CN114169366A

CN114169366A - Neurofeedback training system and method

Info

Publication number: CN114169366A
Application number: CN202111399690.9A
Authority: CN
Inventors: 李小俚; 陈贺; 张昊
Original assignee: Beijing Normal University
Current assignee: Beijing Normal University
Priority date: 2021-11-19
Filing date: 2021-11-19
Publication date: 2022-03-11
Anticipated expiration: 2041-11-19
Also published as: CN114169366B

Abstract

The present disclosure provides a neurofeedback training system and method, the system comprising: the brain electrical signal acquisition device is used for acquiring a first task brain electrical signal of the training object. The processing device is used for acquiring a neural feedback training scheme of the training object, wherein the neural feedback training scheme comprises a filtering frequency band, an electroencephalogram characteristic index and an excitation condition of the set brain rhythm of the training object; the electroencephalogram feature extraction model is used for configuring the training object according to the filtering frequency band and the electroencephalogram feature index; the electroencephalogram characteristic extraction model is used for inputting the first task electroencephalogram signal to the electroencephalogram characteristic extraction model to obtain an index value of the electroencephalogram characteristic index; and acquiring incentive information and displaying the incentive information to the training object when the index value satisfies the incentive condition.

Description

Neurofeedback training system and method

Technical Field

The embodiment of the application relates to the technical field of neural regulation, in particular to a neural feedback training system and method.

Background

Neurofeedback training is one type of biofeedback training. It monitors the activity state of brain in real time, such as brain wave, blood oxygen content of brain, etc. and gives the trainee proper sound, image, touch and other feedback with the assistance of computer system, so that the trainee can change the activity state of brain by subjectively and consciously changing his own biological signal, strengthen the self-regulation capacity of brain and improve the function of brain.

The existing neural feedback training system is used for training different training objects based on a set of fixed neural feedback training scheme, and the training effect of the neural feedback training is poor due to the fact that targeted training cannot be performed on a trainer.

Disclosure of Invention

The embodiment of the application aims to provide a neural feedback training system and method, which can solve the problem of poor training effect of neural feedback training.

In order to solve the technical problem, the present application is implemented as follows:

in a first aspect, an embodiment of the present application provides a neural feedback training system, including:

the electroencephalogram signal acquisition device and the processing device; the electroencephalogram signal acquisition device is connected with the processing device;

the electroencephalogram signal acquisition device is used for acquiring a first task electroencephalogram signal of a training object and sending the first task electroencephalogram signal to the processing device; the first task electroencephalographic signal is an electroencephalographic signal when the training subject performs a training task;

the processing device is used for acquiring a neural feedback training scheme of the training object, wherein the neural feedback training scheme comprises a filtering frequency band for setting a brain rhythm of the training object, an electroencephalogram characteristic index for performing neural feedback training and an excitation condition, and the electroencephalogram characteristic index is used for reflecting a brain function state set by the training object; the electroencephalogram feature extraction model is used for configuring an electroencephalogram feature extraction model of the training object according to the filtering frequency band and the electroencephalogram feature index, wherein the electroencephalogram feature extraction model reflects the mapping relation between the electroencephalogram signal of the training object and the index value of the electroencephalogram feature index; the electroencephalogram feature extraction model is used for inputting the first task electroencephalogram signal to the electroencephalogram feature extraction model to obtain an index value of the electroencephalogram feature index; and the system is used for acquiring incentive information and displaying the incentive information to the training object under the condition that the index value meets the incentive condition.

Optionally, the processing device comprises:

the system comprises a scheme design module, a feedback presentation module, an electroencephalogram feature extraction module and a display module;

the scheme design module is used for acquiring the neural feedback training scheme and sending the neural feedback training scheme to the feedback presentation module;

a first signal input end of the feedback presentation module is connected with the scheme design module to receive the neural feedback training scheme, configure excitation conditions according to the neural feedback training scheme, and generate a configuration file of the electroencephalogram feature extraction model according to the filtering frequency band and the electroencephalogram feature index; a first signal output end of the feedback presentation module is connected with the electroencephalogram feature extraction module so as to send the configuration file to the electroencephalogram feature extraction module; a second signal input end of the feedback presentation module is connected with the electroencephalogram feature extraction module so as to receive the index value output by the electroencephalogram feature extraction module, and a second signal output end of the feedback presentation module is connected with the display module; the feedback presentation module is used for judging whether the received index value meets the excitation condition, acquiring the excitation information under the condition that the index value meets the excitation condition, and sending the excitation information to the display module;

the display module is used for displaying the incentive information to the training object;

the electroencephalogram feature extraction module configures the electroencephalogram feature extraction model according to the configuration file under the condition that the configuration file is received; the second signal input end of the electroencephalogram feature extraction module is connected with the electroencephalogram signal acquisition device to receive the first task electroencephalogram signal, and the electroencephalogram feature extraction module is used for inputting the received first task electroencephalogram signal to the electroencephalogram feature extraction model to obtain the index value and sending the index value to the feedback presentation module.

