CN111920408A - Signal analysis method and component of electroencephalogram nerve feedback system combined with virtual reality - Google Patents
Signal analysis method and component of electroencephalogram nerve feedback system combined with virtual reality Download PDFInfo
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Abstract
The invention discloses a signal analysis method and a signal analysis component of an electroencephalogram neural feedback system combined with virtual reality. The method comprises the steps of collecting an electroencephalogram signal of a user in a resting state, and setting a training initial threshold according to an electroencephalogram signal analysis result in the resting state; acquiring an electroencephalogram signal generated in each training period, carrying out filtering processing, then calculating power spectral density by using a pwelch function to obtain an average value of the power spectral density of the electroencephalogram signal in a corresponding frequency band, taking the average value as a feedback value of the current training period, and adjusting a threshold value of the next training period according to the feedback value; and calculating a feedback training learning effect index of the current training, and selecting a proper scene state by the virtual reality system according to the index for the next training. The method has the advantages that the task difficulty is dynamically adjusted according to the current cognitive ability of the user by processing the electroencephalogram signals generated in the training period of the user in real time and giving a feedback value and adjusting the threshold value in real time according to the feedback value.
Description
Technical Field
The invention relates to the technical field of signal data processing, in particular to a signal analysis method and a signal analysis component of an electroencephalogram neural feedback system combined with virtual reality.
Background
The prior art provides an electroencephalogram neural feedback training system combined with virtual reality, which is a novel brain function training method, and the electroencephalogram neural feedback training system converts a certain characteristic of a measured brain function activity signal into a visual or auditory signal form to be presented to a subject, and requires the subject to selectively enhance or inhibit the signal characteristic, so that the aim of self-regulating brain function activity is fulfilled. The neural feedback can be used for improving the cognitive function, is an effective computer-assisted cognitive training method, and the electroencephalogram neural feedback can be used for improving the cognitive function of healthy people due to the characteristics of no wound, no side effect and the like, and can be gradually used for the cognitive function rehabilitation of patients, including various mental disease patients and patients with cognitive injury caused by brain injury. Neurofeedback training for frontal lobe area Theta brain waves can improve the performance and cognitive control ability of the trainee. If the neural feedback training of Alpha frequency range brain wave activity is reported, the working memory performance of a trainer can be improved. The neural feedback training aiming at SMR frequency range brain waves can also obviously improve the capability of the memory function. However, the existing neural feedback technology has some problems to be solved, such as single and uninteresting feedback form, lack of sense of reality and experience, and the existing neural feedback technology cannot dynamically adjust task difficulty according to the current cognitive ability of a subject to achieve a better training effect.
Disclosure of Invention
The invention aims to provide a signal analysis method, a signal analysis device, computer equipment and a storage medium of an electroencephalogram neural feedback system combined with virtual reality, and aims to solve the problem that the task difficulty cannot be dynamically adjusted according to the current cognitive ability of a subject by the existing neural feedback technology.
In a first aspect, an embodiment of the present invention provides a signal analysis method for an electroencephalogram neurofeedback system in combination with virtual reality, which includes:
collecting an electroencephalogram signal in a resting state of a user, and setting a training initial threshold according to an electroencephalogram signal analysis result in the resting state;
acquiring electroencephalogram signals generated in each training period, carrying out filtering processing through Butterworth band-pass filtering, then calculating power spectral density by using a pwelch function to obtain an average value of power spectral density of electroencephalogram signals of corresponding frequency bands for feedback training, taking the electroencephalogram signals of the corresponding frequency bands for the feedback training as electroencephalogram signals of target frequency bands, taking the average value of the power spectral density of the electroencephalogram signals of the target frequency bands as a feedback value of the training period at the time, and adjusting a threshold value of the next training period according to the feedback value;
the feedback value of each training period is converted and then transmitted to the virtual reality system for controlling the change of the training scene selected by the virtual reality system;
and calculating a feedback training learning effect index of the current training, and selecting a proper scene state by the virtual reality system according to the index for the next training of the user.
In a second aspect, an embodiment of the present invention provides a signal analysis apparatus of an electroencephalogram neurofeedback system in combination with virtual reality, including:
the setting unit is used for acquiring the electroencephalogram signals of the user in the resting state and setting a training initial threshold according to the electroencephalogram signal analysis result in the resting state;
the processing unit is used for acquiring electroencephalogram signals generated in each training period, filtering the electroencephalogram signals through Butterworth band-pass filtering, calculating power spectral density by using a pwelch function to obtain an average value of power spectral density of the electroencephalogram signals of the corresponding frequency band aimed by feedback training, taking the electroencephalogram signals of the corresponding frequency band aimed by the feedback training as electroencephalogram signals of a target frequency band, taking the average value of the power spectral density of the electroencephalogram signals of the target frequency band as a feedback value of the current training period, and adjusting a threshold value of the next training period according to the feedback value;
the feedback unit is used for transmitting the feedback value of each training period to the virtual reality system after conversion, and the virtual reality system generates corresponding scene state change feedback to be presented to a user;
and the selection unit is used for calculating the feedback training learning effect index of the current training, and selecting a proper scene state by the virtual reality system according to the index for the next training of the user.
