WO2023198150A1

WO2023198150A1 - Data compression method for electroencephalogram data, chip, device, and storage medium

Info

Publication number: WO2023198150A1
Application number: PCT/CN2023/088072
Authority: WO
Inventors: 盛廷义; 王斌
Original assignee: 杭州脑芯科技有限公司
Priority date: 2022-04-13
Filing date: 2023-04-13
Publication date: 2023-10-19
Also published as: CN114665886A

Abstract

The present application relates to the technical field of computers, and relates to a data compression method for electroencephalogram data, a chip, a device, and a storage medium. The method comprises: obtaining target electroencephalogram data (201); determining, from data ranges, a target data range to which the target electroencephalogram data belongs (202), the data ranges being obtained by dividing a maximum data range of electroencephalogram data; determining a target data compression rate corresponding to the target data range (203); and using the target data compression rate to compress the target electroencephalogram data to obtain compressed electroencephalogram data, so as to store the compressed electroencephalogram data (204). The problem that the amount of data stored in a brain-computer interface device is large due to the fact that electroencephalogram data changes slowly and the brain-computer interface device generally needs to collect the electroencephalogram data for a long time can be solved; since the target electroencephalogram data is first compressed and then the electroencephalogram data is stored, the size of the stored electroencephalogram data can be reduced, thereby reducing the amount of data stored in the brain-computer interface device and saving the storage space.

Description

Data compression methods, chips, equipment and storage media for EEG data

Cross-references to related applications

This application claims priority to the Chinese patent application filed on April 13, 2022, with the application number 202210383249.X and titled "Data compression method, chip, equipment and storage medium for EEG data".

Technical field

The present application relates to a data compression method, chip, equipment and storage medium for EEG data, and belongs to the field of computer technology.

Background technique

Brain-computer interface equipment is an important bridge for information transmission between the brain and computers. The function of the brain-computer interface device is to convert the collected EEG data into operating signals that can be recognized by the computer, so as to control the computer through the EEG data.

In the process of processing EEG data, traditional brain-computer interface devices directly store the collected EEG data and analyze and convert the stored EEG data to generate operating signals that can be recognized by computers. .

However, due to the slow change of EEG data, brain-computer interface devices usually need to collect EEG data for a long time before they can conduct a complete analysis of the EEG signal. This will generate a large amount of EEG data, which leads to the problem of BCI. The problem is the large amount of data stored on the device.

Contents of the invention

This application provides data compression methods, chips, equipment and storage media for EEG data, which can solve the problem that due to the slow change of EEG data, brain-computer interface devices usually need to collect EEG data for a long time, which will generate a large amount of data. EEG data, which leads to the problem of large amounts of data stored in computers. This application provides the following technical solutions:

In a first aspect, a data compression method for EEG data is provided. The method includes: acquiring target EEG data; determining a target data range to which the target EEG data belongs from each data range; and the data range is It is obtained by dividing the maximum data range of the EEG data; determine the target data compression rate corresponding to the target data range; the data compression rate corresponding to the mth data range is greater than the data compression rate corresponding to the nth data range, and the data compression rate corresponding to the nth data range is The EEG data in the m data range is smaller than the EEG data in the nth data range, and m and n are positive integers; the target EEG data is compressed using the target data compression rate. , obtain compressed EEG data to store the compressed EEG data.

Optionally, before determining the target data range to which the target EEG data belongs from each data range, it further includes: dividing the maximum data range according to a preset dividing method to obtain at least two data ranges; The EEG data in the k-th data range is greater than the EEG data in the h-th data range. The width corresponding to the k-th data range is greater than or equal to the width of the h-th data range. The k and the h is a positive integer.

Optionally, compressing the target EEG data using the target data compression rate to obtain compressed EEG data includes: shifting the target EEG data according to the target compression rate. Operation, the shifted EEG data is obtained; the sum of the shifted EEG data and the target initial value corresponding to the target data range is determined as the compressed EEG data; the target initial value is determined according to the The target data range is determined.

Optionally, performing a shift operation on the target EEG data according to the target compression rate to obtain the shifted EEG data includes: determining the movement of the shift operation according to the target compression rate. number of digits; shift the target EEG data to the right by the number of shifting digits to obtain the shifted EEG data.