Optionally, the processing apparatus further includes a filtering frequency band obtaining module;

the electroencephalogram signal acquisition device is also used for acquiring a first eye-closing electroencephalogram signal of the training object; the first eye-closing electroencephalogram signal is acquired within a set time length and is an electroencephalogram signal when the training object keeps an eye-closing state;

the signal input end of the filtering frequency band acquisition module is connected with the electroencephalogram signal acquisition device so as to receive the first eye-closing electroencephalogram signal; the signal output end of the filtering frequency band acquisition module is connected with the scheme design module; the filtering frequency band obtaining module is used for obtaining the individualized alpha peak frequency of the training object according to the first eye-closing electroencephalogram signal; obtaining the filtering frequency band according to the individualized alpha peak value frequency, and sending the filtering frequency band to the scheme design module;

the scheme design module is further used for generating the neural feedback training scheme according to the filtering frequency band, the preset electroencephalogram characteristic index and the excitation condition.

Optionally, the processing device further comprises a training assessment module;

the signal input end of the training evaluation module is connected with the electroencephalogram feature extraction module so as to collect a plurality of index values obtained in a neural feedback training process, and a training evaluation report is generated according to the index values; and the signal output end of the training evaluation module is connected with the display module so as to send the training evaluation report to the display module for display.

Optionally, the neurofeedback training protocol further comprises a first acquisition channel identifier for identifying a planned acquisition channel for labeling the first task electroencephalogram signal; the first task electroencephalogram signal comprises a second acquisition channel identifier, and the second acquisition channel identifier is used for marking an actual acquisition channel of the first task electroencephalogram signal;

the feedback presenting module is further configured to send the first acquisition channel identifier to the electroencephalogram feature extraction module;

the electroencephalogram feature extraction module is used for detecting whether the first acquisition channel identifier is consistent with the second acquisition channel identifier or not after receiving the first task electroencephalogram signal; under the condition that the first acquisition channel identification is consistent with the second acquisition channel identification, inputting the first task brain electrical signal into the brain electrical characteristic extraction model; acquiring prompt information under the condition that the first acquisition channel identifier is inconsistent with the second acquisition channel identifier; the electroencephalogram feature extraction module is connected with the display module to send the prompt information to the display module for display.

In a second aspect, an embodiment of the present application provides a neural feedback training method, including:

acquiring a neural feedback training scheme of a training object; the neural feedback training scheme comprises a filtering frequency band of a set brain rhythm of the training object, an electroencephalogram characteristic index for neural feedback training and an excitation condition; the electroencephalogram characteristic index is used for reflecting the brain function state set by the training object;

configuring an electroencephalogram feature extraction model of the training object according to the filtering frequency band and the electroencephalogram feature index; the electroencephalogram feature extraction model reflects the mapping relation between the electroencephalogram signal of the training object and the index value of the electroencephalogram feature index;

acquiring a first task electroencephalogram signal of a training subject; the first task electroencephalographic signal is an electroencephalographic signal when the training subject performs a training task;

inputting the first task brain electrical signal into the brain electrical characteristic extraction model to obtain an index value of the brain electrical characteristic index;

acquiring excitation information under the condition that the index value meets the excitation condition;

displaying the motivational information to the training subjects.

Optionally, before acquiring the neurofeedback training scheme, the method further includes:

acquiring a first eye-closing electroencephalogram signal of the training object; the first eye-closing electroencephalogram signal is acquired within a set time length and is an electroencephalogram signal when the training object keeps an eye-closing state;

obtaining the individualized alpha peak frequency of the training object according to the first eye-closing electroencephalogram signal;

obtaining the filtering frequency band according to the individualized alpha peak value frequency;

and acquiring the neural feedback training scheme according to the filtering frequency band, the preset electroencephalogram characteristic index and the excitation condition.

Optionally, the first eye-closing electroencephalogram signal at least comprises an electroencephalogram signal of an O1 channel and an electroencephalogram signal of an O2 channel; obtaining the individualized alpha peak frequency of the training object according to the first eye-closing electroencephalogram signal, wherein the obtaining comprises the following steps:

preprocessing the first eye-closing electroencephalogram signal to obtain a second eye-closing electroencephalogram signal;

acquiring the electroencephalogram signal of the O1 channel in the second eye-closing electroencephalogram signal, and taking the alpha peak frequency of the electroencephalogram signal of the O1 channel as a first alpha peak frequency;

acquiring the electroencephalogram signal of the O2 channel in the second eye-closing electroencephalogram signal, and taking the alpha peak frequency of the electroencephalogram signal of the O2 channel as a second alpha peak frequency;

and obtaining the individualized alpha peak frequency of the training object according to the first alpha peak frequency and the second alpha peak frequency.

Optionally, the method further comprises:

acquiring at least two index values;

and generating a training evaluation report according to at least two index values.

the method further comprises the following steps:

detecting whether the first acquisition channel identifier is consistent with the second acquisition channel identifier;

under the condition that the first acquisition channel identification is consistent with the second acquisition channel identification, inputting the first task brain electrical signal into the brain electrical characteristic extraction model;

acquiring prompt information under the condition that the first acquisition channel identifier is inconsistent with the second acquisition channel identifier;

and sending the prompt information to a display module for display.

The neural feedback training system in the embodiment of the application can configure the electroencephalogram feature extraction model and the excitation condition according to the acquired neural feedback training scheme of the training object, namely, in the neural feedback training process, the training object is trained on the basis of the electroencephalogram feature extraction model and the excitation condition aiming at the training object, the training pertinence is improved, and then the training effect of the neural feedback training is improved.