In a third aspect, an embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor, when executing the computer program, implements the signal analysis method of the virtual reality-combined electroencephalogram neurofeedback system according to the first aspect.
In a fourth aspect, the embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and the computer program, when executed by a processor, causes the processor to execute the signal analysis method of the virtual reality-combined electroencephalogram neurofeedback system according to the first aspect.
The invention discloses a signal analysis method and a signal analysis component of an electroencephalogram neural feedback system combined with virtual reality. The method comprises the steps of collecting an electroencephalogram signal of a user in a resting state, and setting a training initial threshold according to an electroencephalogram signal analysis result in the resting state; acquiring an electroencephalogram signal generated in each training period, carrying out filtering processing, then calculating power spectral density by using a pwelch function to obtain an average value of the power spectral density of the electroencephalogram signal in a corresponding frequency band, taking the average value as a feedback value of the current training period, and adjusting a threshold value of the next training period according to the feedback value; and calculating a feedback training learning effect index of the current training, and selecting a proper scene state by the virtual reality system according to the index for the next training. The method has the advantages that the task difficulty is dynamically adjusted according to the current cognitive ability of the user by processing the electroencephalogram signals generated in the training period of the user in real time and giving a feedback value and adjusting the threshold value in real time according to the feedback value.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a signal analysis method of an electroencephalogram neurofeedback system in combination with virtual reality according to an embodiment of the present invention;
FIG. 2 is a schematic sub-flow chart of a signal analysis method of an electroencephalogram neural feedback system in combination with virtual reality according to an embodiment of the present invention;
FIG. 3 is a schematic view of another sub-flow of a signal analysis method of an electroencephalogram neurofeedback system in combination with virtual reality according to an embodiment of the present invention;
fig. 4 is a schematic flowchart of a signal analysis method of an electroencephalogram neurofeedback system in combination with virtual reality according to an embodiment of the present invention;
FIG. 5 is a schematic view of another sub-flow of a signal analysis method of an electroencephalogram neurofeedback system in combination with virtual reality according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a relative power value of the electroencephalogram signal of the target frequency band in each period in 1 training according to the embodiment of the present invention;
FIG. 7 is a schematic diagram of a relative power value of an electroencephalogram signal of a target frequency band in each period of n consecutive training sessions according to an embodiment of the present invention;
FIG. 8 is a schematic block diagram of a signal analysis method of an electroencephalogram neurofeedback system in combination with virtual reality according to an embodiment of the present invention;
FIG. 9 is a schematic block diagram of a signal analysis device of a virtual reality-combined electroencephalogram neural feedback system according to an embodiment of the present invention;
FIG. 10 is a schematic block diagram of a computer device provided by an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
Referring to fig. 1, fig. 1 is a flowchart of a signal analysis method of an electroencephalogram neural feedback system in combination with virtual reality according to an embodiment of the present invention;
as shown in fig. 1, the method includes steps S101 to S104.
S101, collecting the electroencephalogram signals of the user in the resting state, and setting a training initial threshold according to the electroencephalogram signal analysis result in the resting state.
In the embodiment, before training, the electroencephalogram signal in the resting state when the eyes of a user (namely, a subject) are opened is acquired through the electroencephalogram signal acquisition system, the electroencephalogram signal in the resting state is used as a reference for setting the training starting threshold, and unreasonable changes of the situation during starting training along with training can be avoided by setting the starting threshold, so that the training effect cannot be presented.
Specifically, a user wears a 64-lead wet electrode electroencephalogram cap and reduces impedance to be lower than 20K omega, then wears a visual device VIVE outside the 64-lead wet electrode electroencephalogram cap, and then connects an electroencephalogram signal acquisition system with a virtual reality system (used for receiving signals transmitted by the electroencephalogram signal acquisition system and feeding back the signals to the user) through a local area network, so that electroencephalogram signal acquisition can be started; specifically, firstly, collecting electroencephalogram signals of a user in a resting state through a sampling frequency of 1000 Hz; in the acquisition process, instructing a user to concentrate on watching the cross presented in the middle of the screen so as to reduce eye movement interference, then processing the acquired electroencephalogram signals and obtaining an average value of power spectral densities of the brain electric signals of the target frequency band corresponding to the resting state, and finally setting a training starting threshold according to the average value of the power spectral densities of the brain electric signals of the target frequency band corresponding to the resting state.