Optionally, the target EEG data is expressed in p-ary system, and the p is an integer greater than 1; and determining the number of shifting bits of the shift operation according to the target compression rate includes: using the p is the inverse of the logarithm of the data compression rate, which is determined as the number of moving bits.

Optionally, before compressing the target EEG data using the target data compression rate, the method further includes: Determine the target initial value corresponding to the target data range.

Optionally, determining the target initial value corresponding to the target data range includes: determining from each of the data ranges a reference data range in which the EEG data is smaller than the EEG data in the target data range; according to the target The data compression rate, the width of each reference data range and the corresponding data compression rate determine the target initial value corresponding to the target data range.

Optionally, determining the target initial value corresponding to the target data range according to the target data compression rate, the width of each reference data range and the corresponding data compression rate includes: converting each reference data range into The sum of the products of the width of the range and the corresponding data compression rate is determined as the first target value; the product of the sum of the widths of each reference data range and the target data compression rate is determined as the second target value; The difference between the first target value and the second target value is determined as the target initial value.

A second aspect provides a chip for executing the data compression method for EEG data provided in the first aspect.

In a third aspect, an electronic device is provided. The device includes a processor and a memory; a program is stored in the memory, and the program is loaded and executed by the processor to realize the EEG data provided by the first aspect. Compression method.

In a fourth aspect, a computer-readable storage medium is provided. A program is stored in the storage medium. When the program is executed by a processor, the program is used to implement the data compression method for EEG data provided in the first aspect.

The beneficial effects of this application at least include: obtaining target EEG data; determining the target data range to which the target EEG data belongs from each data range; the data range is obtained by dividing the maximum data range of the EEG data; determining the target data The target data compression rate corresponding to the range; the data compression rate corresponding to the m-th data range is greater than the data compression rate corresponding to the n-th data range, and the EEG data in the m-th data range is smaller than the EEG data in the n-th data range. Data, m and n are positive integers; use the target data compression rate to compress the target EEG data to obtain the compressed EEG data to store the compressed EEG data; it can solve the problem of brain-computer problems due to slow changes in EEG data. Interface devices usually need to collect EEG data for a long time, which will generate a large amount of EEG data, resulting in the problem of a large amount of data stored in the brain-computer interface device; because the target EEG data is first compressed, and then By storing the EEG data, the size of the stored EEG data can be reduced, thereby reducing the amount of data stored by the brain-computer interface device and saving storage space.

At the same time, because the smaller the EEG data is, the data range to which the EEG data belongs corresponds to a greater data compression rate, and the greater the data compression, the higher the accuracy of the compressed data, so the low-level data is in a state of no compression or low compression rate. , which can effectively ensure the accuracy of low-order data.

In addition, because the larger the EEG data, the wider the width of the data range to which the EEG data belongs, and because the larger the EEG data is, the smaller the data compression rate corresponding to the data range to which the EEG data belongs, so in each data range, The larger the width of the data range, the smaller the data compression rate corresponding to the data range, which can improve the data compression effect.

In addition, since the process of compressing the target EEG data is achieved by shifting and adding the target data, the compression of the target EEG data can be achieved by using adders and shifters. The hardware requirements are low and it is conducive to data processing. Application of compression algorithms.

In addition, by setting an appropriate compression algorithm, the compressed EEG data can make full use of storage space, so storage space can be further saved.

In addition, since the compression of EEG data can make full use of the limited storage space to store EEG data, it increases the duration of EEG data that can be stored and can meet the data duration requirements of different EEG data analysis and processing algorithms. , providing more flexible algorithm choices for the analysis and processing of EEG data.

The above description is only an overview of the technical solutions of the present application. In order to have a clearer understanding of the technical means of the present application and implement them according to the contents of the specification, the preferred embodiments of the present application are described in detail below with reference to the accompanying drawings.

Description of the drawings

In order to more clearly explain the specific embodiments of the present invention or the technical solutions in the prior art, the drawings that need to be used in the description of the specific embodiments or the prior art will be briefly introduced below. It is obvious that the drawings in the following description yes For some embodiments of the present invention, those of ordinary skill in the art can also obtain other drawings based on these drawings without exerting creative efforts.

