Training method based on combination of motor imagery and dream guidance

Abstract

The invention discloses a training method based on motor imagery combined with dream guidance, which comprises the following steps: acquiring electroencephalogram data of motor imagery training in an off-line manner, preprocessing the data, extracting features, and training a motor imagery classification model; extracting motor imagery electroencephalogram data of the affected part on line, and performing classification prediction through a previously trained motor imagery classification model to obtain a statistical result; performing classification prediction on the night data model obtained through night dream guidance to obtain a statistical result; the classification prediction results of the daytime data model and the night data model are compared, common correct data parts are screened out for data superposition, the superposed data model is used for training the classification model again, if the accuracy rate is higher than that of the daytime data model, the classification model is reserved, and if the accuracy rate is lower than that of the night data model, the classification model is not reserved. Otherwise, using the previous classification model. According to the method, the data generated by dream intervention is processed and then combined with the motor imagery data model to improve the accuracy of the classification model, so that correct feedback to a patient is achieved.

Description

Technical Field

[0001] The invention belongs to the field of electroencephalogram data processing, and in particular relates to a training method based on motor imagery combined with dream guidance. Background Art

[0002] Since the brain-computer interface was mentioned, it has been widely used in various fields, especially in the medical field. It is used as an effective preventive measure and is very effective. For example, diseases such as epilepsy, ADHD, and Alzheimer's disease can be predicted in advance. It also occupies a good position in postoperative recovery, especially neurological diseases such as hemiplegia caused by stroke, which can be rehabilitated through motor imagery in EEG technology.

[0003] However, the principle of motor imagery requires a large amount of effective data as a basis. If any of these factors are missing, the experimental results will be affected. This involves long-term and repetitive training, which is not only time-consuming and labor-intensive, but also causes negative treatment attitudes. Eliminating this situation is a problem that needs to be solved at present.

[0004] Existing studies have shown that a brain wave feature can be used to identify the patient's state of consciousness: dreaming, deep sleep, wakefulness, etc. The rational use of this feature can identify the beginning of a dream. There are two existing dream intervention methods: one is to identify the dream state and then give a light stimulation prompt; the other is to induce dream trends through the waves of the wakeful state. Without affecting sleep, guiding patients to conduct rehabilitation training during dreams will solve the problems of the length of time for patients to recover and the amount of data. Summary of the invention

[0005] In order to solve the deficiencies in the prior art, the purpose of the present invention is to provide a training method based on motor imagery combined with dream guidance.

[0006] To achieve the purpose of the present invention, the technical solution adopted by the present invention is:

[0007] A training method based on motor imagery combined with dream guidance, comprising the following steps:

[0008] (1) Collect 4 seconds of EEG data of motor imagery training offline until the set number of times is reached, and store it in the daytime data model. The collected EEG data includes the information of the affected side and the normal side; perform data preprocessing and extract features to train the motor imagery classification model;

[0009] (2) Online extraction of motor imagery EEG data of the affected side until the set number of times is reached, and the data is stored in the daytime data model. The previously trained motor imagery classification model is used for classification prediction to obtain statistical results. If the predicted result is the same as the affected side, it is correct, otherwise it is wrong.

[0010] (3) Classify and predict the night data model obtained through night dream guidance to obtain statistical results; if the predicted result is the same as the affected part, it is correct, otherwise it is wrong;

[0011] (4) Compare the classification prediction results of the daytime data model and the nighttime data model, select the common correct data parts for data superposition, and obtain the superimposed data model; use the superimposed data model to train the classification model again. If the accuracy is higher than that of the daytime data model, retain this classification model; otherwise, use the previous classification model.

[0012] Furthermore, step (3) specifically includes the steps of:

[0013] (3.1) Wear an EEG acquisition device to collect EEG data during sleep and calculate the frequency domain characteristics of EEG waves;

[0014] (3.2) judging whether the sleep state is a deep sleep state according to the frequency domain characteristics of the brain waves, and controlling the sleep monitoring system to perform different operations;

[0015] (3.3) If entering a deep sleep state, control the dream guidance system to guide stimulation and conduct the same motor imagery training as during the day to obtain a night data model;

[0016] (3.4) Perform motor imagery training according to the sleep cycle cycle to obtain a night data model of multiple sleep cycles.

[0017] Furthermore, in step (3.1), the EEG data is processed by Fourier transform or wavelet transform to calculate the frequency domain characteristics of the EEG waves, distinguish different biological signals during sleep, and judge the non-rapid eye movement period and the rapid eye movement period.

