CN111258428B - Brain electricity control system and method - Google Patents
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Abstract
The disclosure provides an electroencephalogram control system and method, and relates to the technical field of medical rehabilitation. The specific technical scheme is as follows: the electroencephalogram signal processing device is used for acquiring electroencephalogram signals and sensor data of a user; calculating brain participation indexes based on the brain electrical signals; generating a corresponding control instruction according to the brain participation index and a preset brain participation template, and sending the control instruction to the exercise rehabilitation device; the exercise rehabilitation device determines corresponding exercise parameters according to the control instruction and a preset corresponding relation, and operates according to the exercise parameters; acquiring first biological data of a user corresponding to the exercise rehabilitation device, and sending the first biological data to an electroencephalogram signal processing device, wherein the first biological data comprises exercise state data; the brain signal processing device updates a preset brain participation degree template according to the first biological data. The invention is used for more diversifying the mode according to the evoked electroencephalogram signals.
Description
Technical Field
The disclosure relates to the technical field of medical rehabilitation, in particular to an electroencephalogram control system and method.
Background
Rehabilitation training plays a very important role in the recovery process of the exercise function of patients suffering from diseases, such as cerebral apoplexy. The traditional rehabilitation method comprises exercise therapy, massage by therapist and the like, and meanwhile, a rehabilitation robot assisted rehabilitation training method is also developed in recent years.
Most of the existing brain control rehabilitation training systems adopt motor imagery or visual stimulus induction as a main method, and feedback training is assisted by virtual reality scenes or actual rehabilitation exercise equipment. However, the existing brain control rehabilitation technology has the problems of complex configuration of signal acquisition equipment, complex and unstable method and the like, and is not suitable for a rehabilitation training system. For example, patients who need rehabilitation training usually have impaired motor central nervous functions, are difficult to effectively execute motor imagery brain control methods, have low evoked signal intensity, have difficult to effectively extract motor consciousness characteristics, and have poor control feedback effects.
Disclosure of Invention
The embodiment of the disclosure provides an electroencephalogram control system and method, which can enable the mode of inducing electroencephalogram signals to be more diversified. The technical scheme is as follows:
according to a first aspect of embodiments of the present disclosure, there is provided an electroencephalogram control system, the system comprising: an electroencephalogram signal processing device and a sports rehabilitation device,
an electroencephalogram signal processing device for collecting electroencephalogram signals of a user; calculating brain participation indexes based on the brain electrical signals; generating a corresponding control instruction according to the brain participation index and a preset brain participation template, and sending the control instruction to the exercise rehabilitation device; the brain participation degree template stores the corresponding relation between the brain participation degree index and the control instruction;
The exercise rehabilitation device determines corresponding exercise parameters according to the control instruction and a preset corresponding relation, and operates according to the exercise parameters; acquiring first biological data of a user corresponding to the exercise rehabilitation device, and sending the first biological data to an electroencephalogram signal processing device, wherein the first biological data comprises exercise state data;
the brain signal processing device updates a preset brain participation degree template according to the first biological data.
In one embodiment, calculating the brain engagement index based on the brain electrical signal includes:
independent component analysis and noise elimination processing is carried out on the brain electrical signals, blink and low-frequency noise interference are filtered, and preprocessed signals are obtained;
band-pass filtering the pre-processed signal using an infinite impulse response digital filter;
calculating the symbolized sample entropy of each frequency segment after band-pass filtering;
and inputting the symbolized sample entropy into a preset characteristic weighting support vector machine (Support Vector Machine, SVM) classifier, and calculating to obtain the brain participation index.
In one embodiment, the brain electrical control system further comprises an interaction device,
and the interaction device is used for displaying a preset motion stimulation target which is used for stimulating the brain electrical signals.
And the exercise rehabilitation device adjusts corresponding exercise parameters according to the control instruction and the preset corresponding relation.
In one embodiment, the interaction means provides an operation interface for the user, the operation interface receiving at least one of the following parameters entered by the user: training mode grading parameters, motion stimulation target parameters, brain electricity participation index sensitivity parameters and limit parameters of rehabilitation exercise equipment; the size parameter of the moving stimulus target is used for adjusting the size and/or the position of the moving stimulus target; the brain electrical participation index sensitivity parameter is used for determining a preset brain participation template;
the training mode grading parameters are used for indicating different training scenes, and each training scene corresponds to the limit parameters of the sports equipment and the sensitivity parameters of the electroencephalogram participation index;
the kinetic parameters of the rehabilitation exercise device are not greater than the limit parameters of the rehabilitation exercise device.
In one embodiment, second biological data of a user of the exercise rehabilitation device is acquired and sent to the interaction device, the second biological data comprising limb joint movement change track data;
the interaction device analyzes the second biological data and generates corresponding first attribute parameters of the user and/or second attribute parameters of the user;
and updating the display mode corresponding to the motion stimulation target according to the first attribute parameter of the user and/or the second attribute parameter of the user.
In one embodiment, the exercise rehabilitation device outputs an evaluation index parameter and sends the evaluation index parameter to the electroencephalogram signal processing device, and the electroencephalogram signal processing device updates a preset brain engagement template according to the evaluation index parameter, wherein the evaluation index parameter comprises at least one of the following: motion state data, motion power information, brain participation degree, brain electrical participation degree index sensitivity parameters and brain electrical participation degree change curves.