Figure 1 is a schematic diagram of a data compression system for EEG data provided by an embodiment of the present application;

Figure 2 is a flow chart of a data compression method for EEG data provided by an embodiment of the present application;

Figure 3 is a schematic diagram of the correspondence between target EEG data and compressed EEG data provided by an embodiment of the present application;

Figure 4 is a flow chart of a data compression method for EEG data provided by an embodiment of the present application;

Figure 5 is a block diagram of a data compression device for EEG data provided by an embodiment of the present application;

Figure 6 is a block diagram of an electronic device provided by an embodiment of the present application.

Detailed ways

The technical solution of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. The present application will be described in detail below with reference to the accompanying drawings and embodiments. It should be noted that, as long as there is no conflict, the embodiments and features in the embodiments of this application can be combined with each other.

It should be noted that the terms "first", "second", etc. in the description and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

First, some terms involved in the embodiments of this application are introduced.

EEG data: Data that reflects the electrical activity in brain nerve cells on the surface of the cerebral cortex and/or scalp. EEG data has the characteristics of slow changes and low signal amplitude.

Data compression: refers to a technical method that reduces the amount of data to reduce storage space and improve its transmission, storage and processing efficiency without losing useful information.

Data compression ratio: refers to the ratio of the size of data after compression to the size before compression.

Bit (binary digit, bit): The smallest unit that represents information, which is the information contained in one bit of a binary number.

Figure 1 is a data compression system for EEG data provided by an embodiment of the present application. The system at least includes: a data acquisition device 110, a data compression device 120 and a storage device 130.

The data acquisition device 110 is used to acquire EEG signals reflecting brain activity from the scalp or inside the brain.

Optionally, the data collection device may be a non-invasive device, such as an EEG cap, or an invasive device, such as an implantable medical device. This embodiment does not limit the type of data collection device.

In this embodiment, the data acquisition device 110 also has an analog-to-digital conversion (ADC) function, which is used to convert the collected EEG signals (analog signals) into EEG data (digital signals) that can be recognized by electronic devices. Signal).

Optionally, the EEG data may be a binary number, or it may also be a decimal number. This embodiment does not limit the expression form of the EEG data.

Optionally, the maximum number of data bits of the EEG data generated by the data acquisition device 110 is fixed. The maximum number of data bits of the EEG data generated by the data acquisition device 110 may be 10 bits, or may be 12 bits. This embodiment does not limit the maximum number of data bits of the EEG data generated by the data acquisition device 110 .

Optionally, the maximum data range of the EEG data is determined based on the maximum number of data bits of the EEG data. Specifically, the EEG data is expressed in binary. When the maximum number of data bits of the EEG data is 10 bits, the maximum data range of the EEG data is 0 to 1024, or when the maximum number of data bits of the EEG data is 12 bits. When , the maximum data range of the EEG data is 0 to 4096. This embodiment does not limit the maximum number of digits of the EEG data.

The data compression device 120 is connected by communication with the data acquisition device 110, and is used to compress the EEG data generated by the data acquisition device.

Optionally, the data compression device 120 is a brain-computer interface chip, or may be located outside the brain-computer interface device. Among other electronic devices with computing functions, this embodiment does not limit the type of data compression device 120 .

In this embodiment, the data compression device 120 is used to obtain the target EEG data; determine the target data range to which the target EEG data belongs from each data range; the data range is obtained by dividing the maximum data range of the EEG data; Determine the target data compression rate corresponding to the target data range; the data compression rate corresponding to the m-th data range is greater than the data compression rate corresponding to the n-th data range, and the EEG data in the m-th data range is smaller than that in the n-th data range. EEG data, m and n are positive integers; use the target data compression rate to compress the target EEG data to obtain compressed EEG data to store the compressed EEG data.

There is a communication connection between the storage device 130 and the data compression device 120 for storing compressed EEG data.

Optionally, the storage device 130 may be a memory inside a chip that can store data, or it may be an external memory outside the chip. The memory inside the chip that can store data may be a static random access memory (Static Random-Access Memory, SRAM), flash memory (Flash EEPROM, FLASH), etc. This embodiment does not limit the type of storage device 130.

Optionally, the storage device 130 is communicatively connected with the data compression device 120 through the storage control device 140; the storage control device 140 is used to control the storage device 130 to store the compressed EEG data.

Optionally, the storage control device 140 is used to splice one or more compressed EEG data into data blocks of a preset size, so as to store the data in one data block into the storage device 130 at a time.