S102, collecting electroencephalogram signals generated in each training period, filtering the electroencephalogram signals through Butterworth band-pass filtering, calculating power spectral density by using a pwelch function, obtaining an average value of power spectral density of the electroencephalogram signals of the corresponding frequency band aimed by feedback training, taking the electroencephalogram signals of the corresponding frequency band aimed by the feedback training as electroencephalogram signals of a target frequency band, taking the average value of the power spectral density of the electroencephalogram signals of the target frequency band as a feedback value of the current training period, and adjusting a threshold value of the next training period according to the feedback value.
In the embodiment, after the electroencephalogram signals in a resting state are collected and the training starting threshold value is set, a user starts a plurality of training periods, and a short rest can be performed between each training period according to requirements; in each training period, the user can see different virtual reality scenes, each virtual reality scene is in a constantly changing process, and the process is also a process for displaying the fluctuation of the electroencephalogram signals of the user in real time.
After training, the electroencephalogram signal acquisition system acquires an electroencephalogram signal generated in each training period, then filters the electroencephalogram signal in each training period through Butterworth band-pass filtering, calculates the power spectral density of the electroencephalogram signal in each training period after filtering by utilizing a pwelch function (for performing spectral estimation on the signal), obtains the power spectral density of the electroencephalogram signal in a corresponding frequency band in each training period, then calculates the average value of the power spectral density of the electroencephalogram signal in the corresponding frequency band, takes the average value as the feedback value of the current training period, and adjusts the threshold of the next training period according to the feedback value of the current training period, namely, adjusts the threshold of the second training period according to the feedback value of the first training period, so that the height of the threshold of the next training period can be selectively adjusted according to the state of a user, thereby realizing the adjustment of the difficulty of the personalized dynamic task.
In one embodiment, as shown in fig. 2, the step S102 includes:
s201, acquiring an electroencephalogram signal with a window length of 10S and a step length of 0.1S as a unit;
s202, filtering the electroencephalogram signals through Butterworth band-pass filtering, wherein the frequency band of a filtering pass band is 1-40 Hz;
s203, calculating the power spectral density of the electroencephalogram signal after filtering processing by using a pwelch function;
s204, extracting energy signals of a frequency band corresponding to the electroencephalogram signals of the target frequency band, and solving an average value of power spectral densities of the electroencephalogram signals of the corresponding frequency band;
s205, taking the average value of the power spectral density of the corresponding frequency band electroencephalogram signals as a feedback value of the current training period, and adjusting the threshold value of the next training period according to the feedback value.
In the embodiment, the electroencephalogram signal generated in one training period is collected and processed, firstly, the electroencephalogram signal with the window length of 10s and the step length of 0.1s in the current training period is obtained, then, the current electroencephalogram signal is filtered through a filter to obtain the electroencephalogram signal with the frequency band of 1-40 Hz, wherein the filter can be a Butterworth band-pass filter or other filters, then, the power spectral density of the electroencephalogram signal after filtering is calculated by using a pwelch function, and then, the energy signal of the electroencephalogram signal with the target frequency band, the energy signal with the eye wave band of 4-7 Hz and the energy signal with the myoelectricity wave band of 21-35 Hz are extracted from the feedback signal of a feedback channel electrode; and calculating the average value of the power spectral density of the corresponding frequency band electroencephalogram signals; and then taking the obtained average value of the power spectral density of the electroencephalogram signals in the corresponding frequency band as a feedback value of the current training period, wherein the feedback value comprises the average value of the power spectral density of the electroencephalogram signals in the target frequency band, the average value of the power spectral density of the electro-ocular wave band and the average value of the power spectral density of the electromyogram wave band, and adjusting the threshold value of the next training period according to the feedback value.
In this embodiment, the threshold of the next training period is adjusted according to the power spectral density average value of the electroencephalogram signal in the target frequency band in the feedback value of each training period, after the threshold is adjusted, the difficulty of the next training period can be changed, an increase in the threshold indicates an increase in difficulty, a decrease in the threshold indicates a decrease in difficulty, and the specific adjustment process needs to be adjusted in combination with the state selectivity of the user; specifically, if the average value of the power spectral density of the brain electrical signal of the target frequency band in the current training period of the user is higher than the threshold, the threshold in the next training period can be increased to enhance the task difficulty, and if not, the threshold in the next training period is decreased to reduce the task difficulty, so that personalized dynamic task difficulty adjustment is realized.