In one embodiment, the exercise rehabilitation device sends the evaluation index parameters to the interaction device, and the interaction device generates and displays an analysis report according to the evaluation index parameters; the electroencephalogram signal processing device includes at least one of: an electroencephalogram head ring, a multi-lead dry electrode electroencephalogram cap, a saline electrode electroencephalogram cap or a multi-lead wet electrode electroencephalogram cap; the interaction means comprises at least one of: liquid crystal display device, virtual reality VR device, augmented reality AR device, 3D formation of image display device.
In one embodiment, before the electroencephalogram signal processing device collects the electroencephalogram signal and the sensor data of the user, the electroencephalogram participation degree index sensitivity parameter input through the interaction device is also obtained, and a corresponding brain participation degree template is determined according to the input electroencephalogram participation degree index sensitivity parameter.
In one embodiment, the electroencephalogram signal processing device further collects sensor data, obtains motion acceleration data according to the sensor data, converts quaternion of the sensor data into Euler angles, and calculates angle data;
inputting the symbolized sample entropy into a preset feature weighted Support Vector Machine (SVM) classifier, and calculating to obtain a brain participation index comprises the following steps:
and inputting the symbolized sample entropy, acceleration data and angle data into a preset feature weighted Support Vector Machine (SVM) classifier, and calculating to obtain a brain participation index.
According to a second aspect of embodiments of the present disclosure, there is provided an electroencephalogram control method including:
an electroencephalogram signal processing device for collecting electroencephalogram signals of a user; calculating brain participation indexes based on the brain electrical signals; generating a corresponding control instruction according to the brain participation index and a preset brain participation template, and sending the control instruction to the exercise rehabilitation device; the brain participation degree template stores the corresponding relation between the brain participation degree index and the control instruction;
the exercise rehabilitation device determines corresponding exercise parameters according to the control instruction and a preset corresponding relation, and operates according to the exercise parameters; acquiring first biological data of a user corresponding to the exercise rehabilitation device, and sending the first biological data to an electroencephalogram signal processing device, wherein the first biological data comprises exercise state data;
The brain signal processing device updates a preset brain participation degree template according to the first biological data.
By adopting the embodiment provided by the disclosure, the active and passive training method is mainly adopted in the training process of the patient, the patient is ensured to actively participate in the treatment process, the motor-sensory nerve conduction path is opened, the connection between the nerve and the muscle is effectively stimulated and enhanced, and the rehabilitation effect is ensured. In addition, the present disclosure also provides a rich interaction stimulation mode, avoiding the patient from being tired very easily, not observing the motor stimulation, not actively participating in the training.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure.
Fig. 1 is a block diagram of an electroencephalogram control system according to an embodiment of the present disclosure;
fig. 2 is a block diagram of an electroencephalogram control system according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of calculating brain engagement indicators provided by embodiments of the present disclosure;
FIG. 4 is a schematic diagram of calculating brain engagement indicators provided by embodiments of the present disclosure;
FIG. 5 is a schematic view of angle data provided by an embodiment of the present disclosure;
FIG. 6 is a flowchart of an electroencephalogram control method provided by an embodiment of the present disclosure;
FIG. 7 is a flowchart of a method for using an electroencephalogram control system according to an embodiment of the present disclosure;
fig. 8 is a schematic diagram of a method for using an electroencephalogram control system according to an embodiment of the present disclosure.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as detailed in the accompanying claims.
Some portions of the description which follows are explicitly or implicitly related to algorithms and functional or symbolic representations of operations on data within a computer memory. These algorithmic descriptions and functional or symbolic representations are the means used by those skilled in the data processing arts to more effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of steps leading to a desired result. The steps are those requiring physical manipulations of physical quantities such as electrical, magnetic or optical signals capable of being stored, transferred, combined, compared, and otherwise manipulated.
Unless specifically stated otherwise, as apparent from the following, it is appreciated that throughout the present specification discussions utilizing terms such as "collecting," "computing," "generating," "transmitting," "obtaining," "updating," or the like, refer to the action and processes of a computer system, or similar electronic device, that manipulates and transforms data represented as physical quantities within the computer system into other data similarly represented as physical quantities within the computer system or other information storage, transmission or display devices.
The specification also discloses apparatus for performing the method operations. Such a device may be specially constructed for the required purposes, or may comprise a general purpose computer or other device selectively activated or reconfigured by a computer program stored in the computer. The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Various general-purpose machines may be used with programs in accordance with the teachings herein. Alternatively, more specific apparatus configurations for performing the required method steps are applicable. The structure of a conventional general-purpose computer will be described in the following description.
Furthermore, the present specification also implicitly discloses a computer program, as the steps of the methods described herein may be implemented by computer code as will be apparent to those skilled in the art. The computer program is not intended to be limited to any particular programming language and its execution. It will be appreciated that a variety of programming languages and codes thereof may be used to implement the teachings of the disclosure as contained herein. Furthermore, the computer program is not intended to be limited to any particular control flow. There are many other kinds of computer programs that can use different control flows without departing from the spirit or scope of the present invention.
Moreover, one or more steps of a computer program may be performed in parallel rather than sequentially. Such a computer program may be stored on any computer readable medium. The computer readable medium may include a storage device such as a magnetic or optical disk, memory chip, or other storage device suitable for interfacing with a general purpose computer, etc. The computer readable medium may also include a hard-wired medium such as in an internet system, or a wireless medium. When the computer program is loaded and executed on such a general purpose computer, the computer program effectively creates an apparatus that implements the steps of the preferred method.