[0018] Further, in step (3.2), the sleep monitoring system includes a sleep aid system, a dream guidance system and a wake-up system;

[0019] When in the non-rapid eye movement period or preparing to fall asleep, the sleep aid system provides sleep-aid stimulation to enable the patient to fall asleep quickly or enter the next sleep stage, including music sleep aid and electric stimulation massage sleep aid; when in the rapid eye movement period, guided stimulation is provided through the dream guidance system; when the time reaches the end, awakening stimulation is provided through the awakening system.

[0020] Furthermore, in step (3.3), the corresponding part is stimulated according to the rehabilitation part set by the patient, and the same motor imagery training experiment as during the day is carried out during the stimulation process. Each stimulation lasts for 4 seconds until the set number of times is reached, and the EEG data of the imagination training is stored in the night data model.

[0021] Furthermore, label bits are added to the EEG data, and the data is distinguished according to the label bits as daytime training data or nighttime training data.

[0022] Furthermore, the stimulation guide includes electrical stimulation guide and sound stimulation guide.

[0023] Furthermore, under the guidance of electrical stimulation, when the sleep state enters the rapid eye movement period, the output of the electrical stimulation device is turned on, and the current is output to the set part according to the set rehabilitation part. The output current En is increased or decreased according to the feedback of each deep sleep state.

[0024] Furthermore, with sound stimulation guidance, when the sleep state enters the rapid eye movement period, the microphone mode is turned on, and the corresponding part or the activities performed on the corresponding part or the words related to the part are spoken according to the set rehabilitation part.

[0025] The beneficial effect of the present invention is that, compared with the prior art, the present invention processes the data generated by dream intervention and combines it with the data model of motor imagery to improve the accuracy of the classification model, thereby achieving correct feedback to the patient.

[0026] The present invention uses EEG acquisition equipment combined with sleep status to determine whether to access dreams, and then uses external output as intervention guidance conditions, such as electrical, acoustic, light and magnetic stimulation, to intervene and guide through specific parts or specific output waves, so that the patient can imagine specific parts in the dream for neural repair.

[0027] The present invention guides patients to perform corresponding motor imagination through EEG acquisition equipment combined with the motor imagery paradigm, so that patients can re-recognize the movements of the training parts during the training process to achieve rehabilitation effects. In addition, the rehabilitation effect can also be achieved by driving the movement of the affected limbs through hardware devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a flow chart of the training method based on motor imagery combined with dream guidance according to the present invention;

[0029] Figure 2 It is a sleep monitoring diagram;

[0030] Figure 3 is a schematic diagram of a sleep monitoring system;

[0031] Figure 4 It is a schematic diagram of sleep biological signals;

[0032] Figure 5 This is a diagram of the sleep cycle. DETAILED DESCRIPTION

[0033] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of this application.

[0034] The training method based on motor imagery combined with dream guidance described in the present invention performs motor imagery training and trains a motor imagery classification model during the day, and achieves mirror learning and cognitive learning through an electroencephalogram acquisition device combined with motor imagery to achieve brain repair of nerves. When sleeping at night, the electroencephalogram acquisition device collects electroencephalogram data during sleep to analyze which sleep stage the user is in. If the user is in deep sleep, corresponding stimulation is performed to map out the parts that need rehabilitation, and the data of the stimulation stage is saved and provided to the motor imagery classification model for use, thereby increasing the data volume of the motor imagery classification model, and can eliminate many environmental interference factors through the state of deep sleep to ensure data quality.

[0035] like Figure 1 As shown, the training method based on motor imagery combined with dream guidance of the present invention comprises the following specific steps:

[0036] Step 1, EEG data acquisition and preprocessing;

[0037] Wear an EEG acquisition device to collect EEG data, and pre-process the collected EEG data to remove power frequency interference. Because the EEG data signal characteristic range is within the range of 0.1Hz to 30Hz, the raw data needs to be filtered through a bandpass filter.

[0038] Step 2, performing daytime motor imagery training to obtain a daytime data model for training the motor imagery classification model; comparing the daytime data model with the nighttime data model, superimposing the correct data part, and continuing to train the motor imagery classification model;

[0039] In the offline stage, motor imagery training was conducted under the motor imagery paradigm, and the 4-second EEG data of the imagination training was stored in the daytime data model. The collected EEG data included two types of information: the affected side and the normal side. No less than 60 EEG data were collected under the motor imagery paradigm. The data model was preprocessed, features were extracted through the common space model algorithm, the motor imagery classification model was trained, and two results were obtained through support vector machine classification, and compared to obtain the accuracy rate.