Other features of the present description and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the specification and together with the description, serve to explain the principles of the specification.

FIG. 1 is a schematic block diagram of a neurofeedback training system according to an embodiment;

FIG. 2 is a schematic block diagram of a processing device according to one embodiment;

FIG. 3 is a schematic block diagram of a processing device according to another embodiment;

FIG. 4 is a schematic block diagram of a processing device according to yet another embodiment;

FIG. 5 is a flow diagram of a neural feedback training method, according to one embodiment.

Detailed Description

Various exemplary embodiments of the present application will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.

In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.

It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.

< System embodiment >

FIG. 1 is a schematic block diagram of a neurofeedback training system according to one embodiment.

As shown in fig. 1, the neurofeedback training system includes a brain electrical signal acquisition device 1000 and a processing device 2000. The electroencephalogram signal acquisition device 1000 and the processing device 2000 may be connected through a wired network or a wireless network, and are not limited herein.

In this embodiment, the electroencephalogram signal acquisition device 1000 is configured to execute step S103, and acquire a first task electroencephalogram signal of a training subject. For executing step S104, the first task brain electrical signal is sent to the processing device 2000.

In the course of performing neurofeedback training, a training subject is required to perform a set training task. The training task may be set in advance according to the brain function state of training. For example, in case the trained brain function state is an attention state, a visual search task may be selected as the training task.

In this embodiment, during the period when the training subject executes the set training task, the electroencephalogram signal of the training subject is acquired as the first task electroencephalogram signal by the electroencephalogram signal acquisition device 1000.

In one embodiment, the brain electrical signal acquisition device 1000 includes a multi-channel brain electrical acquisition cap. The multi-channel electroencephalogram acquisition cap comprises a plurality of acquisition electrodes so as to acquire electroencephalogram signals of different brain areas through a plurality of acquisitions.

In this embodiment, the first task brain electrical signal comprises a plurality of channels of brain electrical signals. The electroencephalogram signal of each channel corresponds to the electroencephalogram signals of different electroencephalogram areas.

In this embodiment, the electroencephalogram signal acquisition device 1000 may send the acquired first task electroencephalogram signal to the processing device 2000 in real time, or send the first task electroencephalogram signal to the processing device 2000 according to a preset frequency, which is not limited herein.

In this embodiment, the processing device 2000 is configured to execute step S101 to obtain a neurofeedback training scheme for a training subject. The neural feedback training scheme comprises a filtering frequency band for setting a brain rhythm of a training object, an electroencephalogram characteristic index for neural feedback training and an excitation condition.

Different brain rhythm characteristics reflect different brain functional states. For example, the relative energy of alpha rhythm in the electroencephalogram signal reflects the attention state of an individual, and the higher the relative energy of alpha rhythm, the better the attention state of the corresponding individual.

When training the set brain function state, it is necessary to extract a set brain rhythm corresponding to the set brain function state from the electroencephalogram signal, and use an index reflecting the set brain rhythm characteristic as an electroencephalogram characteristic index for performing neural feedback training. The electroencephalogram characteristic index can reflect the set brain function state of the training object, so that the change of the set brain function state of the training object can be obtained by tracking the index value change of the electroencephalogram characteristic index in the neural feedback training process. For example, when the brain function state is set as an attention state and neural feedback training is performed on the attention state of a training target, an alpha rhythm related to the attention state is extracted from an electroencephalogram signal, and alpha rhythm relative energy reflecting characteristics of the alpha rhythm is used as an electroencephalogram characteristic index for performing the neural feedback training.

Different brain rhythms have different frequency ranges, so the set brain rhythm can be extracted from the electroencephalogram signals through a filter with a filtering frequency band consistent with the set brain rhythm frequency range. For example, the frequency range of alpha rhythm is 7-14HZ, and the alpha rhythm can be extracted from the EEG signal by a filter with the filtering frequency range of 7-14 HZ.

In the course of neurofeedback training, an excitation condition is set in order to guide a training subject to actively perform adjustment for setting a brain rhythm. And when the index value of the electroencephalogram characteristic index meets the excitation condition, namely the set brain function state meets the set requirement, feeding back excitation information to the training object. In this way, the training subject actively adjusts the set brain rhythm to acquire the excitation information, thereby realizing the adjustment of the set brain function state.

The incentive information may be reward points that may be accumulated. After training is finished, the training subject can exchange the reward value for the corresponding reward, and the higher the reward value is, the higher the redeemable reward value is. In this way, the training subject can actively adjust and set the brain rhythm to obtain a higher value reward, thereby improving the training efficiency of the neural feedback. The motivational information may be textual information indicating approval, such as "very Bar! ". The incentive information may also be image information indicating an incentive, for example, "enlarged task window", and the incentive information may also be other information indicating an incentive, which is not particularly limited herein.

In one embodiment, the excitation condition includes a positive excitation condition and a negative excitation condition, and the corresponding excitation information includes positive excitation information and negative excitation information. The positive incentive information is information indicating an incentive, and the negative incentive information is information indicating a penalty.