The invention may also be implemented as a hardware module. More specifically, a module is a functional hardware unit in a hardware sense designed for use with other components or modules. For example, a module may be implemented using discrete electronic components, or it may form part of an overall electronic circuit such as an Application Specific Integrated Circuit (ASIC). Many other possibilities exist. Those skilled in the art will appreciate that the system may also be implemented as a combination of hardware and software modules.
An embodiment of the present disclosure provides an electroencephalogram control system, as shown in fig. 1, including: an electroencephalogram signal processing device 101 and a sports rehabilitation device 102,
an electroencephalogram signal processing device 101 which collects electroencephalogram signals; calculating brain participation indexes based on the brain electrical signals; generating a corresponding control instruction according to the brain participation index and a preset brain participation template, and sending the control instruction to the exercise rehabilitation device; the brain participation degree template stores the corresponding relation between the brain participation degree index and the control instruction;
the exercise rehabilitation device 102 determines corresponding exercise parameters according to a preset corresponding relation according to the control instruction; acquiring biological data of a user of the exercise rehabilitation device, and sending the first biological data to an electroencephalogram signal processing device, wherein the first biological data comprises exercise state data;
The motion state data can be calculated by using angle, speed and acceleration sensors, myoelectric sensors, interaction force sensors or motor current and power changes. For example, the exercise state data includes changes in muscle strength state, changes in exercise speed and changes in limb position during exercise training of the patient.
In order to obtain the change of muscle strength (muscle strength), the interaction force of a patient can be directly obtained by adding a force sensor into the exercise rehabilitation device; the current or power change of the motor of the exercise rehabilitation device can be calculated, and the interaction force of the affected limb and the motor can be deduced, so that the change of muscle strength can be deduced; the myoelectric signals of the limbs of the patient can be obtained by utilizing the myoelectric sensor, and the corresponding muscle strength change condition can be calculated. In order to obtain the change of the limb movement speed and the position during the training of the patient, an angle sensor or a speed and acceleration sensor in the movement rehabilitation device can be used or the sensors are comprehensively utilized to obtain the limb movement speed of the patient and the change state of the maximum limb movement amplitude (position). And simultaneously, the indexes related to the movement state data are comprehensively utilized, and the recovery state of the muscle strength and the limb control capability of the patient can be estimated.
The electroencephalogram signal processing apparatus 101 updates a preset brain engagement template according to the first biological data.
Optionally, the interaction device 103 provides an operation interface for the user, and the operation interface receives at least one of the following parameters input by the user: training mode grading parameters, motion stimulation target parameters, brain electricity participation index sensitivity parameters and limit parameters of rehabilitation exercise equipment; the size parameter of the moving stimulus target is used for adjusting the size and/or the position of the moving stimulus target; the training mode grading parameters are used for indicating different training scenes, and each training scene corresponds to the limit parameters of the sports equipment and the sensitivity parameters of the electroencephalogram participation index.
The brain electrical participation degree index sensitivity parameter is introduced, so that the preset brain participation degree templates are diversified, different brain electrical participation degree index sensitivity parameters correspond to different preset brain participation degree templates, and the brain electrical participation degree index sensitivity parameters are used for indicating the degree that the attention of a patient is easy to control.
For example, the interactive device will present a setting operation control interface, select a training task classification scene according to the impairment degree of the patient exercise function, and may operate various initial parameters of the system, such as the sensitivity of the electroencephalogram participation index, the size of the stimulating person, the upper speed limit of the rehabilitation exercise device, and the initial parameters such as the motor mode. When a patient with serious motion function or a patient in early recovery use system, a low-level task scene can be selected, the number of control instructions and the action precision are reduced, the highest motion speed is limited, the motion idea induced virtual character size is increased, the brain electricity participation degree change sensitivity level is improved, and the patient control experience is ensured. Patients with less impaired exercise function or in the middle of recovery can select advanced tasks, control the cooperative action of the upper limb and the lower limb and complete finer complex actions. According to the characteristics of different patients, the training initial parameters are manually adjusted item by item or the automatic learning initial parameters after short-time training are selected. And after all initial parameters are adjusted, the formal training is started.
The motion parameters of the rehabilitation exercise equipment are not more than the limit parameters of the rehabilitation exercise equipment.
Based on the rehabilitation feedback training strategy of training participation, through the interactive task of training scene, instruct the patient to independently modulate participation index, control rehabilitation system and accomplish different movements, have very strong appeal and interactivity, make the patient actively participate in training process, accomplished central consciousness and sent the neural route of peripheral nerve feedback simultaneously, promoted rehabilitation training.
Optionally, before the electroencephalogram signal processing device 101 collects the electroencephalogram signal and the sensor data of the user, the electroencephalogram participation degree index sensitivity parameter input through the interaction device 103 is also obtained, and a corresponding brain participation degree template is determined according to the input electroencephalogram participation degree index sensitivity parameter.
The disclosed embodiment provides an electroencephalogram control system, as shown in fig. 2, which further comprises an interaction device 103,
the interaction device 103 displays the moving stimulus target, and the moving stimulus target is displayed in a mode of indicating a preset first attribute parameter and a preset second attribute parameter.