It should be further noted that when the threshold of the next training period is adjusted, different quantitative indexes can be selected as the adjustment basis according to needs. The reasons why the SMR power spectral density is the training indicator are: SMR is related to information processing and memory functions; the strongest signal, belonging to the central scalp region of the sensorimotor cortex, is generated in the thalamocortical network, occurs when resting and maintaining attention, and is suppressed during locomotion; in SMR, sensory information is conducted to the cortex to be attenuated or suppressed, thereby reducing sensorimotor interference and improving cognitive ability, and SMR upregulation can result in specific improvements in declarative memory performance, so that users are also required to maintain the SMR power spectral density average above a threshold during these training periods. Selecting alpha as the basis of the training index: alpha is associated with cognitive and memory functions, and alpha neurofeedback has a positive impact on cognitive and memory enhancement as well as clinical treatment. beta theta ratio (BTR for short) is used as the basis of the training index: the BTR is enhanced at different electrode positions through the neural feedback training, which is greatly helpful for the effective treatment of different diseases such as hyperactivity, reading disorder, body balance problem and the like, wherein beta is 15-18 Hz, and theta is 4-7 Hz.
And S103, converting the feedback value of each training period and transmitting the converted feedback value to the virtual reality system for controlling the change of the training scene selected by the virtual reality system.
In the embodiment, in order to enable a user to know feedback value conditions more intuitively, the feedback values are correspondingly converted, and specifically, when the feedback values exceed a set threshold, the electroencephalogram signal acquisition system uniformly converts the feedback values into the same digital signal and transmits the same digital signal to the virtual reality system; when the feedback value is lower than the set threshold value, the electroencephalogram signal acquisition system converts the feedback value into another same digital signal and transmits the digital signal to the virtual reality system, then the virtual reality system generates corresponding scene state change feedback to be presented to a user, and the specific scene comprises the change of the intensity of environmental sound, the change of scene definition and the like.
In one embodiment, as shown in fig. 3, the step S103 includes:
s301, comparing the average value of the power spectral density of the brain electrical signals of the target frequency band with a threshold value set in the training period;
s302, if the average value of the power spectral density of the brain electrical signals in the target frequency band is higher than the threshold value of the training period, outputting a signal 1;
s303, if the average value of the power spectral density of the brain electrical signals in the target frequency band is lower than the threshold value of the training period, outputting a signal 0;
and S304, transmitting the output signal to the virtual reality system for controlling the change of the training scene selected by the virtual reality system.
In this embodiment, in the process of comparing the average value of the power spectral density of the electroencephalogram signal in the target frequency band with the training threshold set in the training period, when the average value of the power spectral density of the electroencephalogram signal in the target frequency band exceeds the set threshold, the electroencephalogram signal acquisition system uniformly converts the feedback value into the signal 1 and transmits the signal 1 to the virtual reality system, when the virtual reality system receives the signal 1, the generated virtual scene is developed from an undesirable state to an ideal direction, and the scene can reach an optimal state with the increase of the received signal '1', and is accompanied by a reward prompt tone; when the average value of the power spectral density of the brain electricity signals in the target frequency band is lower than a set threshold value, the brain electricity signal acquisition system converts the feedback value into a signal 0 and transmits the signal 0 to the virtual reality system, when the virtual reality system receives the signal 0, the generated virtual scene develops towards an undesirable state, and along with the increase of the received signal '0', the scene can reach a state which is the least ideal; what is embodied as a scene is changed from a very fuzzy state to a very clear state; or from a more clear state to a very fuzzy state; such as racing from slow to fast or from fast to slow.
In an embodiment, the method further comprises:
the method comprises the steps of collecting electroencephalogram signals of a baseline period, carrying out filtering processing through Butterworth band-pass filtering, then utilizing a pwelch function to calculate power spectral density, obtaining an average value of the power spectral density of the electroencephalogram signals of corresponding frequency bands, taking the average value as a feedback value of the baseline period, and adjusting a threshold value of a next training period according to the feedback value.
In each training, 12 cycles are set: 2 rest periods (rest state before and after training), a baseline period and 9 neurofeedback training periods, wherein the duration of each period can be set to 3min or other time; firstly, electroencephalogram signals in a resting state before training are collected, then a program is operated to collect baseline data, then nine periods of neural feedback training are collected, and electroencephalogram signals in an eye-opening resting state are collected again after the neural feedback training is finished.
The baseline period in this embodiment is also the first period after each training is started, and after the electroencephalogram signal of the baseline period is collected and processed (the processing procedure is the same as that of the electroencephalogram signal of each training period, and is not repeated here), a feedback value of the baseline period can be obtained, where the feedback value is used as a basis for adjusting the starting threshold value, and is also the first time threshold value is adjusted in each training, and after the threshold value is adjusted, the difficulty of the next task is also changed, which is more suitable for the training of the next training period of the user, so that the baseline period plays an important role in this embodiment, and the subsequent training period can present a better effect.
