CN110916652A - Data acquisition device and method for controlling robot movement based on motor imagery through electroencephalogram and application of data acquisition device and method - Google Patents
Data acquisition device and method for controlling robot movement based on motor imagery through electroencephalogram and application of data acquisition device and method Download PDFInfo
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Abstract
The invention discloses a data acquisition device and method for controlling robot movement by electroencephalogram based on motor imagery and application of the data acquisition device and method, and belongs to the field of application research of human-computer interaction. The invention is an electroencephalogram control mobile robot based on motor imagery, so the robot can be directly used without being tested for special training; the head-wearing electroencephalogram collection cap is adopted, belongs to a non-invasive non-implanted electroencephalogram cap, does not cause any damage to a human body, and improves the adaptability to the environment by adopting a Bluetooth transmission mode; in addition, at present, most of control instructions based on the motor imagery electroencephalogram BCI are relatively few, but the invention adopts imagination of left-hand and right-hand movement and adds various control instructions such as imagination of tongue and leg movement and the like, so that the instructions are richer and the application is wider; meanwhile, the invention adopts an integrated learning method to classify and identify the electroencephalogram signals, and has high identification accuracy and good effect.
Description
Technical Field
The invention relates to a data acquisition device and method for controlling robot movement by electroencephalogram based on motor imagery and application thereof, belonging to the field of application research of human-computer interaction.
Background
The Brain-Computer Interface (BCI) is a novel human-Computer interaction mode, which does not depend on peripheral nerves and muscles for transmission, but directly builds a channel for communication and interaction between the Brain and the outside world through a Computer, and is more and more concerned by researchers at home and abroad. The brain communication method has the main task of deducing the idea and the purpose of a person through electroencephalogram signal analysis, so that the brain is communicated with the outside. It appears to many patients, such as: stroke, cerebral palsy, amyotrophic lateral sclerosis, etc. bring good news. Such patients usually cannot control their limbs through the brain, lose basic ability to communicate and communicate with the outside, and even cannot live independently, which brings great stress and burden to the family and society. But in general, they have clear thinking and the brain can think independently. Based on the point, the brain-computer interface can provide a brand-new way for the motor dysfunction patients to communicate with the outside, help the patients to deal with basic daily life, improve the quality of life of the patients and enable the patients to regain confidence in life.
According to the difference of the brain electrical evoked potential signals, the brain-computer interface can be roughly divided into the following two categories: a self-initiated brain-computer interface and an evoked brain-computer interface. The spontaneous brain-computer interface is characterized in that the brain generates a stimulation signal without external stimulation, and a typical representative of the spontaneous brain-computer interface system is a brain-computer interface system based on motor imagery; the induction type brain-computer interface refers to the fact that some external stimulation is needed for the generation of electroencephalogram signals, typical stimulation signals such as visual stimulation, auditory stimulation and sensory stimulation are included in the induction type brain-computer interface: p300, event related point, etc.
Motor imagery brain electricity is the most important and most widely applied BCI in brain-computer interface technology, and realizes control of external equipment by taking motor consciousness as an instruction. For example, the control of a wheelchair, a rehabilitation robot, and the like can realize the application of the motor imagery BCI in the medical field.
At present, brain-computer interfaces have become a research hotspot at home and abroad, more and more scientific research resources and scientific research talents are put into the field, and expert scholars from the United states, Germany, Japan, China and the like are mainly converged. It is estimated that there are over hundreds of research groups engaged in the related research of brain-computer interface at home and abroad, and under the research and communication of the groups, the brain-computer interface has achieved great research success, which greatly promotes the development of brain-computer interface technology and the development of related disciplines.
Disclosure of Invention
The invention provides a data acquisition device and a data acquisition method for electroencephalogram control of robot movement based on motor imagery, which are used for realizing data acquisition for electroencephalogram control of robot movement based on motor imagery through the device, and provides application of the data acquisition method for electroencephalogram control of robot movement based on motor imagery, which is used for realizing interaction of electroencephalogram signals and an external robot and realizing control of the movement of the robot.