Alternatively, the motor stimulus target is a biological motor stimulus or an optional animated stimulus. Specifically, the moving stimulus target is a moving humanoid animation, the first attribute parameter refers to the moving frequency of the moving stimulus target, and the second attribute parameter refers to the moving phase of the moving stimulus target. For example, the motion frequency refers to the running speed of the humanoid animation, or the rotating speed of the wheels of the bicycle, and the motion phase refers to the current phase of the circular motion of the left leg or the right leg when the humanoid animation runs or the phase of the circular motion of the two legs when the bicycle is ridden. This step is also a step of initializing the display of the moving stimulus target.
Optionally, the exercise rehabilitation device 102 adjusts corresponding exercise parameters according to a preset corresponding relation according to the control instruction; acquiring second biological data of a user of the exercise rehabilitation device, and sending the second biological data to the interaction device;
the second biological data may include, for example, joint trajectory change data during limb movement. The sports rehabilitation device is provided with a detection device for acquiring second biological data.
The interaction device 103 analyzes the second biological data to generate corresponding first attribute parameters of the user and/or second attribute parameters of the user;
and updating the display mode corresponding to the motion stimulation target according to the first attribute parameter of the user and/or the second attribute parameter of the user.
The step can analyze the frequency and the phase of the real-time motion of the user according to the second biological data of the user, so that the display mode of the motion stimulation target is adjusted to be the same as the frequency and the phase of the real-time motion of the user. The human-shaped animation is presented as an induction target of the fixation of the patient in the rehabilitation training process, and the human-shaped animation acts and the sports rehabilitation device for driving the patient to move, such as the action of upper and lower limb training equipment, are in the same frequency and in the same phase, so as to strengthen the mirror image nervous system action, induce the active sports consciousness of the patient, activate the central motor nerves and improve the rehabilitation effect of the brain control rehabilitation training system.
Optionally, the exercise rehabilitation device 102 outputs an evaluation index parameter, and sends the evaluation index parameter to the electroencephalogram signal processing device 101, where the electroencephalogram signal processing device 101 updates the preset brain engagement template according to the evaluation index parameter, and the evaluation index parameter includes at least one of the following: motion state data, motion power information, brain electrical participation index sensitivity parameters and brain electrical participation change curves. The sensitivity parameter of the brain electrical participation index is calculated by the exercise rehabilitation device 102 according to a preset rule, and is used for representing the degree of the patient easily controlled by the brain electrical control system.
Optionally, the exercise rehabilitation device 102 sends the evaluation index parameter to the interaction device 103, and the interaction device 103 generates and displays an analysis report according to the evaluation index parameter.
Optionally, the electroencephalogram signal processing apparatus 101 includes at least one of: an electroencephalogram head ring, a multi-lead dry electrode electroencephalogram cap, a saline electrode electroencephalogram cap or a multi-lead wet electrode electroencephalogram cap.
The electroencephalogram signal processing device in the system can be worn on the head of a user, equipment is easy and convenient to wear, comfortableness is high, index judgment is more accurate by combining head position motion parameters, the system has wider applicability, and the system greatly weakens contradicting emotion of the patient during use and saves configuration time of a rehabilitation engineer.
Optionally, the interaction means 103 comprises at least one of: liquid crystal display device, virtual reality VR device, augmented reality AR device, 3D formation of image display device.
In one embodiment, calculating the brain engagement index based on the brain electrical signal and sensor data includes the steps of:
step 301, performing independent component analysis and noise elimination processing on the electroencephalogram signal, and filtering blink and low-frequency noise interference to obtain a preprocessing signal;
step 302, performing band-pass filtering on the preprocessed signal by using an infinite impulse response digital filter;
specifically, the filter bank adopts an infinite impulse response digital filter, and the upper and lower cut-off frequencies of the filters in the filter bank are respectively 4-7Hz,8Hz-13Hz and 13Hz-20Hz. The electroencephalogram signals after passing through the filter bank are respectively EEG theta (theta rhythm wave), EEG alpha (alpha rhythm wave) and EEG beta (beta rhythm wave) 3 electroencephalogram rhythm wave bands.
Step 303, calculating the symbolized sample entropy of each frequency segment after band-pass filtering;
the calculation method preferably symbolizes the sample entropy value. First, each band signal is subjected to a symbolization process to obtain a symbol sequence sθ (θ rhythm wave symbol sequence), sα (α rhythm wave symbol sequence), and sβ (β rhythm wave symbol sequence). The calculation method is that EEG (electroencephalogram) time series EEG= { Xi: 1.ltoreq.i.ltoreq.N }, firstly, sorting according to the magnitude, when the number of symbols N is given, finding N-1 equally dividing points (marked as t1, t2, …, tn-1) as a threshold value of symbol division, and converting an original signal sequence into a discrete symbol sequence { Si) according to the following rule: 1.ltoreq.i.ltoreq.N }:
And then calculating sample entropy of symbol sequences corresponding to each rhythm wave to obtain SEsymb-theta (theta rhythm wave symbolized sample entropy), SEsymb-alpha (alpha rhythm wave symbolized sample entropy) and SEsymb-beta (beta rhythm wave symbolized sample entropy) as results.
And 305, inputting the symbolized sample entropy into a preset feature weighted Support Vector Machine (SVM) classifier, and calculating to obtain a brain participation index.