In this embodiment, when the index value of the electroencephalogram feature index satisfies the positive excitation condition, that is, when the set brain function state satisfies the set requirement, the positive excitation information is displayed to the training subject. And displaying negative excitation information to the training object under the condition that the index value of the electroencephalogram characteristic index meets the negative excitation condition, namely the set brain function state does not meet the set requirement. In this way, the training subject can actively adjust the set brain rhythm in order to acquire the positive stimulation information and avoid the negative stimulation information, thereby realizing the adjustment of the set brain function state.

Different brain functional states correspond to different training directions, for example, some brain functional states need to be improved, some brain functional states need to be reduced, and some brain functional states need to be maintained within a set range.

In one embodiment, to meet the training direction requirements of different brain functional states, the excitation conditions are classified into three categories, namely, ascending excitation conditions, descending excitation conditions and maintaining excitation conditions.

In this embodiment, in the ascending excitation condition, the positive excitation condition is that the index value of the electroencephalogram characteristic index is greater than the set upper threshold, and the negative excitation condition is that the index value of the electroencephalogram characteristic index is less than the set lower threshold. In the descending excitation condition, the negative excitation condition is that the index value of the electroencephalogram characteristic index is larger than a set upper limit threshold, and the positive excitation condition is that the index value of the electroencephalogram characteristic index is smaller than a set lower limit threshold. In the keep-alive condition, the positive-excitation condition is that the index value of the electroencephalogram characteristic index is greater than or equal to a set lower-limit threshold and less than or equal to a set upper-limit threshold, and the negative-excitation condition is that the index value of the electroencephalogram characteristic index is greater than the set upper-limit threshold or less than the set lower-limit threshold. Wherein the set upper threshold is greater than the set lower threshold.

In this embodiment, the processing device 2000 is further configured to execute step S102, and configure an electroencephalogram feature extraction model of the training object according to the filtering frequency band and the electroencephalogram feature index. The electroencephalogram feature extraction model reflects the mapping relation between the electroencephalogram signal of the training object and the index value of the electroencephalogram feature index. And the step S105 is executed, the first task brain electrical signal is input to the brain electrical characteristic extraction model, and the index value of the brain electrical characteristic index is obtained. For executing step S106, when the index value satisfies the incentive condition, incentive information is acquired and displayed to the training subject.

In this embodiment, the processing device 2000 may be a mobile phone, a tablet computer, a notebook computer, or a desktop computer, and is not limited specifically herein.

In one embodiment, the processing device 2000, as shown in fig. 2, includes: the system comprises a scheme design module 2100, a feedback presentation module 2200, an electroencephalogram feature extraction module 2300 and a display module 2400.

In this embodiment, the scheme designing module 2100 is configured to execute step S201, acquire a neural feedback training scheme, and execute step S202 to send the neural feedback training scheme to the feedback presenting module 2200.

In this embodiment, a first signal input of the feedback presentation module 2200 is connected to the regimen design module 2100 for receiving a neurofeedback training regimen. The feedback presenting module 2200 is configured to, in a case that the neurofeedback training scheme is received, execute step S203, configure the excitation condition according to the neurofeedback training scheme; and the configuration file is used for executing the step S204 and generating a configuration file of the electroencephalogram feature extraction model according to the filtering frequency band and the electroencephalogram feature index.

In one embodiment, the electroencephalogram feature extraction module is provided with a filtering model and a plurality of index value calculation models. The index value calculation models may include a brain rhythm relative energy calculation model, a coherence calculation model, a complexity calculation model, and the like, which are not limited in detail herein.

In the embodiment, the electroencephalogram feature extraction module is provided with a perfect data interface, and a new index value calculation model can be added according to specific requirements so as to adapt to the requirements of various neural feedback training.

In this embodiment, the feedback presenting module 2200 generates a subscription instruction of the corresponding index value calculation model according to the electroencephalogram characteristic index in the neurofeedback training scheme. For example, when the electroencephalogram characteristic index acquired by the feedback presenting module 2200 is alpha rhythm relative energy, a subscription instruction of an index value calculation model corresponding to the alpha rhythm relative energy is generated, and when the index value calculation model is subscribed through the subscription instruction, corresponding index value calculation operation is executed.

In this embodiment, the feedback presenting module 2200 takes the subscription instruction and the filtering frequency band as a configuration file of the electroencephalogram feature extraction model.

In this embodiment, the first signal output end of the feedback presenting module 2200 is connected to the electroencephalogram feature extraction module 2300 to send the configuration file to the electroencephalogram feature extraction module 2300, so as to implement personalized configuration of the electroencephalogram feature extraction model, and enable the configured electroencephalogram feature extraction model to perform targeted electroencephalogram feature extraction on a training object.

In this embodiment, a second signal input end of the feedback presenting module 2200 is connected to the electroencephalogram feature extraction module 2300 to receive the index value output by the electroencephalogram feature extraction module 2300, and a second signal output end of the feedback presenting module 2200 is connected to the display module 2400. The feedback presenting module 2200 is configured to determine whether the received index value meets an excitation condition, acquire excitation information when the index value meets the excitation condition, and send the excitation information to the display module.

In this embodiment, the display module 2400 is configured to execute step S211 to display incentive information to the training subject. The display module may be a liquid crystal display or a touch display, and is not limited herein.

In this embodiment, the electroencephalogram feature extraction module 2300, upon receiving the configuration file, executes step S205 to configure an electroencephalogram feature extraction model according to the configuration file.