[0040] In the online stage, several times of motor imagery EEG data of the affected part are extracted and stored in the daytime data model. After each extraction, the motor imagery classification model trained previously is used for classification. If the result prediction is the same as the affected part, it is correct, otherwise it is wrong. In addition, the night data model obtained through night dream guidance needs to be classified and predicted to obtain statistical results.

[0041] After the online phase is over, the daytime data model is compared with the nighttime data model, and the common correct data parts are selected for data superposition to obtain the superimposed data model. After that, the classification model is trained again using the superimposed data model. If the accuracy is higher than that of the daytime data model, the superimposed data model is used and the classification model is retained. Otherwise, the previous data model and classification model are used.

[0042] The daytime data model refers to the EEG data model guided by active motor imagery during the patient's wakefulness, and the nighttime training model refers to the EEG data model of the patient's passive stimulation after deep sleep. Both models are established based on the data accumulated after several motor imagery sessions, including offline and online stages.

[0043] like Figure 2 As shown, at night, the same motor imagery training as during the day is carried out through dream guidance, and the data is stored in the night data model. The EEG data during sleep is collected by the EEG acquisition device to distinguish the sleep state and determine whether the current sleep state is a deep sleep state. If it is a deep sleep state, the stimulation source is turned on for guidance; if it is not a deep sleep, the sleep aid system is activated to accelerate falling asleep.

[0044] Through stimulation and guidance, according to the part of the patient that needs rehabilitation, the final result of guiding the dream through external intervention after entering the dream is the part that needs rehabilitation; finally, the data generated by stimulation and guidance will be used to create a night data model separately, and compared with the daytime data model, and the correct part of the data will be merged with the motor imagination data to enhance the model effect, thereby increasing the classification accuracy of the motor imagination model to improve the accuracy of the patient's motor imagination.

[0045] The specific steps are as follows:

[0046] Step 1: EEG data collection and processing, calculating the frequency domain characteristics of EEG waves;

[0047] Wearing EEG acquisition equipment, Figure 3 In the sleep monitoring system shown, the sleep start time, sleep duration, rehabilitation site (guidance site), and stimulation source category (electricity, light, sound, magnetism, etc.) are set. When the time reaches the preset sleep start time, the EEG acquisition device is turned on and real-time data transmission is performed.

[0048] The sleep monitoring system includes a sleep aid system, a dream guidance system and an awakening system; sleep aid stimulation is performed through the sleep aid system, guidance stimulation is performed through the dream guidance system, and awakening stimulation is performed through the awakening system.

[0049] EEG data processing is to calculate the frequency domain characteristics of brain waves through Fourier transform or wavelet transform, and distinguish different biological signals during sleep, such as Figure 4 It is mainly divided into four parts: non-rapid eye movement stage 1, when the characteristic wave is mainly 8-13Hz alpha wave, with an amplitude of 20-40μV; non-rapid eye movement stage 2, when the characteristic wave is 11-16Hz sleep spindle wave and K complex wave, with an amplitude less than 50μV; non-rapid eye movement stage 3, when the characteristic wave is 0.5-2Hz delta wave, with an amplitude greater than 75μV; rapid eye movement stage, when the characteristic wave is 3-7Hz theta wave, with an amplitude less than 50μV.

[0050] Step 2, judging whether the sleep state is a deep sleep state according to the frequency domain characteristics of the brain waves, and controlling the sleep monitoring system to perform different operations;

[0051] During the whole sleep process, there will be 4 to 6 sleep cycles, which are from non-rapid eye movement to rapid eye movement and then back to non-rapid eye movement. According to the real-time filtering processing in step 1, the frequency domain characteristics of the brain waves in each period are obtained, and it is judged in real time whether the REM period (deep sleep state) has been entered or exited, and the sleep aid system, dream guidance system and awakening system need to be switched in real time.