Finally, the entropy results SESymb-theta, SESymb-alpha, SESymb-beta and head movement posture feature Angle of all symbolized samples Diff ，Angle ACC The information is input into a pre-trained SVM classifier (the pre-trained SVM classifier is different according to selected brain participation templates with different sensitivities), so that a three-classification result is obtained, and the three-classification result represents the high, medium and low degrees of participation. This is merely an illustrative description of the present disclosure and the embodiments are not limited in any way to the specific class of the embodiments. In one embodiment, the electroencephalogram signal processing apparatus also collects sensor data; generating a corresponding control instruction according to the brain engagement index and a preset brain engagement template comprises:
and generating a corresponding control instruction according to the brain engagement index, the sensor data and a preset brain engagement template.
Calculating a brain engagement index based on the brain electrical signals and sensor data includes the steps of:
Steps 301 to 303 are the same as the above embodiments, and will not be described again. After step 303, step 304 is performed.
Step 304, motion acceleration data are obtained according to the sensor data, quaternion is converted into Euler angles for the sensor data, and angle data are calculated;
the sensor comprises at least one of the following: gyroscopes, accelerometers or electronic compasses.
In particular, the head orientation angle and motion acceleration data acquired by the sensor may represent, to some extent, the head motion pose of the user. And converting quaternions acquired by the sensor into Euler angles, and calculating angle data. The angle data includes a first characteristic value and a second characteristic value. The first characteristic value refers to a characteristic value of the degree of visual field deviation of the head, and the second characteristic value refers to a characteristic value of the movement intensity of the head.
Specifically, the quaternion collected by a sensor (preferably a sensor comprising a gyroscope, an accelerometer and an electronic compass) is calculated and converted into an euler angle, and the result obtained by calculation is three-axis posture data representing the posture position of the head: roll Angle (Angle ψ), pitch Angle (Angle θ), yaw Angle (Angle γ). When the user needs to watch the stimulation target during training, and the offset space of the head view direction and the screen watching the stimulation target is smaller, the training participation degree is higher, and vice versa. The degree of offset between the head view orientation and the screen stimulation target can therefore be used as a feature to evaluate the change in attention. FIG. 5 is a schematic view of Angle data, with head-view offset characteristic values shown as Angle, according to an embodiment of the present disclosure Diff The calculation method comprises the following steps: and calculating the offset space included angle between the head visual field orientation position and the induced stimulation target in the screen within 1 second through the three-axis attitude angle of the sensor. (typically the absolute value of the angle varies between 0 and 90 degrees, with values of 90 degrees greater than 90 degrees). Then normalized, when the absolute value of the Angle difference is 90 degrees, the first characteristic value Angle Diff When the Angle difference is 0 degree, the characteristic value Angle is set to 0 degree Diff Setting 1, when the Angle difference is between 90 degrees and 0 degrees, the first characteristic value Angle Diff And varies linearly between 0 and 1.
In addition, the head tends to move slowly when noticing a certain task, the change of the movement acceleration is small, the movement intensity of the head is low, on the contrary, the head tends to move fast when unconsciously moving, the change of the movement acceleration is large, and the movement intensity of the head is high. Therefore, the acceleration change is also taken as the motion gesture feature to participate in the calculation of training attention, and the average value of the acquired head motion acceleration absolute values in the 1s duration is compared with the set threshold value to obtain the head motion intensity feature value Angle ACC . The calculation method comprises the following steps: at least the threshold, classified as training inattentive behavior, characteristic value Angle ACC Is 0; less than the threshold, classified as training attention behavior, characteristic value Angle ACC 1.
Wherein A is CC And calculating the average absolute acceleration of the duration per unit, wherein phi is the set threshold value of the intensity of the movement. The threshold may be set based on specific experience or may be modified.
Specifically, step 305 specifically performs the following operations:
and inputting the symbolized sample entropy, acceleration data and angle data into a preset feature weighted Support Vector Machine (SVM) classifier, and calculating to obtain a brain participation index.
Finally, the entropy results SESymb-theta, SESymb-alpha, SESymb-beta and head movement posture feature Angle of all symbolized samples Diff ，Angle ACC The information is input into a pre-trained SVM classifier (the pre-trained SVM classifier is different according to selected brain participation templates with different sensitivities), so that a three-classification result is obtained, and the three-classification result represents the high, medium and low degrees of participation. This is merely an illustrative description of the present disclosure and the embodiments are not limited in any way to the specific class of the embodiments.
An embodiment of the present disclosure provides an electroencephalogram control method, as shown in fig. 6, including:
step 601, an electroencephalogram signal processing device collects electroencephalogram signals and sensor data of a user;
step 602, calculating brain participation indexes based on the brain electrical signals and sensor data;
Step 603, generating a corresponding control instruction according to the brain engagement index and a preset brain engagement template;
step 604, sending the control instruction to a sports rehabilitation device; the brain participation degree template stores the corresponding relation between the brain participation degree index and the control instruction;
step 605, the exercise rehabilitation device determines corresponding exercise parameters according to the control instruction and a preset corresponding relation, and operates according to the exercise parameters;
step 606, acquiring first biological data of a user corresponding to the exercise rehabilitation device;
step 607, transmitting the first biological data to an electroencephalogram signal processing apparatus, where the first biological data includes movement state data;
step 608, the electroencephalogram signal processing device updates the preset brain participation degree template according to the first biological data.