The technical scheme of the invention is as follows: a data acquisition device for controlling robot movement by electroencephalogram based on motor imagery comprises a head-wearing electroencephalogram acquisition cap and a computer, wherein an electroencephalogram signal preprocessing module, a signal conversion module and an electroencephalogram signal characteristic processing module are arranged in the computer;
the head-wearing electroencephalogram acquisition cap sends acquired electroencephalogram signals of a user to a computer, the electroencephalogram signals are amplified, filtered and analyzed and processed by an electroencephalogram signal preprocessing module in the computer, then the processed electroencephalogram signals are transmitted to a signal conversion module to be converted into digital signals, the digital signals are extracted by an electroencephalogram signal feature processing module, feature vectors related to motor imagery are converted into corresponding control commands through mode classification, and the control commands serve as acquisition data of a data acquisition device to be transmitted to a robot.
A data acquisition method for controlling robot movement based on motor imagery electroencephalogram comprises the following steps:
s1, electroencephalogram signal acquisition: a subject acquires n electroencephalogram signals through electroencephalogram acquisition equipment;
s2, signal preprocessing:
s2.1, amplifying the acquired microvolt-level original electroencephalogram signals through an electroencephalogram signal amplifier to obtain millivolt-level electroencephalogram signals;
s2.2, filtering the millivolt-level electroencephalogram signals through a filter; the filter uses a band-pass filter to carry out 8-30HZ band-pass filtering, and the calculation formula of the bandwidth BW of the pass band is as follows: BW ═ omega2-ω1，ω2For high pass cut-off angular frequency, omega1Is the low-pass cut-off angular frequency;
s2.3, carrying out independent component analysis on the filtered signals;
s3, converting the signal obtained in the step S2 into a digital signal; then, extracting the characteristics of the electroencephalogram signals by using a Hilbert-Huang transform method for the converted signals; then, classification and identification are carried out by adopting ensemble learning; wherein, the ensemble learning specifically adopts a Bagging algorithm.
In S1, 32 electroencephalogram signals are acquired.
The data acquisition method for controlling the movement of the robot by the electroencephalogram based on the motor imagery is used for controlling the movement of the robot, and a control command is sent according to a classification recognition result for controlling the movement of the robot, wherein the specific matching mode is as follows: the subject imagines that the limbs do corresponding movement, imagines that the right hand moves, and then the recognition result displays R on the computer interface to drive the robot to move rightwards; imagine the left hand movement, the recognition result displays L on the computer interface, drive the robot to move to the left; imagining tongue movement, displaying an identification result F on a computer interface, and driving the robot to move forwards; imagining leg movement, displaying a recognition result B on a computer interface, and driving the robot to move backwards; without any imagination, the robot is stopped.
The invention has the beneficial effects that: the invention is an electroencephalogram control mobile robot based on motor imagery, so the robot can be directly used without being tested for special training; the head-wearing electroencephalogram collection cap is adopted, belongs to a non-invasive non-implanted electroencephalogram cap, does not cause any damage to a human body, and improves the adaptability to the environment by adopting a Bluetooth transmission mode; in addition, at present, most of control instructions based on the motor imagery electroencephalogram BCI are relatively few, but the invention adopts imagination of left-hand and right-hand movement and adds various control instructions such as imagination of tongue and leg movement and the like, so that the instructions are richer and the application is wider; meanwhile, the invention adopts an integrated learning method to classify and identify the electroencephalogram signals, and has high identification accuracy and good effect.
Drawings
FIG. 1 is a schematic diagram of the BCI system of the present invention;
FIG. 2 is a schematic diagram of an EEG signal acquisition area according to the present invention;
fig. 3 is a visual stimulus working interface of the present invention.
Detailed Description
Example 1: as shown in fig. 1-3, a data acquisition device for controlling robot movement based on motor imagery electroencephalogram comprises a head-wearing electroencephalogram acquisition cap and a computer, wherein the computer is provided with an electroencephalogram signal preprocessing module, a signal conversion module and an electroencephalogram signal characteristic processing module;
the head-wearing electroencephalogram acquisition cap sends acquired electroencephalogram signals of a user to a computer, the electroencephalogram signals are amplified, filtered and analyzed and processed by an electroencephalogram signal preprocessing module in the computer, then the processed electroencephalogram signals are transmitted to a signal conversion module to be converted into digital signals, the digital signals are extracted by an electroencephalogram signal feature processing module, feature vectors related to motor imagery are converted into corresponding control commands through mode classification, and the control commands serve as acquisition data of a data acquisition device to be transmitted to a robot.
