side and once stings oscillogram
Fig. 8 stings on the right side oscillogram twice
Fig. 9 stings on the right side tertiary wave shape figure
The right length of Figure 10 is stung oscillogram
Figure 11 stings oscillogram one time in the left and right simultaneously
Figure 12 stings oscillogram twice in the left and right simultaneously
Figure 13 stings the left and right tertiary wave shape figure simultaneously
Figure 14 stings oscillogram with duration in the left and right
Embodiment
In order to make having further understanding in the present invention, hereinafter coordinate relevant enforcement and accompanying drawing to elaborate:
Tooth actuating signal harvester of the present invention as shown in Figure 1.The main signal electrode of this device comprises: a reference electrode (11), a C3 brain electrode (12), a C4 brain electrode (13) and play fixation and the adjustable support (14) of size.Electrode C3 and electrode C4 are distributed in the brain left and right sides, the autonomous brain signal that produces of acquisition and recording.Reference electrode 11 is connected with the left ear of human body, thereby gets rid of user's static with it and the interference of power frequency (50HZ).Support 14 mainly plays the fixed electorde effect, due to everyone head different sizes, adopts adjustable way that harvester is worn rationally.According to the collection analysis result, also can screen the strong electrode of other signal characteristics and do main electrode.
Enumerate different tooth action control modes, concrete (to grit one's teeth as example) is as follows:
S11 represents that the left side stings tooth one time, and the signal of collection as shown in Figure 3.
S12 represents that the left side stings tooth twice, and twice interval of gritting one's teeth can not be long, stings once otherwise can be detected as two left sides, and its signal as shown in Figure 4.
S13 represents that the left side grits one's teeth three times, and the interval of at every turn gritting one's teeth also can not be long, but total time can not surpass 1 second, its signal is as shown in Figure 5.
S14 represents that left side length stings, and long stinging requires the duration to surpass 1 second, and its signal as shown in Figure 6.
S15 represents that the right and left stings once simultaneously, and its signal as shown in figure 11.
S16 represent the right and left sting simultaneously twice, twice grit one's teeth the interval can not be long, its signal is as shown in figure 12.
S17 represents that the right and left stings three times simultaneously, and its signal as shown in figure 13.
S18 represents that the right and left stings with duration, and its signal as shown in figure 14.
Sting tooth one time on the right of S19 represents, its signal as shown in Figure 7.
S20 represents that the right stings tooth twice, and the interval between gritting one's teeth for twice can not be long, and its signal as shown in Figure 8.
Sting tooth three times on the right of S21 represents, total time of gritting one's teeth is no more than 1 second, and its signal as shown in Figure 9.
Long gritting one's teeth on the right of S22 represents, the duration of gritting one's teeth required more than 1 second, and its signal is as shown in figure 10.
As shown in Figure 2, be signal processing flow figure of the present invention, its basic procedure is as follows:
Step (1) is utilized signal pickup assembly shown in Figure 1, gathers one section clock signal.Signal is carried out pre-service, and this process mainly comprises: signal filtering keeps frequency range interested; Remove signal drift.These methods can be cured in hardware, thereby improve the efficient that data are processed.
Step (2) setting threshold T1, and traversal clock signal extract signal amplitude greater than the signal segment of threshold value T1.This segment signal is exactly the signal of gritting one's teeth and producing so, in order to determine that this signal is to belong to any signal of gritting one's teeth, adopts neural network and variable window length method that signal is classified.Because the two sorting algorithm is relatively independent, can adopt parallel design method, accelerate algorithm process efficient.
Step (3) quantizes the signal that extracts, and the signal after quantizing is classified by neural network.Neural network needs priori to train, and obtains a disaggregated model.Its training process is as follows:
A. build Artificial Neural Network Structures, and structural parameters are carried out initialization.
B. gather training data, training data has 3 classes: grit one's teeth in the left side, gritting one's teeth in the right, grits one's teeth simultaneously in the left and right.
C. utilize training data dynamically to adjust Parameters of Neural Network Structure, and make error lower than desired value, utilize training data to detect the accuracy rate of classification results more than 99%.
At last, utilize the neural network model after training that the signal of gritting one's teeth is classified, can obtain: gritting one's teeth in the left side, grits one's teeth in the right and grit one's teeth simultaneously in the left and right these classification results.Neural network model also has the adaptive learning ability simultaneously, thereby makes classification results more accurate.
Whether step (4) judges the clock signal duration over 1 second, and if so, this signal is exactly a long signal of stinging.This judged result is combined with the classification results of neural network model can obtains corresponding category signal.
Otherwise utilize the variable window method that signal is classified, its process is as follows:
A. initialization window, and window enumeration device N is set is 1, and window threshold value T is set.
B. travel through time sequence window, and increase gradually the length of window, if length of window greater than threshold value T, window enumeration device N adds 1, otherwise continues cycling among windows, until travel through complete time sequence window.
The number of times of c. gritting one's teeth according to the value judgement of N, if N is 1, tooth is stung in expression 1 time, if N is 2, tooth has been stung in expression 2 times, if N is 3, tooth has been stung in expression 3 times.
At last, the result of gritting one's teeth is combined with the neural network classification result, can obtain the category signal of gritting one's teeth accordingly.The collection analysis step of other tooth actions is the same, repeats no more.
In sum, the invention provides real-time detection control device and the method for the brain electromyographic signal of the various states that a kind of tooth produces, choose main electrode by the signal pickup assembly of personalized design, reduce the data processing amount of signal, the tooth actuating signal is stablized and satisfies real-time simultaneously, is applicable in daily life and various fields.