The application method of the electroencephalogram control system provided by the invention is described below in combination with an application scene, as shown in fig. 7 to 8, a head of a user (such as a patient) wears an electroencephalogram signal processing device (an electroencephalogram signal acquisition and analysis module), a body wears a sports rehabilitation training module, and the electroencephalogram signal processing device is connected with an interaction device (a scene interaction stimulation module) in a wired or wireless mode. As shown in fig. 7, the method mainly comprises the following steps:
Step 701, wearing an electroencephalogram signal acquisition and analysis module by a patient, namely the electroencephalogram signal processing device in the embodiment, selecting different types of electrodes according to the signal acquisition quality, fixing a fixing device of a motion rehabilitation training module, and placing hardware display equipment of a scene interaction stimulation module at a proper position.
Specifically, according to the brain electrical signal quality of a patient, a head brain electrical signal acquisition and analysis module (preferably equipment such as brain electrical head rings, multi-lead dry electrodes or saline electrode brain electrical caps) is worn, limbs of the patient are safely fixed through a fixing device of a motion rehabilitation training module, and after a hardware display device (optional high refresh rate liquid crystal display device or VR glasses) of a scene interaction stimulation module is placed at a proper position, all the modules can be started.
Step 702, an electroencephalogram signal acquisition and analysis module enters a working stage to start calculation of a training brain participation index. The brain participation index is obtained by comprehensively calculating two parts of data, namely an electroencephalogram signal and a head movement posture, acquired by equipment.
After the electroencephalogram signal acquisition and analysis module enters the working phase, the training participation index is calculated by the electroencephalogram signal acquisition and analysis module, and all the electroencephalogram signals and participation characteristic data can be transmitted and stored in a wireless mode. The participation index calculation is mainly carried out by combining brain electrical rhythm wave changes such as brain electrical theta wave, alpha wave, beta wave and the like within 1 second as main characteristics, and meanwhile, parameters such as the orientation angle, acceleration and the like of the head of a patient are taken as secondary characteristics to also participate in the participation index calculation. Patients using the system for the first time can carry out short-time learning training, and the scenario interaction stimulation module feeds back participation indexes to the patients through the participation track graph to guide the patients to learn and control the change of the brain electricity participation indexes. The participation index calculation adopts the electroencephalogram signals with the length of 1 second to be processed, and simultaneously outputs three-classification training participation results (high participation, medium participation and low participation) at intervals of 1 s. Firstly, the brain electrical signal is subjected to independent component analysis and noise elimination treatment, and blink and low-frequency noise interference are filtered. And then carrying out band-pass filtering on the electroencephalogram signals acquired by all signal leads by using an infinite impulse response digital filter, wherein the filtering range is 4-7Hz (theta rhythm wave), 8Hz-13Hz (alpha rhythm wave), 13Hz-20Hz (beta rhythm wave) and other 3 electroencephalogram rhythm wave bands, and respectively calculating the symbolized entropy value of each frequency band. The head orientation position and average change acceleration information are then calculated. Because when the participation degree of the patient is low, the head deviation is often caused by the fact that the gazing position is not aligned with the target of the stimulation person, or the head random movement is caused by the fact that the gazing position is not fixed, the head movement orientation position and average acceleration change information can be used as secondary characteristic information of the training participation degree. And finally, inputting all symbolized entropy results, head movement positions and average acceleration change information into a feature weighted SVM classifier which is trained by a large number of people in advance to obtain a training participation index. The training can be repeated for a plurality of times, the data and the index in each rehabilitation training process are used as new training data, the classifier is further optimized until the participation degree characteristics are kept to be controllably and stably output after a certain period of treatment.
Step 703, a limb exercise rehabilitation training module, that is, the exercise rehabilitation device described in the above embodiment, enters a working phase: the motor of the limb movement rehabilitation training module works.
After the exercise rehabilitation training module enters the working stage, the motor works. And selecting a movement mode, wherein the movement mode is selected to drive the limbs to passively move at a set speed according to the impaired degree of the movement function of the patient or provide auxiliary force to help the limbs to actively move, and the speed and the auxiliary force are adjusted and changed according to the participation degree and the impaired degree of the movement function.
Step 704, the contextual interaction stimulus module, i.e. the interaction means in the above embodiments, enters the working phase.
The scene interaction stimulation module presents an operation control interface, and sets various initial parameters such as maximum training speed, auxiliary force, sensitivity, training participation index reference value, task complexity and the like of the system. The parameters are set according to the damage degree of the exercise function of the patient, if the damage degree of the patient is higher, the task difficulty is reduced, only the patient is required to control rough exercise, the number of control instructions is reduced, the maximum exercise speed is reduced, the sensitivity of the assisting force or speed change is improved, the sensitivity of the exercise participation is improved, and the patient can control the exercise system more easily.
Step 705, the patient changes the participation index according to the task requirement in the scene interaction stimulation module, controls the exercise equipment and the stimulation target, and the exercise equipment (optionally combined with the electric stimulation) feeds back the exercise sensation to complete the training task. The motion parameters of the motion rehabilitation training module are changed in real time according to the participation index and are synchronous with the motion stimulation target in the scene interaction stimulation module.
In the formal training process, the training strategy adjusts the motion parameters (such as motion assisting force, damping, speed and the like) of the motion rehabilitation training module by adopting the change of the participation index of the patient, and simultaneously synchronizes the motion change in the scene interaction stimulation module so as to ensure that the patient can control the training motion by independently modulating the participation degree, complete the related training task, promote the system interactivity and strengthen the active participation feeling of the patient.
