阅读说明：本技术 一种基于lzc算法的晕车程度量化方法、装置和存储介质 (Carsickness degree quantification method and device based on LZC algorithm and storage medium ) 是由 赵蕾蕾 杨铁牛 于 2019-09-18 设计创作，主要内容包括：本发明公开了一种基于LZC算法的晕车程度量化方法、装置和存储介质,本发明在获取脑电信号后进行预处理,得出脑电数据,根据预先设定的评估时间段对所述脑电数据按照时间顺序进行分段,得出分段脑电数据,计算出分段脑电数据的复杂度后,与所获取的评分数据共同计算相关性,根据相关性和对应的评分数据得出量化结果,通过复杂度反映出胜利信号随人体状态变化而变化的情况,结合评分数据实现了量化的主客观结合,使得得出的量化数据更有参考性。(The invention discloses a method, a device and a storage medium for quantifying carsickness degree based on an LZC algorithm.)

1. A carsickness degree quantification method based on an LZC algorithm is characterized by comprising the following steps:

the method comprises the steps that a client side obtains an electroencephalogram signal, and the electroencephalogram signal is preprocessed to obtain electroencephalogram data;

the client acquires a preset evaluation time period, and segments the acquired electroencephalogram data according to the evaluation time period and a time sequence to obtain segmented electroencephalogram data;

the client calculates the complexity of the segmented electroencephalogram data according to an LZC algorithm;

and the client acquires the scoring data corresponding to the evaluation time period, calculates the correlation between the scoring data and the numerical value of the complexity, and obtains a quantification result according to the correlation and the corresponding scoring data.

2. The quantification method of the car sickness level based on the LZC algorithm as claimed in claim 1, wherein: the electroencephalogram signals are signals with frequency bands located in theta wave frequency bands, and the sampling frequency of the electroencephalogram signals is 1000 Hz.

3. The quantification method of the degree of carsickness based on the LZC algorithm as claimed in claim 2, wherein the preprocessing comprises the following steps:

the client carries out filtering and baseline removing processing on the electroencephalogram signals to obtain first preprocessing signals;

the client reduces the sampling frequency of the first preprocessing signal to 250Hz to obtain a second preprocessing signal;

and removing the electro-oculogram noise and the myoelectricity noise in the second preprocessed signal by the client to obtain the electroencephalogram data.

4. The quantification method of the car sickness level based on the LZC algorithm, according to claim 3, characterized in that: the filtering of the electroencephalogram signal comprises high-pass filtering and low-pass filtering.

5. The quantification method of the car sickness level based on the LZC algorithm as claimed in claim 1, wherein: the scoring data is obtained through voice scoring of the user acquired and recognized by the sound pickup equipment.

6. An apparatus for performing an LZC algorithm based car sickness quantification method, comprising a CPU unit for performing the steps of:

the method comprises the steps that a client side obtains an electroencephalogram signal, and the electroencephalogram signal is preprocessed to obtain electroencephalogram data;

the client acquires a preset evaluation time period, and segments the acquired electroencephalogram data according to the evaluation time period and a time sequence to obtain segmented electroencephalogram data;

the client calculates the complexity of the segmented electroencephalogram data according to an LZC algorithm;

and the client acquires the scoring data corresponding to the evaluation time period, calculates the correlation between the scoring data and the numerical value of the complexity, and obtains a quantification result according to the correlation and the corresponding scoring data.

7. The apparatus for performing LZC algorithm based car sickness quantification method according to claim 6, wherein the CPU unit is further configured to perform the following steps:

the client carries out filtering and baseline removing processing on the electroencephalogram signals to obtain first preprocessing signals;

the client reduces the sampling frequency of the first preprocessing signal to 250Hz to obtain a second preprocessing signal;

and removing the electro-oculogram noise and the myoelectricity noise in the second preprocessed signal by the client to obtain the electroencephalogram data.

8. A computer-readable storage medium characterized by: the computer-readable storage medium stores computer-executable instructions for causing a computer to perform a method for quantifying car sickness level based on an LZC algorithm according to any one of claims 1 to 5.

Technical Field

The invention relates to the field of biological signal processing, in particular to a carsickness degree quantification method and device based on an LZC algorithm and a storage medium.

Background

With the development of science and technology, driving simulation equipment has been applied to the automobile and entertainment industries, so that drivers can obtain real driving experience. However, the simulated driving equipment is different from the real driving, different delays can be brought by different performances of the equipment, so that a driver is easy to have car sickness during the simulated driving, and in order to improve the equipment, the test is carried out by a subject before the equipment is put into use, so that the improvement is carried out on the cause of the car sickness. The existing scheme mainly depends on collecting electroencephalogram signals of a subject, and realizes the quantification of the carsickness degree by calculating the center of gravity frequency.