And S104, calculating a feedback training learning effect index of the current training, and selecting a proper scene state by the virtual reality system according to the index for the next training of the user.
In this embodiment, a feedback training learning effect index of the whole current training is calculated and obtained according to the training condition of each training period in the current training, and a suitable contextual state is selected by the virtual reality system according to the index for the training of the next training of the user (where the training includes 9 neurofeedback training periods), and the selection of the suitable (matching or corresponding) contextual state can better aim at the current cognitive ability of the user, so that the training effect is better.
In one embodiment, as shown in fig. 4, the method further comprises:
s401, collecting and preprocessing the electroencephalogram signals of each training period, and calculating to obtain the relative power value of the electroencephalogram signals of the target frequency band of each training period.
In this embodiment, after repeated training for many times, the data acquired during each training is processed and analyzed to obtain the relative power value of the electroencephalogram signal of the target frequency band corresponding to the electroencephalogram signal of each training period and evaluate the relative power value, so that a basis can be provided for selecting the difficulty of the next training.
In one embodiment, as shown in fig. 5, step S401 includes:
s501, filtering the electroencephalogram signal in each training period by using a 0.5-40 Hz band-pass filter and a 50Hz notch filter, and deleting abnormal data sections;
s502, removing ocular artifacts and myoelectric artifacts to finish preprocessing;
s503, dividing the preprocessed electroencephalogram signal data of each training period into a plurality of data sections, performing frequency domain analysis through Fourier transform, estimating by adopting a Welch power spectrum estimation method, and calculating frequency spectrum signals of corresponding frequency bands;
s504, calculating the relative power values of the electroencephalogram signals of the target frequency bands corresponding to the data bands of each training period according to the following formula:
relative power value of target frequency band electroencephalogram signal
HF and LF are maximum boundary values and minimum boundary values of corresponding frequency bands of the electroencephalogram signals of the target frequency band, P (i) is a frequency spectrum amplitude, and i is a frequency spectrum index;
s505, taking the median of the relative power values of the electroencephalograph signals of the target frequency bands of the multiple data segments as the relative power value of the electroencephalograph signals of the target frequency band of each training period;
in this embodiment, the electroencephalogram signals of each training period are preprocessed by a Letswave software toolbox in an MATLAB operating environment, and then the relative power values of the electroencephalogram signals of the target frequency band are calculated.
Specifically, signal data of 11 s-180 s (the duration of each training period is 3min) in the electroencephalogram signals of each training period are obtained, then the data are filtered through a 0.5-40 Hz band-pass filter and a 50Hz notch filter, and then the data are checked and abnormal data sections are deleted; then, removing the ocular and myoelectric artifacts by an independent component analysis method, thereby completing the preprocessing operation; dividing the preprocessed electroencephalogram signal data of each training period into a plurality of data segments of 1s, performing frequency domain analysis through Fourier transform, then selecting a Welch power spectrum estimation method (the number of fast Fourier transform points is 125, the overlap is 48%, and a Hamming window is selected) for estimation, and calculating the frequency spectrum signals of the corresponding frequency bands of the electroencephalogram signals of the target frequency band; substituting the maximum boundary HF of the frequency band corresponding to the electroencephalogram signal of the target frequency band, the minimum boundary value LF of the frequency band corresponding to the electroencephalogram signal of the target frequency band, the frequency spectrum amplitude P (i) and the frequency spectrum index i into a formula:
relative power value of target frequency band electroencephalogram signal
Calculating and normalizing relative power values of brain wave activity of all continuous and uninterrupted data segments of each training period by a log10 transformation method, selecting a median value of the relative power values of brain wave activity from the relative power values of brain wave activity of a plurality of data segments, and taking the median value of the relative power values of brain wave activity as the relative power value of brain wave activity of each training period.
S402, carrying out linear regression processing on the relative power value of the electroencephalogram signal of the target frequency band in each training period to obtain a slope and a difference between the relative power values of the electroencephalogram signals of the target frequency band before and after training, and obtaining a training effect according to the slope and the difference.
In this embodiment, as shown in fig. 6, linear regression is performed on the relative power values of the electroencephalogram signals in the target frequency band in 12 periods (2 rest periods, a baseline period, and 9 neural feedback training periods) corresponding to 1 training, so as to obtain a slope L1.
As shown in fig. 7, the relative power values of the electroencephalogram signals in the target frequency band of n × 10 training periods (baseline period, 9 neurofeedback training tests) of n times of training are subjected to linear regression to obtain a slope L2.