In a specific embodiment, the training mode is active auxiliary force training, and the patient needs to control the riding virtual character to complete the training task, namely, the training action of the upper and lower limbs is controlled and perfected according to the instruction of the training participation degree. When the task indicates to increase the participation degree, riding personnel pass through an arch bridge interaction scene, the original basic auxiliary force of the system movement rehabilitation training module is reduced, and the damping is increased, so that the movement of a patient is more difficult. At the moment, the user is prompted to concentrate on participating in the task, and the sports stimulus character in the scene interaction stimulus module is controlled to maintain riding movement. When a patient concentrates on participating in a task, the training participation index is maintained at a high level, and the system gradually improves the auxiliary force of the exercise rehabilitation training module, so that the patient can control the exercise speed of exercise equipment, and the training task is completed through an arch bridge in an interactive exercise stimulation scene. This training strategy results in a higher sense of participation by the patient during the exercise training process, and a stronger sense of evoked exercise ideas. When the training mode is speed training, the strategies are similar, and the control of the movement speed of the training equipment is completed according to the training participation index.
Step 706, the frequency phase of the motion stimulation target in the scene interaction stimulation module and the frequency phase of the motion of the upper and lower limb motion rehabilitation training module can be kept synchronous, so that the patient can more effectively induce motion consciousness and activate the motion center when observing stimulation. In the movement process, the human body real motion capture system can record the movement track details of the limbs of the patient, combine the movement track details with the action of the stimulating person in the scene, or simultaneously display the real actions of the patient in the interactive interface so as to ensure the activation of mirror image neurons and enhance the movement consciousness of the patient. (optionally, the peripheral nerve of the limb body can be stimulated by adopting the functional electric stimulation simultaneously, so that the motor-sensory stimulation path is further perfected.)
Step 707, after completing the first training task, returning to step 704, repeating step 704 and step 705, and performing the next training step.
After completing the first training task, return to step 703 and repeat step 704, step 705 and step 706. The next training step can be selected in the contextual interaction stimulation module. The task amount and difficulty in the scene are increased, and the patient is ensured to obtain sufficient iteration intensity training.
Step 708, after completing all tasks, the rehabilitation training report module provides a short training feedback report according to the data such as the electroencephalogram signals and the exercise training parameters, and feeds the training parameters back to the personalized training algorithm, so as to further iterate and optimize the training participation index algorithm of the patient.
After all tasks are completed, the scene interaction stimulation module stores all training data and is used as an iterative training data personalized optimization classifier for training participation calculation. Meanwhile, a training report is provided according to each index of the training process (the report reflects the change of the index of the training participation degree and the indication task related to the patient in the single training process, the state of the power change process of the training exercise of the affected limb, and the score change of the training effect in the rehabilitation course is provided according to the parameters), so that the patient can obtain the feedback of the rehabilitation training process, and a doctor and the patient can more specifically formulate a training strategy according to the training feedback.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (6)
1. An electroencephalogram control system, characterized in that the system comprises: an electroencephalogram signal processing device and a sports rehabilitation device,
the electroencephalogram signal processing device is used for collecting electroencephalogram signals of a user; calculating brain participation indexes based on the brain electrical signals; generating a corresponding control instruction according to the brain participation index and a preset brain participation template, and sending the control instruction to a sports rehabilitation device; the brain participation degree template stores the corresponding relation between the brain participation degree index and the control instruction, wherein the brain participation degree index is determined by an electroencephalogram signal acquired by equipment and a head movement gesture, the electroencephalogram signal is determined based on electroencephalogram rhythm waves of electroencephalogram theta waves, alpha waves and beta waves, and the head movement gesture at least comprises an orientation angle and acceleration of a head;
the exercise rehabilitation device determines corresponding exercise parameters according to the control instruction and a preset corresponding relation, and operates according to the exercise parameters; acquiring first biological data of a user corresponding to the exercise rehabilitation device, and sending the first biological data to an electroencephalogram signal processing device, wherein the first biological data comprises exercise state data;
the electroencephalogram signal processing device updates the preset brain participation degree template according to the first biological data;
Wherein, calculate brain participation index based on the brain electrical signal, include:
performing independent component analysis and noise elimination on the electroencephalogram signal, and filtering blink and low-frequency noise interference to obtain a preprocessing signal; band-pass filtering the pre-processed signal with an infinite impulse response digital filter; calculating the symbolized sample entropy of each frequency segment after band-pass filtering; inputting the symbolized sample entropy into a preset feature weighted Support Vector Machine (SVM) classifier, and calculating to obtain a brain participation index;
the electroencephalogram control system also comprises an interaction device,
the interaction device displays a preset motion stimulation target, and the motion stimulation target is used for stimulating the brain electrical signals;
the exercise rehabilitation device adjusts corresponding exercise parameters according to the control instruction and a preset corresponding relation;
the interaction device provides an operation interface for a user, and the operation interface receives at least one of the following parameters input by the user: training mode grading parameters, motion stimulation target parameters, brain electricity participation index sensitivity parameters and limit parameters of rehabilitation exercise equipment; the size parameter of the moving stimulus target is used for adjusting the size and/or the position of the moving stimulus target; the brain electrical participation index sensitivity parameter is used for determining a preset brain participation template, and the brain electrical participation index sensitivity parameter is used for indicating the degree of easy control of the attention of a patient; the training mode grading parameters are used for indicating different training scenes, and each training scene corresponds to the limit parameters of the sports equipment and the sensitivity parameters of the electroencephalogram participation index; the motion parameters of the rehabilitation exercise equipment are not more than the limit parameters of the rehabilitation exercise equipment;
The exercise rehabilitation device outputs evaluation index parameters and sends the evaluation index parameters to the electroencephalogram signal processing device, the electroencephalogram signal processing device updates the preset brain participation degree template according to the evaluation index parameters, and the evaluation index parameters comprise at least one of the following: motion state data, motion power information, brain electrical participation index sensitivity parameters and brain electrical participation change curves.