Optionally, the storage control device 140 is also used to control the order in which the storage device 130 stores the compressed EEG data.

In one example, the storage control device 140 is a First Input First Output (FIFO) memory.

It should be supplemented that in this embodiment, two or more devices among the data collection device 110, the data compression device 120, the storage device 130 and the storage control device 140 can be implemented as the same device, or they can all be implemented as one device. Implemented as different devices, this embodiment does not limit the implementation of the data collection device 110, the data compression device 120, the storage device 130 and the storage control device 140.

In summary, the data compression system for EEG data adopted in this embodiment obtains the target EEG data; determines the target data range to which the target EEG data belongs from each data range; the data range is the maximum data range of the EEG data. Obtained by dividing; determine the target data compression rate corresponding to the target data range; the data compression rate corresponding to the m-th data range is greater than the data compression rate corresponding to the n-th data range, and the EEG data in the m-th data range is less than the EEG data in n data ranges, m and n are positive integers; use the target data compression rate to compress the target EEG data to obtain compressed EEG data to store the compressed EEG data; it can solve the problem due to EEG data changes slowly, and brain-computer interface devices usually need to collect EEG data for a long time, which will generate a large amount of EEG data, which leads to the problem of a large amount of data stored in the BCI device; due to the first The target EEG data is compressed and then stored, so the size of the stored EEG data can be reduced, thereby reducing the amount of data stored by the brain-computer interface device and saving storage space.

FIG. 2 is a flow chart of a data compression method for EEG data provided by an embodiment of the present application. This application takes the application of this method to the data compression device 120 in the EEG data data compression system shown in FIG. 1 as an example. , this method includes at least the following steps:

Step 201: Obtain target EEG data.

Among them, the target EEG data is collected by the data acquisition equipment.

Optionally, obtaining the target EEG data includes: obtaining the target EEG data based on the communication connection with the data collection device.

Step 202: Determine the target data range to which the target EEG data belongs from each data range.

Among them, the data range is obtained by dividing the maximum data range of the EEG data. The maximum data range of the EEG data is stored in the data compression device in advance.

Optionally, before determining the target data range to which the target EEG data belongs from each data range, the following steps are also included: Divide the maximum data range according to the preset division method to obtain at least two data ranges.

Optionally, for the k-th data range and h-th data range in each data range, if the EEG data in the k-th data range is greater than the EEG data in the h-th data range, the k-th data range The corresponding width is greater than or equal to the width of the h-th data range, and k and h are positive integers. That is, the greater the EEG data in the data range, the greater the width of the data range. In order to realize that the larger the EEG data, the wider the width of the data range to which the EEG data belongs.

Among them, the EEG data in the k-th data range is greater than the EEG data in the h-th data range, which means: the minimum value of the EEG data in the k-th data range is greater than the EEG data in the h-th EEG data range. The maximum value of the EEG data.

The width of the data range refers to the number of EEG data types included in the data range.

For example, the kth data range is 64~127, and the hth data range is 0~63. Then the minimum value of the EEG data in the kth data range is 64, and the maximum value of the EEG data in the hth data range is is 63.

For another example, the k-th data range is 128-511, and the h-th data range is 64-127, then the width of the k-th data range is 384, and the width of the h-th data range is 64.

In this embodiment, the preset division method is stored in the data compression device in advance, and different maximum data ranges correspond to different preset division methods.

For example, the maximum data range is 0~1023, and the preset division method is to divide the maximum data range into four data ranges: 0~63, 64~127, 128~511 and 512~1023.

In actual implementation, different preset division methods can be set for the same maximum data range according to actual needs. This embodiment does not limit the division method of the maximum data range.

In one example, the maximum data range is 0~1023. The preset division method is to divide the maximum data range into four data ranges: 0~63, 64~127, 128~511 and 512~1023. The target EEG data is 67 , then the target data range is 64~127.

Step 203: Determine the target data compression rate corresponding to the target data range.

For the m-th data range and n-th data range in each data range, if the data compression rate corresponding to the m-th data range is greater than the data compression rate corresponding to the n-th data range, then the brain in the m-th data range The electrical data is smaller than the EEG data in the nth data range, and m and n are positive integers. That is, the smaller the EEG data in the data range, the greater the data compression rate corresponding to the data range. In order to realize that the smaller the EEG data is, the greater the data compression rate corresponding to the data range to which the EEG data belongs.