The virtual reality system comprises a plurality of scene training modes with different difficulties, each scene training mode comprises a plurality of different scene modes with the same difficulty, the slope L1 is used as a learning index (the training comprises a plurality of training periods) of the training, and the short-term training effect can be judged according to the learning index L1; the method specifically comprises the following steps: if the L1 is a positive value, the training effect is good, a virtual reality contextual model with higher difficulty can be selected for training again, if the L1 is a negative value, the training effect is poor, the selection standard needs to be correspondingly reduced at the moment, the situation with lower training difficulty is selected for training, the contextual model with the same difficulty level is selected for training at the same time, different situations are switched at the same difficulty level, the training fatigue of a user can be relieved, and meanwhile, the enthusiasm of the patient for training is improved; the slope L2 is used as a long-term training learning evaluation index (including multiple times of training), and is used for evaluating a user after multiple times of training, so that the personalized dynamic task difficulty of the system can be verified, and the positive effect of adjustment on the training effect can be achieved.
In an embodiment, the method further comprises:
and acquiring the electroencephalogram signal in the trained resting state, and calculating the difference value between the relative power value of the brain wave activity corresponding to the electroencephalogram signal in the trained resting state and the relative power value of the brain wave activity corresponding to the electroencephalogram signal in the resting state before training.
In this embodiment, the electroencephalogram signal in the resting state before the training and the electroencephalogram signal in the resting state after the training are in the same state, the relative power value of the brain wave activity corresponding to the electroencephalogram signal before the training and the relative power value of the brain wave activity of the electroencephalogram signal after the training are compared, a difference is calculated, and the training effect can be obtained more intuitively according to the difference.
The embodiment of the invention also provides a signal analysis device of the electroencephalogram neural feedback system combined with the virtual reality, which is used for executing any embodiment of the signal analysis method of the electroencephalogram neural feedback system combined with the virtual reality. Specifically, please refer to fig. 9, fig. 9 is a schematic block diagram of a signal analysis device of an electroencephalogram neurofeedback system in combination with virtual reality according to an embodiment of the present invention.
As shown in fig. 9, the signal analysis apparatus 900 of the electroencephalogram neurofeedback system in combination with virtual reality includes: setting section 901, processing section 902, feedback section 903, and feedback section 904.
The setting unit 901 is used for collecting the electroencephalogram signals of the user in the resting state and setting a training initial threshold according to the electroencephalogram signal analysis result in the resting state;
the processing unit 902 is configured to collect electroencephalograms generated in each training period, perform filtering processing through butterworth bandpass filtering, calculate power spectral density by using a pwelch function, obtain an average value of power spectral density of electroencephalograms in a corresponding frequency band to which feedback training is directed, take the electroencephalograms in the corresponding frequency band to which the feedback training is directed as electroencephalograms in a target frequency band, take the average value of power spectral density of electroencephalograms in the target frequency band as a feedback value of a current training period, and adjust a threshold of a next training period according to the feedback value;
a feedback unit 903, configured to convert a feedback value of each training period and transmit the converted feedback value to the virtual reality system, and configured to control a change of a training scene selected by the virtual reality system;
the selecting unit 904 calculates a learning effect index of feedback training of the current training, and selects a suitable scene state for the next training of the user according to the index by the virtual reality system.
The device dynamically adjusts the task difficulty according to the current cognitive ability of the user in the training process by processing the electroencephalogram signals generated in each training period of the user in real time and giving a feedback value and adjusting the threshold value of the next training period in real time according to the feedback value.
More specifically, with reference to fig. 8 and 9, an electroencephalogram signal acquisition system acquires an electroencephalogram signal generated in each neural feedback training period, then performs filtering processing, calculates a power spectral density of a corresponding frequency band, and outputs a feedback value to a virtual reality system, the virtual reality system adjusts a threshold of a next training period according to the feedback value so that a task difficulty of the next training period is more suitable for a current cognitive state of a user, thereby achieving a better training effect, generates a corresponding scenario according to the feedback value and feeds back the scenario to the user in real time, calculates a feedback training learning effect index of the current training, and selects a suitable scenario state by the virtual reality system according to the index for the next training of the user.
Since the embodiments of the apparatus portion and the method portion correspond to each other, please refer to the description of the embodiments of the method portion for the embodiments of the apparatus portion, which is not repeated here.
The signal analysis apparatus of the brain electrical neurofeedback system in combination with virtual reality described above may be implemented in the form of a computer program that can be run on a computer device as shown in fig. 10.
Referring to fig. 10, fig. 10 is a schematic block diagram of a computer device according to an embodiment of the present invention. The computer device 1000 is a server, and the server may be an independent server or a server cluster composed of a plurality of servers.