In the embodiment where the configuration file includes a subscription instruction and a filtering frequency band, the electroencephalogram feature extraction module 2300 sets the filtering frequency band of the filter model according to the filtering frequency band in the configuration file, and subscribes the corresponding index value calculation model according to the subscription instruction, thereby obtaining an electroencephalogram feature extraction model for the training object.

In the embodiment, the electroencephalogram feature extraction model can be flexibly configured according to the configuration file, so that the electroencephalogram feature extraction model in the neural feedback training can accurately extract the index value of the electroencephalogram feature index of the training object, the neural feedback training based on the index value is more targeted, and the effectiveness of the neural feedback training is improved.

The second signal input end of the electroencephalogram feature extraction module 2300 is connected with the electroencephalogram signal acquisition device 1000 to receive the first task electroencephalogram signal, and the electroencephalogram feature extraction module 2300 is used for executing the step S207 to input the received first task electroencephalogram signal to the electroencephalogram feature extraction model to obtain an index value. For executing step S208, the index value is sent to the feedback presenting module 2200.

Research shows that the frequency distribution ranges of the set brain rhythms are different among different individuals and among different ages of the same individual, and therefore, the set brain rhythm extracted based on the fixed filtering frequency band cannot accurately reflect the set brain rhythm characteristics of the training object.

In one embodiment, the processing device 2000 is shown in fig. 3, and further includes a filtering frequency band obtaining module 2500, where the filtering frequency band obtaining module 2500 is configured to accurately obtain a filtering frequency band for setting a brain rhythm of a training subject.

In the distribution of the brain rhythm, the alpha rhythm is between the high-frequency rhythm and the low-frequency rhythm, so most of the dividing methods of the brain rhythm adopt the peak frequency of the alpha rhythm to anchor and set the filtering frequency band of the brain rhythm. For example, if the peak frequency of the alpha rhythm is iAPF, the filter band for alpha rhythm is 0.8iAPF-1.2IiAPF, the filter band for delta rhythm is 1-4Hz, the filter band for theta rhythm is 4-0.8 iAPF, the filter band for low beta rhythm is 1.2 iAPF-2 iAPF, the filter band for high beta rhythm is 2 iAPF-3 iAPF, and the filter band for gamma rhythm is 3 iAPF-70 Hz.

And because the alpha rhythm is obvious in the eye closing state, the electroencephalogram signal of the training object in the eye closing state can be acquired, and the individualized alpha rhythm peak frequency of the training object is calculated according to the eye closing electroencephalogram signal, so that the calculation accuracy of the individualized alpha rhythm peak frequency can be improved, and the calculation accuracy of the filtering frequency band is further improved.

In this embodiment, the electroencephalogram signal acquisition device 1000 acquires a first eye-closing electroencephalogram signal of a training subject. The first eye-closing electroencephalogram signal is an electroencephalogram signal which is acquired within a set time length and is obtained when the training object keeps an eye-closing state. The set time period may be set in advance according to an application scenario, for example, the set time period may be 120 s.

In this embodiment, the signal input terminal of the filtering frequency band obtaining module 2500 is connected to the electroencephalogram signal collecting device 1000 to receive the first closed-eye electroencephalogram signal. The signal output end of the filtering frequency band obtaining module 2500 is connected to the scheme designing module 2100. And the filtering frequency band obtaining module 2500 is configured to obtain an individualized alpha peak frequency of the training object according to the first eye-closing electroencephalogram signal. And obtaining a filtering frequency band according to the individualized alpha peak value frequency. The filtered frequency band is sent to scheme design module 2100.

In this embodiment, the scheme design module 2100 is further configured to generate the neural feedback training scheme according to a filtering frequency band, a preset electroencephalogram characteristic index, and an excitation condition. The electroencephalogram characteristic index for training is set according to the brain function state of the training, and the excitation condition is set according to the index value of the electroencephalogram characteristic index before the training of the training object, so that the individualized neural feedback training scheme for the training object can be generated through the scheme design module 2100.

In one embodiment, the processing device 2000 is shown in fig. 4, and further includes a training evaluation module 2600. The signal input end of the training and evaluating module 2600 is connected to the electroencephalogram feature extraction module 2300 to obtain at least two index values of the neurofeedback training process, and generate a training and evaluating report according to the at least two index values.

In an embodiment, the training evaluation module 2600 obtains the index value in real time during the neurofeedback training process, and generates a training evaluation report according to a change rate of the index value during the neurofeedback training process after the neurofeedback training result. For example, in attention-enhancing neurofeedback training, the relative energy of alpha rhythm is increased by 10% relative to that before training, and the attention of the training subject is advanced by 10% relative to that before training.

In this embodiment, a signal output terminal of the training assessment module 2600 is connected to the display module 2400 to send the training assessment report to the display module 2400 for display.

In the training process, the index value of the electroencephalogram characteristic index can change along with the adjustment of the brain function state set by the training object. At this time, the excitation condition in the neural feedback training scheme can be dynamically adjusted according to the change situation of the index value in the evaluation result, so that the training difficulty is maintained at an appropriate level, and the effectiveness of the neural feedback training is improved.