Among them, the EEG data in the m-th data range is smaller than the EEG data in the n-th data range, which means: the maximum value of the EEG data in the m-th data range is less than the EEG data in the n-th EEG data range. Minimum value of EEG data.

The data compression rate refers to the ratio of the size after data compression to the size before compression. That is, the greater the data compression rate, the higher the accuracy of the compressed data. The lower the data compression rate, the better the data compression effect.

In this embodiment, because the smaller the EEG data, the greater the data compression rate corresponding to the data range to which the EEG data belongs, and the greater the data compression, the higher the accuracy of the compressed data, so in the compressed EEG data , the smaller the EEG data, the higher the accuracy of the data, which can ensure the accuracy of low-order EEG data, that is, smaller EEG data.

In addition, because the larger the EEG data, the smaller the data compression rate corresponding to the data range to which the EEG data belongs, and the larger the EEG data, the wider the width of the data range to which the EEG data belongs, so in each data range, the data The larger the width of the range, the smaller the data compression rate corresponding to the data range, which can improve the data compression effect.

In this embodiment, the data compression rates corresponding to each data range are stored in the data compression device in advance, and the data compression rates corresponding to different data ranges are different.

In an example, the maximum data range is 0~1023. The preset division method is to divide the maximum data range into four data ranges: 0~63, 64~127, 128~511 and 512~1023. The data range 0~63 corresponds to The data compression rate of one eighth.

In actual implementation, different data compression rates can be set for the same data range according to actual needs. This embodiment does not limit the data compression rate corresponding to each data range.

In an example, the maximum data range is 0~1023. The maximum data range is divided into four data ranges: 0~63, 64~127, 128~511 and 512~1023. The data compression rate corresponding to the data range 0~63 is 1. The data compression rate corresponding to the data range 64-127 is one-half, the data compression rate corresponding to the data range 128-511 is one-fourth, and the data compression rate corresponding to the data range 512-1023 is one-eighth. The target data range is 64 to 127, and the target data compression rate is one-half.

Step 104: Compress the target EEG data using the target data compression rate to obtain compressed EEG data, so as to store the compressed EEG data.

Optionally, using the target data compression rate to compress the target EEG data to obtain the compressed EEG data includes: performing a shift operation on the target EEG data according to the target compression rate to obtain the shifted EEG data; The sum of the shifted EEG data and the target initial value corresponding to the target data range is determined as the compressed EEG data.

Among them, the target initial value is determined based on the target data EEG data.

Among them, the shift operation refers to: shifting the entire data to the left or right by the corresponding number of bits to obtain new data; when shifting to the right, the low bits are shifted out (discarded), and the high bits are filled with zeros; when shifting to the left, the high bits are shifted out ( Discard), and the low-order vacancies are filled with zeros.

Optionally, the sum of the shifted EEG data and the target initial value corresponding to the target data range is determined as the compressed EEG data, expressed by the following formula:
f=g+s

Among them, f is the compressed EEG data; g is the shifted EEG data; s is the target initial value corresponding to the target data range.

Optionally, perform a shift operation on the target EEG data according to the target compression rate to obtain the shifted EEG data, including: determining the number of shifting bits for the shift operation according to the target compression rate; shifting the target EEG data to the right. number of digits to obtain the shifted EEG data.

In one example, the target data is expressed in binary, the target EEG data is 1110, and the number of shifting bits is 2, then the shifted EEG data is 11.

Optionally, the target EEG data is expressed in p-base, and p is an integer greater than 1; determine the number of shifting bits in the shift operation based on the target compression rate, including: taking p as the base of the opposite of the logarithm of the data compression rate Determine the number of moving digits.

In actual implementation, in order to facilitate the shift operation of the target data, the target EEG data is expressed in binary; at this time, the number of shifting digits of the shift operation is determined according to the target compression rate, including: compressing the data with base 2 The inverse of the logarithm of the rate is determined as the number of moving bits.

Optionally, the inverse of the logarithm of the data compression rate with base p is determined as the number of moving bits, expressed by the following formula:
c＝-log _p b

Among them, p indicates that the target EEG data is in p base; b is the target compression rate; c is the number of moving bits.

In an example, the target EEG data is expressed in binary. At this time, p is 2, and the target compression rate is one quarter, then the number of moving bits is That is 2.