Disclosure of Invention

In order to overcome the defects of the prior art, the invention aims to provide a method, a device and a storage medium for quantifying the carsickness degree based on an LZC algorithm, which can be used for quantifying the carsickness degree by combining an electroencephalogram signal and a subject score and improving the reference value of a quantification result.

The technical scheme adopted by the invention for solving the problems is as follows: in a first aspect, the invention provides a method for quantifying a carsickness degree based on an LZC algorithm, which comprises the following steps:

the method comprises the steps that a client side obtains an electroencephalogram signal, and the electroencephalogram signal is preprocessed to obtain electroencephalogram data;

the client acquires a preset evaluation time period, and segments the acquired electroencephalogram data according to the evaluation time period and a time sequence to obtain segmented electroencephalogram data;

the client calculates the complexity of the segmented electroencephalogram data according to an LZC algorithm;

and the client acquires the scoring data corresponding to the evaluation time period, calculates the correlation between the scoring data and the numerical value of the complexity, and obtains a quantification result according to the correlation and the corresponding scoring data.

Furthermore, the electroencephalogram signals are signals with frequency bands located in theta wave frequency bands, and the sampling frequency of the electroencephalogram signals is 1000 Hz.

Further, the pretreatment comprises the following steps:

the client carries out filtering and baseline removing processing on the electroencephalogram signals to obtain first preprocessing signals;

the client reduces the sampling frequency of the first preprocessing signal to 250Hz to obtain a second preprocessing signal;

and removing the electro-oculogram noise and the myoelectricity noise in the second preprocessed signal by the client to obtain the electroencephalogram data.

Further, the filtering of the electroencephalogram signal includes high-pass filtering and low-pass filtering.

Further, the scoring data is obtained through voice scoring of the user acquired and recognized by the sound pickup equipment.

In a second aspect, the present invention provides an apparatus for performing an LZC algorithm based car sickness quantification method, comprising a CPU unit for performing the steps of:

the method comprises the steps that a client side obtains an electroencephalogram signal, and the electroencephalogram signal is preprocessed to obtain electroencephalogram data;

the client acquires a preset evaluation time period, and segments the acquired electroencephalogram data according to the evaluation time period and a time sequence to obtain segmented electroencephalogram data;

the client calculates the complexity of the segmented electroencephalogram data according to an LZC algorithm;

and the client acquires the scoring data corresponding to the evaluation time period, calculates the correlation between the scoring data and the numerical value of the complexity, and obtains a quantification result according to the correlation and the corresponding scoring data.

Further, the CPU unit is further configured to perform the steps of:

the client carries out filtering and baseline removing processing on the electroencephalogram signals to obtain first preprocessing signals;

the client reduces the sampling frequency of the first preprocessing signal to 250Hz to obtain a second preprocessing signal;

and removing the electro-oculogram noise and the myoelectricity noise in the second preprocessed signal by the client to obtain the electroencephalogram data.

In a third aspect, the present invention provides an apparatus for performing an LZC algorithm based car sickness quantification method, comprising at least one control processor and a memory for communicative connection with the at least one control processor; the memory stores instructions executable by the at least one control processor to enable the at least one control processor to perform the LZC algorithm based car sickness quantification method as described above.

In a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon computer-executable instructions for causing a computer to perform the LZC algorithm-based car sickness quantification method as described above.

In a fifth aspect, the present invention also provides a computer program product comprising a computer program stored on a computer-readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the method for quantifying the degree of carsickness based on an LZC algorithm as described above.

One or more technical schemes provided in the embodiment of the invention have at least the following beneficial effects: according to the method, the electroencephalogram signals are acquired and then preprocessed to obtain electroencephalogram data, the electroencephalogram data are segmented according to a preset evaluation time period according to a time sequence to obtain segmented electroencephalogram data, after the complexity of the segmented electroencephalogram data is calculated, the relevance is calculated together with the acquired score data, a quantitative result is obtained according to the relevance and the corresponding score data, the condition that victory signals change along with the change of human body states is reflected through the complexity, quantitative subjective and objective combination is achieved by combining the score data, and the obtained quantitative data are enabled to be more referential.

Drawings

The invention is further illustrated with reference to the following figures and examples.

Fig. 1 is a flowchart of a method for quantifying car sickness level based on an LZC algorithm according to a first embodiment of the present invention;

FIG. 2 is a flow chart of preprocessing electroencephalogram signals in a method for quantifying the degree of carsickness based on an LZC algorithm according to a first embodiment of the invention;

fig. 3 is a schematic diagram of an apparatus for performing an LZC algorithm-based car sickness quantification method according to a second embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

It should be noted that, if not conflicted, the various features of the embodiments of the invention may be combined with each other within the scope of protection of the invention. Additionally, while functional block divisions are performed in apparatus schematics, with logical sequences shown in flowcharts, in some cases, steps shown or described may be performed in sequences other than block divisions in apparatus or flowcharts.