In order to meet the training requirements of different brain functional states, a plurality of electrode channels and reference modes can be arranged in the neural feedback training scheme. Wherein, the double-ear electrode (M1/M2) and the Cz channel are fixed, and the other 6 channels can be selected from 0-6 channels from 16 channels (F3, F4, Fz, F7, F8, T3, T4, C3, C4, T5, T6, P3, P4, Pz, O1 and O2) in a 10-20 system. The reference mode has 6 options: cz reference, binaural connection reference, ipsilateral reference, contralateral reference, left ear reference, and right ear reference. The combination of the electrode and reference mode can be selected according to specific training requirements in the design of the neural feedback scheme.

In one embodiment, the neurofeedback training protocol may further include a first acquisition channel identification that identifies a planned acquisition channel for labeling the first task brain electrical signals.

In this embodiment, a planned acquisition channel of the first task brain electrical signal may be set according to the specifically trained brain function state. Wherein each planned acquisition channel has a corresponding acquisition channel identifier. For example, in the neural feedback training for attention improvement, the planned acquisition channels of the first task electroencephalogram signal may be an a1 channel, an a2 channel, a Cz channel, an F3 channel, an F4 channel, a P3 channel, a P4 channel, an O1 channel, and an O2 channel, where a1, a2, Cz, F3, F4, P3, P4, O1, and O2 are first acquisition channel identifiers corresponding to the planned acquisition channels. A1, A2, Cz, F3, F4, P3, P4, O1 and O2 are brain area positions corresponding to the acquisition channel, and the brain area positions are positions calibrated in the international 10/20 system.

In this embodiment, the first task electroencephalogram signal acquired by the electroencephalogram signal acquisition device 1000 includes a second acquisition channel identifier, and the second acquisition channel identifier is used for marking an actual acquisition channel of the first task electroencephalogram signal.

Before neural feedback training, the actual acquisition channel of the electroencephalogram signal acquisition device 1000 needs to be set according to the planned acquisition channel, but in the setting process of the actual acquisition channel, the situation that the set actual acquisition channel is inconsistent with the planned acquisition channel is inevitable. Therefore, in one embodiment of the present application, the processing device 2000 may also be used to perform a consistency check of the acquisition channel.

In this embodiment, the feedback presenting module 2200 is further configured to send the first acquisition channel identifier in the neural feedback training scheme to the electroencephalogram feature extracting module 2300. The electroencephalogram feature extraction module 2300 is used for detecting whether the first acquisition channel identifier is consistent with the second acquisition channel identifier after receiving the first task electroencephalogram signal; and under the condition that the first acquisition channel identification is consistent with the second acquisition channel identification, inputting the first task brain electrical signal into the brain electrical characteristic extraction model. And under the condition that the first acquisition channel identifier is inconsistent with the second acquisition channel identifier, acquiring prompt information, and sending the prompt information to a display module 2400 connected with the electroencephalogram feature extraction module 2300 for display. Therefore, the staff can change the reference mode, the position of the collecting electrode and the like according to the prompt information, and the accuracy of the setting of the collecting channel is ensured.

In one embodiment, the processing device 2000 further comprises a data acquisition module.

In this embodiment, the signal input terminal of the data acquisition module is connected to the signal output terminal of the electroencephalogram signal acquisition device 1000 to receive the electroencephalogram signal. The signal output end of the data acquisition module is connected with the electroencephalogram feature extraction module 2300 so as to send the received electroencephalogram signal to the electroencephalogram feature extraction module.

The data acquisition module can draw the data waveform of the electroencephalogram signal in real time and adjust the display in the acquisition process of the electroencephalogram data.

The data acquisition module comprises various buttons corresponding to different waveform display modes, and can adjust the channel number, amplitude range, display width and the like of waveform display of the electroencephalogram signals according to the requirements of acquired data, and also can select whether to display reference lines. The waveform of the brain electrical signal is updated in a rolling covering mode, and the current position and the time of event triggering are marked by red lines.

The data acquisition module supports impedance detection, in the impedance detection process, the impedance data of the electrode of the electroencephalogram acquisition device can be displayed on the schematic diagram of the position of the scalp where the electrode is located in different colors, and in the preparation process of actual data acquisition, experimenters can visually see the condition of real-time impedance, so that the contact condition of the electrode and the skin can be adjusted in time.

The data acquisition module supports joint synchronous acquisition of video data besides receiving electroencephalogram data transmitted by the electroencephalogram acquisition device 1000. The method adopts a programming interface provided by an image processing library Accord (https:// githu. com/acord-net/frame /), and supports video recording with various resolutions and code rates and recording and image saving functions in the recording process. In the aspect of communication between the electroencephalogram acquisition device 1000 and the data collection module, two connection modes of wired connection and wireless connection are supported, wherein the wired connection mode adopts USB connection, and the wireless connection mode adopts WI-FI connection. The data acquisition module can support synchronous acquisition of a plurality of electroencephalogram acquisition devices 1000.

In the aspect of electroencephalogram data storage, the data acquisition module may support data storage in a European Data Format (EDF) file format, may also support data storage in an EDF plus file format, and may also support data storage in a BDF plus file format, which is not specifically limited herein.