A data acquisition method for controlling robot movement based on motor imagery electroencephalogram comprises the following steps:
s1, electroencephalogram signal acquisition: a subject acquires n electroencephalogram signals through electroencephalogram acquisition equipment;
s2, signal preprocessing:
s2.1, amplifying the acquired microvolt-level original electroencephalogram signals through an electroencephalogram signal amplifier to obtain millivolt-level electroencephalogram signals;
s2.2, filtering the millivolt-level electroencephalogram signals through a filter; the filter uses a band-pass filter to carry out 8-30HZ band-pass filtering, and the calculation formula of the bandwidth BW of the pass band is as follows: BW ═ omega2-ω1，ω2For high pass cut-off angular frequency, omega1Is the low-pass cut-off angular frequency;
s2.3, carrying out independent component analysis on the filtered signals;
the electroencephalogram signals are amplified so as to be convenient to process, then the electroencephalogram signals are subjected to filtering and independent component analysis, and the electroencephalogram signals are matched together for double processing, so that the defects that the electroencephalogram signals are extremely weak and a large amount of interference signals and artifacts are mixed due to motor imagery are overcome, and the purposes of denoising, removing eye electricity, myoelectricity, power frequency interference and the like are achieved;
s3, converting the signal obtained in the step S2 into a digital signal; then, extracting the characteristics of the electroencephalogram signals by using a Hilbert-Huang transform method for the converted signals; then, classification and identification are carried out by adopting ensemble learning; wherein, the ensemble learning specifically adopts a Bagging algorithm.
Most important for the extraction of the Hilbert-Huang transform characteristics is Empirical Mode Decomposition (EMD), and the EMD analysis of the data aims to obtain an Intrinsic Mode Function (IMF), and the method comprises the following specific steps:
(1) solving all local maximum value points and minimum value points of an original data sequence x (t), namely an input signal;
(2) fitting the maximum value point and the minimum value point obtained in the step (1) by adopting a cubic spline difference function to form an upper envelope line and a lower envelope line of the original signal;
(3) the average value ml of the upper envelope line and the lower envelope line is subtracted by the original signal to obtain h;
(4) then judging whether the difference h meets the condition of the intrinsic mode function, and if so, taking h as a first IMF; otherwise, the above operations are carried out until the IMF is solved, the difference r between the original signal and the IMF is solved, and finally the r is taken as the signal to be processed until the r is the monotone signal.
The invention adopts Bagging as an integrated learning method, which is a voting classification mechanism. The ensemble learning is a powerful technique capable of improving accuracy in various machine learning tasks, and completes the learning task by combining a plurality of base classifiers. The base classifier generally adopts weak learnable classifiers, and is combined into a strong learnable classifier through integrated learning. The weak learnable means that the learning accuracy is only slightly better than the randomly guessed polynomial learning algorithm; the strongly learnable refers to a polynomial learning algorithm with a higher accuracy. The generalization capability of ensemble learning is generally better than that of a single base classifier because most base classifiers have a much lower probability of classification error than single base classifiers.
The main steps and strategies are as follows:
1. resampling (with repetition) n samples from the sample set;
2. on the attribute of all, establishing classifiers (such as ID3, C4.5, CART and the like) for the n samples;
3. repeating or iterating for 1, 2.. m times to obtain m classifiers;
4. and (4) putting the data on the m classifiers, and finally determining which type the original data belong to according to the voting results of the m classifiers.
Further, in S1, 32 electroencephalogram signals may be acquired.
The data acquisition method for controlling the movement of the robot by the electroencephalogram based on the motor imagery is used for controlling the movement of the robot, and a control command is sent according to a classification recognition result for controlling the movement of the robot, wherein the specific matching mode is as follows: the subject imagines that the limbs do corresponding movement, imagines that the right hand moves, and then the recognition result displays R on the computer interface to drive the robot to move rightwards; imagine the left hand movement, the recognition result displays L on the computer interface, drive the robot to move to the left; imagining tongue movement, displaying an identification result F on a computer interface, and driving the robot to move forwards; imagining leg movement, displaying a recognition result B on a computer interface, and driving the robot to move backwards; without any imagination, the robot is stopped.
For example, when a right hand movement is imagined, the nerve electrical activity of the lateral motor cortex C4 area of the brain is significantly enhanced, the peak values of the α and β frequency bands are reduced, which is called event-related desynchronization (ERD), and the amplitudes of the α and β frequency bands of the ipsilateral cerebral motor cortex are increased, which is called event-related synchrony (ERS), and similarly, when a left hand movement is imagined, the nerve electrical activity of the lateral motor cortex C3 area of the brain is significantly enhanced, the peak values of the α and β frequency bands are reduced, and the amplitudes of the α and β frequency bands of the ipsilateral cerebral motor cortex are increased, and in addition, the phenomena of tongue and foot movement ERD/ERS appear in the temporal lobe and the apical lobe, respectively, and in the approximate cortex of the movement Cz and CP6, as shown in the diagram of fig. 2.