[0052] When the waveform characteristics are those of the non-rapid eye movement period or the period of preparing to fall asleep, the sleep aid system provides corresponding sleep-aid stimulation to enable the patient to fall asleep quickly or enter the next sleep stage; including: music to aid sleep, which is also the most commonly used way for people to fall asleep, and is very suitable for non-rapid eye movement 1 or the period of falling asleep; electrical stimulation massage to aid sleep, which can release weak current through the stimulation electrodes in the scalp to massage the scalp, making people enter a state of fatigue and fall asleep quickly.

[0053] When in the rapid eye movement period, guidance stimulation is provided through the dream guidance system; when the time reaches the end, awakening stimulation is provided through the awakening system.

[0054] Step 3: If the sleep state reaches a deep state, the dream guidance system is controlled to guide and stimulate the sleep state, and the same motor imagery training as during the day is performed to obtain a nighttime data model.

[0055] When the characteristic waveform is the rapid eye movement period (deep sleep state), the guidance mode is entered. Because dreams occur during the rapid eye movement period, the corresponding parts are stimulated according to the rehabilitation parts set by the patient.

[0056] During the guided stimulation process, the same motor imagery training experiment as during the day is required, with a stimulation duration of 4 seconds. The 4-second EEG data of the imagination training is stored in the night data model. A label is added to this part of the EEG data, and then the data stage is distinguished based on the label. The rest time between each stimulation can be adjusted by setting it in advance.

[0057] The label bit is used to distinguish between daytime training data (active training data set under waking consciousness) and nighttime training data (passive training data set during the sleep stage). Its function is to distinguish the data so as not to confuse the specified model when training the model.

[0058] Guidance includes electrical stimulation guidance and sound stimulation guidance. For electrical stimulation guidance, when the sleep state enters the rapid eye movement period, the output of the electrical stimulation device is turned on, and the current is output to the set part according to the set rehabilitation part. The output current En increases or decreases according to the feedback of each deep sleep state. For example, when the En value output increases to K, the EEG monitors the end of the rapid eye movement period and enters the non-rapid eye movement or short-term wakefulness state, and switches to the sleep aid mode and records the current value K. The starting value will be K-Kn when entering the deep sleep period next time, and the operation will be repeated until the time is over or the sleep is awakened. In addition, the patient's feedback on the sleep quality of this time will be recorded. If it is found that it affects the sleep quality, the En value K will be lowered again when the user uses it next time.

[0059] Sound stimulation guidance: when the sleep state enters the rapid eye movement period, turn on the microphone mode, and speak out the corresponding part or the activities performed on the corresponding part or the words related to the part according to the set rehabilitation part. For example, when the rehabilitation part is the nerve that controls the left foot, the words "walking", "running", "left foot" and other guiding words are told to the patient through sound. The volume control is the same as the sound volume in the sleep aid mode, and can be accompanied by music.

[0060] Step 4, performing motor imagery training according to the sleep cycle cycle to obtain a night data model of multiple sleep cycles;

[0061] according to Figure 5 As shown in the sleep cycle, the mode will be switched according to the current state in each sleep cycle until the patient wakes up or the set duration is reached to start the awakening mode. At this time, the sleep-aiding stimulus source or the dream-guiding stimulus source will be turned off first, and then the awakening stimulus source will be turned on and increased continuously until the patient's EEG signal is detected to be in an awake state, and then the awakening stimulus source will be stopped.

[0062] During the rapid eye movement period (deep sleep state) of each sleep cycle, the dream guidance system is controlled to provide guided stimulation and the same motor imagery training as during the day is performed to obtain a night data model.

[0063] A set of data will be saved for one deep sleep cycle. Through the creation time, size, quality and real-time stimulation data of the data file, we can get the number of times the patient enters deep sleep, the duration, and the different durations of deep sleep combined with the intensity of the stimulation source, and infer the best guidance method, duration and stage, so as to gradually improve the guidance method.

[0064] The beneficial effect of the present invention is that, compared with the prior art, the present invention processes the data generated by dream intervention and combines it with the motor imagery data model to improve the accuracy of the classification model, thereby achieving correct feedback to the patient.

[0065] The present invention uses EEG acquisition equipment combined with sleep status to determine whether to access dreams, and then uses external output as intervention guidance conditions, such as electrical, acoustic, light and magnetic stimulation, to intervene and guide through specific parts or specific output waves, so that the patient can imagine specific parts in the dream for neural repair.

[0066] The present invention guides patients to perform corresponding motor imagination through EEG acquisition equipment combined with the motor imagery paradigm, so that patients can re-recognize the movements of the training parts during the training process to achieve rehabilitation effects. In addition, the rehabilitation effect can also be achieved by driving the movement of the affected limbs through hardware devices.