The data acquisition module can be used for cooperating with other systems besides the acquisition, display and storage of own electroencephalogram and video data, so that various data acquisition requirements are met. Typical stimuli presentation such as in ERP studies is synchronized with brain electrical data. The data acquisition module supports two cooperation modes, external events are synchronized to electroencephalogram records and electroencephalogram data are forwarded to an external system, in the first cooperation, a certain port is monitored in a program, and the external events are transmitted into the electroencephalogram records through the port and are synchronized. In the second cooperation, the acquired electroencephalogram data is forwarded to a specified network address and port in real time through a network port, and real-time data transmission and sharing are achieved.

Besides the acquisition of the brain electricity, the recording of events in the neural feedback training process is also completed by the data acquisition module. By means of the peripheral cooperation function of the acquisition system, various events are sent to the acquisition module and synchronously recorded with the electroencephalogram data, such as the start and the end of feedback, the selection of a feedback scheme, the behavior change of a tested object and the like, and the subsequent data analysis is facilitated.

In one embodiment, the training assessment module 2600 can also be connected to a data acquisition module to receive brain electrical data stored by the data acquisition module to generate a training assessment report from the brain electrical data.

< method examples >

As shown in fig. 5, the neural feedback training method of the present embodiment may include steps S505 to S510.

And S505, acquiring a neural feedback training scheme of the training object. The neural feedback training scheme comprises a filtering frequency band for setting a brain rhythm of a training object, an electroencephalogram characteristic index for neural feedback training and an excitation condition; the electroencephalogram characteristic index is used for reflecting the brain function state set by the training object.

S506, configuring an electroencephalogram feature extraction model of the training object according to the filtering frequency band and the electroencephalogram feature index. The electroencephalogram feature extraction model reflects the mapping relation between the electroencephalogram signal of the training object and the index value of the electroencephalogram feature index.

And S507, acquiring a first task brain electrical signal of the training object. Wherein the first task electroencephalogram signal is an electroencephalogram signal when the training subject performs a training task.

And S508, inputting the first task electroencephalogram signal into the electroencephalogram feature extraction model to obtain an index value of the electroencephalogram feature index.

S509, when the index value satisfies the excitation condition, excitation information is acquired.

And S510, displaying the motivational information to the training object.

In one embodiment, before obtaining the neurofeedback training scheme, steps S501-S504 are also included.

S501, obtaining a first eye-closing electroencephalogram signal of a training object. The first eye-closing electroencephalogram signal is an electroencephalogram signal which is acquired within a set time length and is obtained when the training object keeps an eye-closing state.

S502, obtaining the individualized alpha peak frequency of the training object according to the first eye-closing electroencephalogram signal.

In one embodiment, the first eye-closed brain electrical signal comprises at least an O1 channel brain electrical signal and an O2 channel brain electrical signal. Step S502 specifically includes S502-1 to 502-5.

S502-1, preprocessing the first eye-closing electroencephalogram signal to obtain a second eye-closing electroencephalogram signal.

In this embodiment, the step of preprocessing the first eye-closing electroencephalogram signal includes S502-1-1 to S502-1-3.

S502-1-1, segmenting the electroencephalogram signals of the channels according to a preset segmentation rule to obtain a plurality of electroencephalogram signal segments corresponding to the channels.

The preset segmentation rule may be as follows: the electroencephalogram signal is divided into electroencephalogram signal segments with the length of 4s in a 2s overlapping mode. Taking the segmentation of the electroencephalogram signal corresponding to the Cz channel as an example, a first segmentation rule is explained: extracting the eye-closing computer signals of 0s-4s from the eye-closing electroencephalogram signals corresponding to the Cz channel to serve as electroencephalogram signal segments of a first time period; and extracting the eye-closure electroencephalogram signals of 2s-6s as an electroencephalogram signal segment of a second time period, extracting the eye-closure electroencephalogram signals of 4s-8s as an electroencephalogram signal segment of a third time period, and so on to obtain a plurality of electroencephalogram signal segments corresponding to the Cz channel. And segmenting the electroencephalogram signals of other channels by referring to an electroencephalogram signal segmentation method corresponding to the Cz channel to obtain a plurality of electroencephalogram signal segments corresponding to each channel.

The preset segmentation rule may also be set according to specific requirements, and is not specifically limited herein.

S502-1-2, screening and splicing the electroencephalogram signal segments with the lowest myoelectricity ratio and the set number in the plurality of electroencephalogram signal segments corresponding to the channels to obtain the effective electroencephalogram signals corresponding to the channels.

The step of obtaining the effective brain electrical signal corresponding to the Cz channel comprises the following steps: and carrying out normalization processing on a plurality of electroencephalogram signal segments corresponding to the Cz channel, and calculating the power spectrum of each electroencephalogram signal segment after normalization processing. Based on the power spectrum of each electroencephalogram signal segment, calculating the proportion of 40-100Hz energy to 1-100Hz energy as the myoelectricity proportion of each electroencephalogram signal segment. And sequencing according to the myoelectricity ratio of each electroencephalogram signal segment. And screening out the electroencephalogram signal segments with the lowest myoelectricity ratio and the set number for splicing to obtain the effective electroencephalogram signals corresponding to the Cz channels.

In this embodiment, the set number may be set in advance according to specific requirements, for example, the set number may be 40, that is, 40 electroencephalogram signal segments with the lowest myoelectricity occupation ratio are screened and spliced to obtain effective electroencephalogram signals corresponding to Cz channels.