2. The brain wave control system according to claim 1, wherein,
acquiring second biological data of a user of the exercise rehabilitation device, and sending the second biological data to the interaction device, wherein the second biological data comprises limb joint movement change track data;
the interaction device analyzes the second biological data and generates corresponding first attribute parameters of the user and/or second attribute parameters of the user;
and updating the display mode corresponding to the motion stimulation target according to the first attribute parameter of the user and/or the second attribute parameter of the user.
3. The electroencephalogram control system according to claim 1, wherein the exercise rehabilitation device transmits the evaluation index parameter to an interaction device, and the interaction device generates and displays an analysis report according to the evaluation index parameter;
The electroencephalogram signal processing device comprises at least one of the following: an electroencephalogram head ring, a multi-lead dry electrode electroencephalogram cap, a saline electrode electroencephalogram cap or a multi-lead wet electrode electroencephalogram cap;
the interaction means comprises at least one of: liquid crystal display device, virtual reality VR device, augmented reality AR device, 3D formation of image display device.
4. The brain electrical control system according to claim 3, wherein the brain electrical signal processing device further acquires brain electrical participation index sensitivity parameters input through the interaction device before acquiring brain electrical signals and sensor data of the user, and determines a corresponding brain participation template according to the input brain electrical participation index sensitivity parameters.
5. The electroencephalogram control system according to claim 1, wherein the electroencephalogram signal processing device further collects sensor data, obtains motion acceleration data according to the sensor data, performs quaternion conversion on the sensor data into euler angles, and calculates angle data;
inputting the symbolized sample entropy into a preset feature weighted Support Vector Machine (SVM) classifier, and calculating to obtain a brain participation index comprises the following steps:
and inputting the symbolized sample entropy, acceleration data and angle data into a preset feature weighted Support Vector Machine (SVM) classifier, and calculating to obtain a brain participation index.
6. A brain-controlled rehabilitation method, the method comprising:
the electroencephalogram signal processing device is used for collecting electroencephalogram signals and sensor data of a user; calculating brain participation indexes based on the brain electrical signals; generating a corresponding control instruction according to the brain participation index and a preset brain participation template, and sending the control instruction to a sports rehabilitation device; the brain participation degree template stores the corresponding relation between the brain participation degree index and the control instruction, wherein the brain participation degree index is determined by an electroencephalogram signal acquired by equipment and a head movement gesture, the electroencephalogram signal is determined based on electroencephalogram rhythm waves of electroencephalogram theta waves, alpha waves and beta waves, and the head movement gesture at least comprises an orientation angle and acceleration of a head;
the exercise rehabilitation device determines corresponding exercise parameters according to the control instruction and a preset corresponding relation, and operates according to the exercise parameters; acquiring first biological data of a user corresponding to the exercise rehabilitation device, and sending the first biological data to an electroencephalogram signal processing device, wherein the first biological data comprises exercise state data;
the brain signal processing device updates the preset brain participation degree template according to the first biological data;
Wherein, calculate brain participation index based on the brain electrical signal, include:
performing independent component analysis and noise elimination on the electroencephalogram signal, and filtering blink and low-frequency noise interference to obtain a preprocessing signal; band-pass filtering the pre-processed signal with an infinite impulse response digital filter; calculating the symbolized sample entropy of each frequency segment after band-pass filtering; inputting the symbolized sample entropy into a preset feature weighted Support Vector Machine (SVM) classifier, and calculating to obtain a brain participation index;
the interaction device displays a preset motion stimulation target, wherein the motion stimulation target is used for stimulating the brain electrical signals;
the exercise rehabilitation device adjusts corresponding exercise parameters according to the control instruction and a preset corresponding relation;
the interaction device provides an operation interface for a user, and the operation interface receives at least one of the following parameters input by the user: training mode grading parameters, motion stimulation target parameters, brain electricity participation index sensitivity parameters and limit parameters of rehabilitation exercise equipment; the size parameter of the moving stimulus target is used for adjusting the size and/or the position of the moving stimulus target; the brain electrical participation index sensitivity parameter is used for determining a preset brain participation template, and the brain electrical participation index sensitivity parameter is used for indicating the degree of easy control of the attention of a patient; the training mode grading parameters are used for indicating different training scenes, and each training scene corresponds to the limit parameters of the sports equipment and the sensitivity parameters of the electroencephalogram participation index; the motion parameters of the rehabilitation exercise equipment are not more than the limit parameters of the rehabilitation exercise equipment;
The exercise rehabilitation device outputs evaluation index parameters and sends the evaluation index parameters to the electroencephalogram signal processing device, the electroencephalogram signal processing device updates the preset brain participation degree template according to the evaluation index parameters, and the evaluation index parameters comprise at least one of the following: motion state data, motion power information, brain electrical participation index sensitivity parameters and brain electrical participation change curves.
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