[0067] The applicant of the present invention has made a detailed explanation and description of the implementation examples of the present invention in conjunction with the drawings in the specification. However, those skilled in the art should understand that the above implementation examples are only preferred implementation schemes of the present invention, and the detailed description is only to help readers better understand the spirit of the present invention, but not to limit the scope of protection of the present invention. On the contrary, any improvements or modifications based on the inventive spirit of the present invention should fall within the scope of protection of the present invention.

Claims

1. A training method based on motor imagery combined with dream guidance, characterized in that: Includes steps: (1) Collect 4 seconds of EEG data of motor imagery training offline until the set number of times is reached, and store it in the daytime data model. The collected EEG data includes the information of the affected side and the normal side; perform data preprocessing and extract features to train the motor imagery classification model; (2) Online extraction of motor imagery EEG data of the affected side until the set number of times is reached, and the data is stored in the daytime data model. The previously trained motor imagery classification model is used for classification prediction to obtain statistical results. If the predicted result is the same as the affected side, it is correct, otherwise it is wrong. (3) Classify and predict the night data model obtained through night dream guidance to obtain statistical results; if the predicted result is the same as the affected part, it is correct, otherwise it is wrong; (4) Compare the classification prediction results of the daytime data model and the nighttime data model, select the common correct data parts for data superposition, and obtain the superimposed data model; use the superimposed data model to train the classification model again. If the accuracy is higher than that of the daytime data model, retain this classification model; otherwise, use the previous classification model.

2. The training method based on motor imagery combined with dream guidance according to claim 1, characterized in that: Step (3) specifically includes the steps of: (3.1) Wear an EEG acquisition device to collect EEG data during sleep and calculate the frequency domain characteristics of EEG waves; (3.2) judging whether the sleep state is a deep sleep state according to the frequency domain characteristics of the brain waves, and controlling the sleep monitoring system to perform different operations; (3.3) If entering a deep sleep state, control the dream guidance system to guide stimulation and conduct the same motor imagery training as during the day to obtain a night data model; (3.4) Perform motor imagery training according to the sleep cycle cycle to obtain a night data model of multiple sleep cycles.

3. The training method based on motor imagery combined with dream guidance according to claim 2, characterized in that: Step (3.1) processes EEG data through Fourier transform or wavelet transform, calculates the frequency domain characteristics of EEG waves, distinguishes different biological signals during sleep, and is used to determine non-rapid eye movement and rapid eye movement periods.

4. The training method based on motor imagery combined with dream guidance according to claim 2, characterized in that: Step (3.2), the sleep monitoring system includes a sleep aid system, a dream guidance system and a wake-up system; When in the non-rapid eye movement period or preparing to fall asleep, the sleep aid system provides sleep-aid stimulation to enable the patient to fall asleep quickly or enter the next sleep stage, including music sleep aid and electric stimulation massage sleep aid; when in the rapid eye movement period, guided stimulation is provided through the dream guidance system; when the time reaches the end, awakening stimulation is provided through the awakening system.

5. The training method based on motor imagery combined with dream guidance according to claim 2, characterized in that: Step (3.3) stimulates the corresponding part according to the rehabilitation part set by the patient, and guides the stimulation process to conduct the same motor imagery training experiment as during the day. Each stimulation lasts for 4 seconds until the set number of times is reached, and the EEG data of the imagination training is stored in the night data model.

6. The training method based on motor imagery combined with dream guidance according to claim 5, characterized in that: Add label bits to the EEG data, and use the label bits to distinguish whether the data is in the daytime training data or nighttime training data.

7. The training method based on motor imagery combined with dream guidance according to claim 5, characterized in that: Stimulation guidance includes electrical stimulation guidance and sound stimulation guidance.

8. The training method based on motor imagery combined with dream guidance according to claim 7, characterized in that: Electrical stimulation guidance: when the sleep state enters the rapid eye movement period, the electrical stimulation device is turned on to output current to the set part according to the set rehabilitation part. The output current En increases or decreases according to the feedback of each deep sleep state.

9. The training method based on motor imagery combined with dream guidance according to claim 7, characterized in that: Sound stimulation guidance: when the sleep state enters the rapid eye movement period, turn on the microphone mode, and speak the corresponding part or the activities performed on the corresponding part or the words related to the part according to the set rehabilitation part.

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