And obtaining effective electroencephalogram signals corresponding to the channels by referring to a Cz channel processing method.

S502-1-3, obtaining a second eye-closing test electroencephalogram signal based on the effective electroencephalogram signal corresponding to each channel. Specifically, the effective electroencephalograms corresponding to the channels are combined to obtain a second eye-closing test electroencephalogram.

Alpha rhythms are most pronounced in the occipital region (i.e., the O1 and O2 positions). Therefore, the individualized alpha peak frequency may be accurately calculated from the electroencephalographic signals acquired at the O1 and O2 locations.

S502-2, acquiring an electroencephalogram signal of an O1 channel in the second eye-closing electroencephalogram signal, and taking the alpha peak frequency of the electroencephalogram signal of the O1 channel as a first alpha peak frequency.

S502-3, acquiring an electroencephalogram signal of an O2 channel in the second eye-closing electroencephalogram signal, and taking the alpha peak frequency of the electroencephalogram signal of the O2 channel as a second alpha peak frequency.

S502-4, obtaining the individualized alpha peak frequency of the training object according to the first alpha peak frequency and the second alpha peak frequency.

In this embodiment, the average value of the first alpha peak frequency and the second alpha peak frequency may be used as the individualized alpha peak frequency of the training subject, and the weighted average value of the first alpha peak frequency and the second alpha peak frequency may also be used as the individualized alpha peak frequency of the training subject, which is not limited herein.

S503, obtaining a filtering frequency range according to the individualized alpha peak value frequency.

For example, if the calculated individualized alpha peak frequency is iAPF, then the filter band for alpha rhythms may be 0.8iAPF-1.2IiAPF, the filter band for delta rhythms may be 1-4Hz, the filter band for theta rhythms may be 4-0.8 iAPF, the filter band for low beta rhythms may be 1.2 iAPF-2 iAPF, the filter band for high beta rhythms may be 2 iAPF-3 iAPF, and the filter band for gamma rhythms may be 3 iAPF-70 Hz.

And S504, acquiring a neural feedback training scheme according to the filtering frequency band, the preset electroencephalogram characteristic index and the excitation condition.

In one embodiment, the neurofeedback training method further comprises: acquiring at least two index values in a neural feedback training process; and generating a training evaluation report according to at least two index values.

In one embodiment, the neurofeedback training protocol further includes a first acquisition channel identifier that identifies a planned acquisition channel for labeling the first task brain electrical signal. The first task electroencephalogram signal comprises a second acquisition channel identifier, and the second acquisition channel identifier is used for marking an actual acquisition channel of the first task electroencephalogram signal.

In this embodiment, the neural feedback training method further includes: and detecting whether the first acquisition channel identifier is consistent with the second acquisition channel identifier. And under the condition that the first acquisition channel identification is consistent with the second acquisition channel identification, inputting the first task brain electrical signal into the brain electrical characteristic extraction model. And under the condition that the first acquisition channel identifier is inconsistent with the second acquisition channel identifier, acquiring prompt information, and sending the prompt information to the display module for displaying.

One or more embodiments of the present description may be a system, method, and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the specification.

The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.

The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.

The computer program instructions for carrying out operations for embodiments of the present description may be assembly instructions, Instruction Set Architecture (ISA) instructions, machine related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, an electronic circuit, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA), can execute computer-readable program instructions to implement various aspects of the present description by utilizing state information of the computer-readable program instructions to personalize the electronic circuit.

Aspects of the present description are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the description. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present description. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, implementation by software, and implementation by a combination of software and hardware are equivalent.

The foregoing description of the embodiments of the present specification has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the application is defined by the appended claims.

Claims

1. A neurofeedback training system, comprising:

2. The system of claim 1, wherein the processing means comprises:

3. The system of claim 2, wherein the processing device further comprises a filtered frequency band obtaining module;

4. The system of claim 2, wherein the processing device further comprises a training assessment module;

5. The system of claim 2, wherein the neurofeedback training protocol further comprises a first acquisition channel identification, the first acquisition channel identification identifying a planned acquisition channel for labeling the first task brain electrical signal; the first task electroencephalogram signal comprises a second acquisition channel identifier, and the second acquisition channel identifier is used for marking an actual acquisition channel of the first task electroencephalogram signal;

6. A neural feedback training method, comprising:

displaying the motivational information to the training subjects.

7. The method of claim 6, further comprising, prior to obtaining the neurofeedback training regimen:

8. The method of claim 7, wherein the first eye-closed brain electrical signal comprises at least an O1 channel brain electrical signal and an O2 channel brain electrical signal; obtaining the individualized alpha peak frequency of the training object according to the first eye-closing electroencephalogram signal, wherein the obtaining comprises the following steps:

9. The method of claim 6, further comprising:

acquiring at least two index values;

10. The method of claim 6, wherein the neurofeedback training protocol further comprises a first acquisition channel identification, the first acquisition channel identification identifying a planned acquisition channel for labeling the first task brain electrical signal; the first task electroencephalogram signal comprises a second acquisition channel identifier, and the second acquisition channel identifier is used for marking an actual acquisition channel of the first task electroencephalogram signal;

the method further comprises the following steps:

and sending the prompt information to a display module for display.