Referring to fig. 10, the computer device 1000 includes a processor 1002, a memory, which may include a non-volatile storage medium 1003 and an internal memory 1004, and a network interface 1005 connected by a system bus 1001.
The nonvolatile storage medium 1003 can store an operating system 10031 and a computer program 10032. The computer program 10032, when executed, can cause the processor 1002 to perform a method of signal analysis for a brain electrical neurofeedback system in conjunction with virtual reality.
The processor 1002 is used to provide computing and control capabilities, supporting the operation of the overall computer device 1000.
The internal memory 1004 provides an environment for running the computer program 10032 in the non-volatile storage medium 1003, and when the computer program 10032 is executed by the processor 1002, the processor 1002 may be caused to execute a signal analysis method of the brain electrical nerve feedback system in combination with virtual reality.
The network interface 1005 is used for network communication, such as providing transmission of data information. Those skilled in the art will appreciate that the configuration shown in fig. 10 is a block diagram of only a portion of the configuration associated with aspects of the present invention and is not intended to limit the computing device 1000 to which aspects of the present invention may be applied, and that a particular computing device 1000 may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
Those skilled in the art will appreciate that the embodiment of a computer device illustrated in fig. 10 does not constitute a limitation on the specific construction of the computer device, and that in other embodiments a computer device may include more or fewer components than those illustrated, or some components may be combined, or a different arrangement of components. For example, in some embodiments, the computer device may only include a memory and a processor, and in such embodiments, the structures and functions of the memory and the processor are consistent with those of the embodiment shown in fig. 10, and are not described herein again.
It should be understood that, in the embodiment of the present invention, the Processor 1002 may be a Central Processing Unit (CPU), and the Processor 1002 may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable gate arrays (FPGAs) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and the like. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
In another embodiment of the invention, a computer-readable storage medium is provided. The computer readable storage medium may be a non-volatile computer readable storage medium. The computer readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the signal analysis method of the electroencephalogram neurofeedback system in combination with virtual reality of the embodiments of the present invention.
The storage medium is an entity and non-transitory storage medium, and may be various entity storage media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only memory (ROM), a magnetic disk, or an optical disk.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses, devices and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A signal analysis method of an electroencephalogram neural feedback system combined with virtual reality is characterized by comprising the following steps:
collecting an electroencephalogram signal in a resting state of a user, and setting a training initial threshold according to an electroencephalogram signal analysis result in the resting state;
acquiring electroencephalogram signals generated in each training period, carrying out filtering processing through Butterworth band-pass filtering, then calculating power spectral density by using a pwelch function to obtain an average value of power spectral density of electroencephalogram signals of corresponding frequency bands for feedback training, taking the electroencephalogram signals of the corresponding frequency bands for the feedback training as electroencephalogram signals of target frequency bands, taking the average value of the power spectral density of the electroencephalogram signals of the target frequency bands as a feedback value of the training period at the time, and adjusting a threshold value of the next training period according to the feedback value;
transmitting the feedback value of each training period to the virtual reality system after conversion, and controlling the change of the training scene selected by the virtual reality system;
and calculating a feedback training learning effect index of the current training, and selecting a proper scene state by the virtual reality system according to the index for the next training of the user.
2. The method for analyzing signals of an electroencephalogram neural feedback system combined with virtual reality according to claim 1, wherein the steps of collecting electroencephalogram signals generated in each training period, performing filtering processing through Butterworth band-pass filtering, then calculating power spectral density by using a pwelch function, feeding back an average value of power spectral density of electroencephalogram signals of a corresponding frequency band for training, taking the electroencephalogram signals of the corresponding frequency band for the feedback training as electroencephalogram signals of a target frequency band, taking the average value of the power spectral density of the electroencephalogram signals of the target frequency band as a feedback value of a current training period, and adjusting a threshold of a next training period according to the feedback value comprise:
acquiring an electroencephalogram signal with a window length of 10s and a step length of 0.1s as a unit;
filtering the electroencephalogram signals through Butterworth band-pass filtering, wherein the frequency band of a filtering pass band is 1-40 Hz;
calculating the power spectral density of the electroencephalogram signal after filtering processing by using a pwelch function;
then extracting energy signals of a frequency band corresponding to the electroencephalogram signals of the target frequency band, and solving the average value of power spectral density of the electroencephalogram signals of the corresponding frequency band;
and taking the average value of the power spectral density of the corresponding frequency band brain electrical signals as a feedback value of the current training period, and adjusting the threshold value of the next training period according to the feedback value.