While the present invention has been described in detail with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, and various changes can be made without departing from the spirit of the present invention within the knowledge of those skilled in the art.
Claims (4)
1. The utility model provides a data acquisition device that is used for brain electricity control robot based on motor imagery to remove which characterized in that:
the electroencephalogram acquisition system comprises a head-wearing electroencephalogram acquisition cap and a computer, wherein an electroencephalogram signal preprocessing module, a signal conversion module and an electroencephalogram signal characteristic processing module are arranged in the computer;
the head-wearing electroencephalogram acquisition cap sends acquired electroencephalogram signals of a user to a computer, the electroencephalogram signals are amplified, filtered and analyzed and processed by an electroencephalogram signal preprocessing module in the computer, then the processed electroencephalogram signals are transmitted to a signal conversion module to be converted into digital signals, the digital signals are extracted by an electroencephalogram signal feature processing module, feature vectors related to motor imagery are converted into corresponding control commands through mode classification, and the control commands serve as acquisition data of a data acquisition device to be transmitted to a robot.
2. A data acquisition method for controlling robot movement by electroencephalogram based on motor imagery is characterized in that: the method comprises the following steps:
s1, electroencephalogram signal acquisition: a subject acquires n electroencephalogram signals through electroencephalogram acquisition equipment;
s2, signal preprocessing:
s2.1, amplifying the acquired microvolt-level original electroencephalogram signals through an electroencephalogram signal amplifier to obtain millivolt-level electroencephalogram signals;
s2.2, communicating the millivolt-level electroencephalogram signalsThe filter carries out filtering processing; the filter uses a band-pass filter to carry out 8-30HZ band-pass filtering, and the calculation formula of the bandwidth BW of the pass band is as follows: BW ═ omega2-ω1，ω2For high pass cut-off angular frequency, omega1Is the low-pass cut-off angular frequency;
s2.3, carrying out independent component analysis on the filtered signals;
s3, converting the signal obtained in the step S2 into a digital signal; then, extracting the characteristics of the electroencephalogram signals by using a Hilbert-Huang transform method for the converted signals; then, classification and identification are carried out by adopting ensemble learning; wherein, the ensemble learning specifically adopts a Bagging algorithm.
3. The data acquisition method for motor imagery based electroencephalogram control of robot movement according to claim 1, wherein: in S1, 32 electroencephalogram signals are acquired.
4. Use of the data acquisition method according to claim 2 for controlling the movements of a robot, characterized in that: and sending a control command according to the classification recognition result for controlling the robot to move, wherein the specific matching mode is as follows: the subject imagines that the limbs do corresponding movement, imagines that the right hand moves, and then the recognition result displays R on the computer interface to drive the robot to move rightwards; imagine the left hand movement, the recognition result displays L on the computer interface, drive the robot to move to the left; imagining tongue movement, displaying an identification result F on a computer interface, and driving the robot to move forwards; imagining leg movement, displaying a recognition result B on a computer interface, and driving the robot to move backwards; without any imagination, the robot is stopped.
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| CN112016415A (en) * | 2020-08-14 | 2020-12-01 | 安徽大学 | Motor imagery classification method combining ensemble learning and independent component analysis |
| CN112148011A (en) * | 2020-09-24 | 2020-12-29 | 东南大学 | Electroencephalogram mobile robot sharing control method under unknown environment |
| CN112518743A (en) * | 2020-11-20 | 2021-03-19 | 南京航空航天大学 | Multi-mode neural decoding control system and method for on-orbit operation of space manipulator |
| CN112633365A (en) * | 2020-12-21 | 2021-04-09 | 西安理工大学 | Mirror convolution neural network model and motor imagery electroencephalogram recognition algorithm |
| CN112741637A (en) * | 2020-12-23 | 2021-05-04 | 杭州国辰迈联机器人科技有限公司 | P300 electroencephalogram signal extraction method, cognitive rehabilitation training method and system |
| CN113504828A (en) * | 2021-06-28 | 2021-10-15 | 广州大学 | Brain-computer interface control method and system based on language imagery and motor imagery |
| CN111728822B (en) * | 2020-07-24 | 2021-11-26 | 清华大学 | Brain-computer interactive closed-loop rehabilitation robot control method used after brain injury |
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