The Lempel-Ziv complexity (LZC) algorithm is an algorithm proposed by Lempel and Ziv to measure the increase of a new pattern with the increase of the length of a sequence, and characterizes the rate of appearance of the new pattern in a time sequence. The complexity can reflect the change of physiological signals along with the state of the human body.

Referring to fig. 1, a first embodiment of the present invention provides an LZC algorithm-based car sickness quantification method, including the steps of:

s100, acquiring an electroencephalogram signal by a client, and preprocessing the electroencephalogram signal to obtain electroencephalogram data;

s200, a client acquires a preset evaluation time period, and segments the acquired electroencephalogram data according to the evaluation time period and the time sequence to obtain segmented electroencephalogram data;

step S300, the client calculates the complexity of the segmented electroencephalogram data according to the LZC algorithm;

step S400, the client side obtains scoring data corresponding to the evaluation time period, calculates the correlation between the scoring data and the numerical value of the complexity, and obtains a quantification result according to the correlation and the corresponding scoring data.

It should be noted that, in this embodiment, it is preferable to collect signals at a sampling frequency of 1000Hz through the 32 leads of anteego mrt, and other devices may also be used, so that the electroencephalogram signals can be collected, which is not described herein again. It should be noted that the evaluation time period is a time length arbitrarily set according to specific experimental requirements, for example, if a test of 10 minutes is to be performed and 5 sets of data are required, the evaluation time period is 5 time periods with a duration of 2 minutes. It should be noted that, if the number of the segmented electroencephalogram data acquired in step S200 is greater than 2, for example, 5 time periods described in the above column are used to obtain 5 segmented electroencephalogram data, step S300 and step S400 are sequentially performed on each segmented electroencephalogram data according to the time sequence to obtain 5 groups of data.

It should be noted that, any algorithm in the prior art may be adopted for the calculation of the complexity by using the LZC algorithm, and the following is a preferred calculation method of this embodiment: for any one (0, 1) time series S (S1, S2, S3 … … Sn), each Si in the series takes a value of 1 or 0, and the length of S is n. A substring subs (i, j) of S is a character string composed of the i-th letter to the j-th letter in S in order, and satisfies (i < ═ j < ═ n); if another sequence Q (ql, Q2, Q3 …. qm) whose value range satisfies (0, 1) is available, then S and Q are concatenated to obtain SQ, where SQ is (sl, S2, S3 … sn, ql, Q2, Q3 …. qm); and subtracting the last value from the sequence SQ to yield the SQV sequence (sl, s2, s3 … sn, ql, q2, q3 … qm-1). Let v (SQV) denote a set of all different substrings of the SQV, and c (n) is complexity of S of the sequence, when calculating complexity, c (n) ═ l, S ═ S1, Q ═ S1, and Nsov ═ S1 are set; if Q belongs to v (SQV), indicating that a character of Q can be copied from S, then concatenating the next letter of the sequence to be solved to Q, S ═ S1, (S2, S3), SQV ═ sl, S2; if Q does not belong to v (soy), i.e. the inserted character, in Q, Q should be concatenated to S, S equals SQ, and Q is cleared, and then the next letter of the sequence to be found is added to Q, when S equals (S1, S2) and Q equals (S3); c (n) ═ c (n) +1 is executed once every time the Q cascades Ns, repeating the processes (2) and (3) until Q takes the last bit of the target sequence; this divides (sl, s2, s3 …. sn) into c (n) different substrings, i.e., (sl, s2, s3 …. sn).

Further, in another embodiment of the invention, the electroencephalogram signal is a signal with a frequency band located in a theta wave frequency band, and the sampling frequency of the electroencephalogram signal is 1000 Hz.

It should be noted that the electroencephalogram signal may be any one or more of delta, theta, alpha, beta, and gamma frequency bands, and since the complexity of the theta wave increases with the increase of the car sickness degree, the theta wave is preferably used in this embodiment, and the calculation complexity caused by calculating data of multiple frequency bands can be avoided.

Referring to fig. 2, further, in another embodiment of the present invention, the pre-processing comprises the steps of:

step S110, the client-side carries out filtering and baseline removing processing on the electroencephalogram signal to obtain a first preprocessing signal;

step S120, the client reduces the sampling frequency of the first preprocessing signal to 250Hz to obtain a second preprocessing signal;

and step S130, removing the electro-oculogram noise and the myoelectricity noise in the second preprocessed signal by the client to obtain the electroencephalogram data.