In this instructions, the present invention is described with reference to its specific embodiment.But, still can make various modifications and conversion obviously and not deviate from the spirit and scope of the present invention.Therefore, instructions and accompanying drawing are regarded in an illustrative, rather than a restrictive.
Claims (5)
1. the detection control apparatus of the brain myoelectricity of a tooth operating state, comprise acquisition module (1) and control module (2), it is characterized in that, also comprise the sensing module (3) of the brain electromyographic signal of tooth operating state in described device, the output terminal of described sensing module is connected with the input end of acquisition module.
2. the detection control apparatus of the brain myoelectricity of tooth operating state according to claim 1, is characterized in that, described sensing module comprises reference electrode, C3 and C4 electrode.
3. the real-time detection control device to the brain myoelectricity of tooth operating state claimed in claim 1 detects the method for control, it is characterized in that, described method comprises the following steps:
(1) gather the brain electromyographic signal that the tooth operating state produces;
(2) collection signal is processed be converted to control command.
4. the method for the real-time detection of the brain myoelectricity of the various states that action produces to tooth according to claim 3, is characterized in that, the brain electromyographic signal that described collection tooth operating state produces comprises the following steps:
(1) the tooth operating state be divided into that tooth moves up and down, tangential movement and aggregate motion; Tooth moves up and down, referred to as gritting one's teeth; The tooth tangential movement is referred to as grinding one's teeth in sleep; The set of teeth resultant motion is ground one's teeth in sleep referred to as stinging;
(2) opsition dependent of gritting one's teeth is divided into the left side, the right, centre; The opsition dependent of grinding one's teeth in sleep is divided into the left side, the right, middle limit; Gritting one's teeth, grind one's teeth in sleep, sting the mixing of grinding one's teeth in sleep uses and is assembled state;
(3) brain electromyographic signal collection electrode is installed, international 10-20 normal electrode system distribution plan is adopted in the installation site of brain electrode;
(4) according to signal strength characteristics, choose relevant acquisition electrode, reduce electrode number, farthest system is simplified;
(5) the grit one's teeth related brain electrode of operating state is reference electrode, C3 and C4 electrode.
5. the method for the real-time detection of the brain myoelectricity of the various states that action produces to tooth according to claim 3, is characterized in that, described collection signal is processed is converted to control command, comprises the following steps:
(1) adopt bandpass filtering to keep the signal of 40 ~ 100Hz frequency range, remove baseline wander;
(2) different conditions and the number of times that utilize respectively neural network and variable window method to identify the tooth action are control command.
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN104317388A (en) * | 2014-09-15 | 2015-01-28 | 联想(北京)有限公司 | Interaction method and wearable electronic equipment |
| WO2015196353A1 (en) * | 2014-06-24 | 2015-12-30 | 陈梓铭 | Control system and method for converting acoustical signal produced by teeth into electrical signal |
| CN105988569A (en) * | 2015-02-13 | 2016-10-05 | 北京智谷睿拓技术服务有限公司 | Method and device for determining control information |
| CN105988570A (en) * | 2015-02-13 | 2016-10-05 | 北京智谷睿拓技术服务有限公司 | Method and device for determining control information |
| CN106227354A (en) * | 2016-08-31 | 2016-12-14 | 博睿康科技（常州）股份有限公司 | A kind of brain-machine interaction donning system |
| CN106371563A (en) * | 2015-08-19 | 2017-02-01 | 北京智谷睿拓技术服务有限公司 | Blowing/sucking determination method and equipment |
| WO2018218967A1 (en) * | 2017-06-02 | 2018-12-06 | 京东方科技集团股份有限公司 | Dental prosthesis apparatus and operating method thereof, terminal, and signal interaction system |
| CN110051351A (en) * | 2019-03-28 | 2019-07-26 | 深圳市宏智力科技有限公司 | It grits one's teeth the control method and device of signal acquiring method and electronic equipment |
| CN111050248A (en) * | 2020-01-14 | 2020-04-21 | Oppo广东移动通信有限公司 | Wireless earphone and control method thereof |
| CN111248915A (en) * | 2019-12-30 | 2020-06-09 | 联想(北京)有限公司 | Processing method and device and electronic equipment |
| CN117170492A (en) * | 2022-05-27 | 2023-12-05 | 林诚皓 | Input system and method based on natural language processing and computer vision |
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Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
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| WO2018218967A1 (en) * | 2017-06-02 | 2018-12-06 | 京东方科技集团股份有限公司 | Dental prosthesis apparatus and operating method thereof, terminal, and signal interaction system |
| CN110051351A (en) * | 2019-03-28 | 2019-07-26 | 深圳市宏智力科技有限公司 | It grits one's teeth the control method and device of signal acquiring method and electronic equipment |
| CN111248915A (en) * | 2019-12-30 | 2020-06-09 | 联想(北京)有限公司 | Processing method and device and electronic equipment |
| CN111050248A (en) * | 2020-01-14 | 2020-04-21 | Oppo广东移动通信有限公司 | Wireless earphone and control method thereof |
| CN111050248B (en) * | 2020-01-14 | 2021-10-01 | Oppo广东移动通信有限公司 | Wireless earphone and control method thereof |
| CN117170492A (en) * | 2022-05-27 | 2023-12-05 | 林诚皓 | Input system and method based on natural language processing and computer vision |
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