3. The method for analyzing signals of an electroencephalogram neural feedback system in combination with virtual reality according to claim 1, wherein the feedback values of each training period are converted and then transmitted to the virtual reality system for controlling the change of the training scene selected by the virtual reality system, and the method comprises the following steps:
comparing the average value of the power spectral density of the brain electrical signals of the target frequency band with a threshold value set in the training period;
if the average value of the power spectral density of the brain electrical signals of the target frequency band is higher than the threshold value of the training period, outputting a signal 1;
if the average value of the power spectral density of the brain electrical signals of the target frequency band is lower than the threshold value of the training period, outputting a signal 0;
and transmitting the output signal to the virtual reality system for controlling the change of the training scene selected by the virtual reality system.
4. The method for analyzing signals of an electroencephalogram neurofeedback system in combination with virtual reality according to claim 1, further comprising:
the method comprises the steps of collecting electroencephalogram signals of a baseline period, carrying out filtering processing through Butterworth band-pass filtering, then utilizing a pwelch function to calculate power spectral density, obtaining an average value of the power spectral density of the electroencephalogram signals of corresponding frequency bands, taking the average value as a feedback value of the baseline period, and adjusting a threshold value of a next training period according to the feedback value.
5. The method for analyzing signals of an electroencephalogram neurofeedback system in combination with virtual reality according to claim 1, further comprising:
collecting and preprocessing the electroencephalogram signals of each training period, and calculating to obtain the relative power value of the electroencephalogram signals of the target frequency band of each training period;
and performing linear regression processing on the relative power value of the electroencephalogram signal of the target frequency band in each training period to obtain a slope and the difference between the relative power values of the electroencephalogram signals of the target frequency band before and after training, and acquiring a training effect according to the slope and the difference.
6. The method for analyzing the signal of the electroencephalogram neural feedback system combined with the virtual reality according to claim 5, wherein the step of acquiring and preprocessing the electroencephalogram signal of each training period, and calculating the relative power value of the electroencephalogram signal of the target frequency band of each training period comprises the following steps:
filtering the electroencephalogram signal in each training period by using a 0.5-40 Hz band-pass filter and a 50Hz notch filter, and deleting abnormal data sections;
removing ocular and myoelectric artifacts to complete preprocessing;
dividing the preprocessed electroencephalogram signal data of each training period into a plurality of data sections, performing frequency domain analysis through Fourier transform, estimating by adopting a Welch power spectrum estimation method, and calculating frequency spectrum signals of corresponding frequency bands;
and then calculating the relative power values of the electroencephalogram signals of the plurality of target frequency bands corresponding to the plurality of data bands of each training period according to the following formula:
relative power value of target frequency band electroencephalogram signal
HF and LF are maximum boundary values and minimum boundary values of corresponding frequency bands of the electroencephalogram signals of the target frequency band, P (i) is a frequency spectrum amplitude, and i is a frequency spectrum index;
and taking the median of the relative power values of the electroencephalogram signals of the target frequency bands of the plurality of data segments as the relative power value of the electroencephalogram signals of the target frequency band of each training period.
7. The method for analyzing signals of an electroencephalogram neurofeedback system in combination with virtual reality according to claim 1, further comprising:
and acquiring the electroencephalogram signals in the trained resting state, and calculating the difference value between the relative power value of the electroencephalogram signals in the target frequency band in the trained resting state and the relative power value of the electroencephalogram signals in the target frequency band in the resting state before training.
8. A signal analysis device of an electroencephalogram neurofeedback system in combination with virtual reality is characterized by comprising:
the setting unit is used for acquiring the electroencephalogram signals of the user in the resting state and setting a training initial threshold according to the electroencephalogram signal analysis result in the resting state;
the processing unit is used for acquiring electroencephalogram signals generated in each training period, filtering the electroencephalogram signals through Butterworth band-pass filtering, calculating power spectral density by using a pwelch function to obtain an average value of power spectral density of the electroencephalogram signals of the corresponding frequency band aimed by feedback training, taking the electroencephalogram signals of the corresponding frequency band aimed by the feedback training as electroencephalogram signals of a target frequency band, taking the average value of the power spectral density of the electroencephalogram signals of the target frequency band as a feedback value of the current training period, and adjusting a threshold value of the next training period according to the feedback value;
the feedback unit is used for transmitting the feedback value of each training period to the virtual reality system after conversion, and is used for controlling the change of the training scene selected by the virtual reality system;
and the selection unit is used for calculating the feedback training learning effect index of the current training, and selecting a proper scene state by the virtual reality system according to the index for the next training of the user.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of signal analysis of a virtual reality combined electroencephalogram neurofeedback system according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, characterized in that it stores a computer program which, when executed by a processor, causes the processor to execute the method of signal analysis of a virtual reality-integrated electroencephalogram neurofeedback system according to any one of claims 1 to 7.
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