It should be noted that, in order to ensure that the acquired electroencephalogram signals are complete, a larger sampling frequency is usually adopted for acquisition, for example, 1000Hz is common, so that the acquired electroencephalogram signals have larger noise, most of the more obvious noise can be removed through filtering and baseline removal processing, and a first pre-processing signal for preliminary denoising is obtained. It should be noted that, in order to simplify the calculation result, after the preliminary denoising is performed, the down-sampling frequency is performed on the first preprocessed signal, 250Hz is only the preferred choice of this embodiment, and the obtained second preprocessed signal includes richer electroencephalogram signals. It should be noted that, many types of noise are usually included in the electroencephalogram signal, so the embodiment preferably removes the electro-oculogram and electromyogram noise by an independent component analysis method, for example, a common method of running ICA in egglab ensures that the noise is not included in the obtained electroencephalogram data.

Further, in another embodiment of the present invention, filtering the brain electrical signal includes high-pass filtering and low-pass filtering.

In this embodiment, the cut-off frequency of the high-pass filtering is 40Hz, and the cut-off frequency of the low-pass filtering is 1Hz, or other cut-off frequencies, which may be adjusted according to the requirements of specific tests.

Further, in another embodiment of the invention, the scoring data is obtained by voice scoring of the user acquired and recognized by the sound pickup device.

The sound pickup device in this embodiment is a common speech recognition device in the market, such as a microphone, and may be configured to recognize a speech score input by the subject and input the speech score to the client.

Referring to fig. 3, a second embodiment of the present invention further provides an apparatus for performing an LZC algorithm-based car sickness quantification method, where the apparatus is a smart device, such as a smart phone, a computer, a tablet computer, and the like, and can have a processor and implement corresponding functions, and the present embodiment is described by taking a computer as an example.

In the computer 3000 for executing the LZC algorithm-based carsickness degree quantization method, a CPU unit 3100 is included, the CPU unit 3100 being configured to perform the steps of:

the client acquires the electroencephalogram signals, and preprocesses the electroencephalogram signals to obtain electroencephalogram data;

the client acquires a preset evaluation time period, and segments the acquired electroencephalogram data according to the evaluation time period and the time sequence to obtain segmented electroencephalogram data;

the client calculates the complexity of the segmented electroencephalogram data according to the LZC algorithm;

the client side obtains the scoring data corresponding to the evaluation time period, calculates the correlation between the scoring data and the numerical value of the complexity, and obtains a quantification result according to the correlation and the corresponding scoring data.

Further, in another embodiment of the present invention, the CPU unit is further configured to perform the steps of:

the client carries out filtering and baseline removing processing on the electroencephalogram signal to obtain a first preprocessing signal;

the client reduces the sampling frequency of the first preprocessing signal to 250Hz to obtain a second preprocessing signal;

and removing the electro-oculogram noise and the myoelectricity noise in the second preprocessed signal by the client to obtain the electroencephalogram data.

The computer 3000 and the CPU unit 3100 may be connected via a bus or other means, and the computer 3000 further includes a memory as a non-transitory computer-readable storage medium for storing non-transitory software programs, non-transitory computer-executable programs, and modules, such as program instructions/modules corresponding to the apparatus for performing the method for quantifying the degree of carsickness based on the LZC algorithm according to the embodiments of the present invention. The computer 3000 controls the CPU unit 3100 to execute various functional applications for executing the LZC algorithm-based carsickness degree quantization method and data processing, i.e., to implement the LZC algorithm-based carsickness degree quantization method of the above-described method embodiment, by running non-transitory software programs, instructions, and modules stored in the memory.

The memory may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the CPU unit 3100, and the like. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory located remotely from CPU unit 3100, which may be connected to computer 3000 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The one or more modules are stored in the memory, and when executed by the CPU unit 3100, perform the LZC algorithm-based carsickness level quantification method in the above-described method embodiment.

An embodiment of the present invention further provides a computer-readable storage medium, where a computer-executable instruction is stored in the computer-readable storage medium, and the computer-executable instruction is executed by the CPU 3100, so as to implement the method for quantifying a carsickness level based on an LZC algorithm.

The above-described embodiments of the apparatus are merely illustrative, and the apparatuses described as separate components may or may not be physically separate, may be located in one place, or may be distributed over a plurality of network apparatuses. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.

It should be noted that, since the apparatus for performing the method for quantifying the carsickness level based on the LZC algorithm in the present embodiment is based on the same inventive concept as the above-mentioned method for quantifying the carsickness level based on the LZC algorithm, the corresponding contents in the method embodiment are also applicable to the present apparatus embodiment, and are not described in detail herein.

Through the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be implemented by software plus a general hardware platform. Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by hardware related to instructions of a computer program, which may be stored in a computer-readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read Only Memory (ROM), a Random Access Memory (RAM), or the like.

While the preferred embodiments of the present invention have been described in detail, it will be understood by those skilled in the art that the foregoing and various other changes, omissions and deviations in the form and detail thereof may be made without departing from the scope of this invention.