Optionally, before compressing the target EEG data using the target data compression rate, the method further includes: determining a target initial value corresponding to the target data range.

In this embodiment, determining the target initial value corresponding to the target data range includes at least the following two situations:

In the first case, the initial value corresponding to the data range is a preset value, which is pre-stored in the electronic device. At this time, determining the target initial value corresponding to the target data range includes: obtaining the corresponding target data range pre-stored in the data compression device. target initial value.

In the second case, the target initial value is calculated by the data compression device based on the target data range. At this time, determining the target initial value corresponding to the target data range includes: determining from each data range that the EEG data is smaller than the target data range. The reference data range of the EEG data in the surrounding area; according to the target data compression rate, the width of each reference data range and the corresponding data compression rate, determine the target initial value corresponding to the target data range.

Optionally, based on the target data compression rate, the width of each reference data range and the corresponding data compression rate, determining the target initial value corresponding to the target data range includes: comparing the width of each reference data range with the corresponding data compression rate. The sum of the products is determined as the first target value; the product of the sum of the widths of each reference data range and the target data compression rate is determined as the second target value; the difference between the first target value and the second target value is determined as the target initial value .

Optionally, when the number of reference data ranges is 0, the target initial value is 0.

When the reference data range is not 0, the target initial value corresponding to the target data range is determined based on the target data compression rate, the width of each reference data range and the corresponding data compression rate, which is expressed by the following formula:
q＝e ₁ t ₁ +...+e _n t _n -(e ₁ +...+e _n )t ₀

Among them, q is the reference data range; e _i is the width of the i-th reference range, 1≤i≤n; t _i is the data compression rate corresponding to the i-th reference range, 1≤i≤n; n is the reference range number; t ₀ is the target data compression rate.

For example, the maximum data range is 0~1023. The maximum data range is divided into four data ranges: 0~63, 64~127, 128~511 and 512~1023. The data range 0~63 corresponds to a data compression rate of 1. The data compression rate corresponding to the range 64-127 is one-half, the data compression rate corresponding to the data range 128-511 is one-fourth, the data compression rate corresponding to the data range 512-1023 is one-eighth, the target data range is 512~1023, then the reference data range is 0~63, 64~127 and 128~511. It can be obtained that the target initial value is That is 128.

It should be added that in the first case above, the initial value corresponding to the data range pre-stored in the acquisition can also be calculated based on the data range. The initial value is calculated in the same way as in the second case above based on the target data. The method for calculating the target initial value in the range is the same. For the specific calculation process, refer to the second case, which will not be described again in this embodiment.

In order to better illustrate the data compression method of EEG data provided by this application, an example is given below for illustration.

Assume that the EEG data is 10-bit data, that is, the maximum number of data digits of the EEG data is 10 bits, and the maximum data range of the EEG data is 0 to 1023. The preset division method is to divide the maximum data range into four data ranges: 0~63, 64~127, 128~511 and 512~1023. The data compression rate corresponding to the data range 0~63 is 1, and the data range 64~ The data compression rate corresponding to 127 is one-half, the data compression rate corresponding to the data range 128-511 is one-quarter, and the data compression rate corresponding to the data range 512-1023 is one-eighth.

It can be seen from the above that the width of the data range 0~63 is 64, the width of the data range 64~127 is 64, the width of the data range 128~511 is 384, the width of the data range 512~1023 is 512, according to the width of the data range and The compression ratio corresponding to the data range can be calculated. The compressed width of the data range 0 to 63 is 64, the compressed width of the data range 64 to 127 is 32, the compressed width of the data range 128 to 511 is 96, and the data range 512 ~1023The compressed width is 64.

According to the above method, it can be calculated that when the data compression rate is 1, the number of shifting bits is 0, so there is no need to perform a shifting operation on the EEG data in the data range 0 to 63 during the data compression process; similarly, it can be seen that the data compression rate When the data compression rate is one-half, the number of shifting bits is 1 bit, so during the data compression process, the EEG data in the data range of 64 to 127 needs to be shifted right by 1 bit; when the data compression rate is one-quarter, the number of shifting bits is 2 bits, so in the process of data compression, the EEG data in the data range 128 ~ 511 needs to be shifted to the right by 2 bits; when the data compression rate is one-eighth, the number of shifted bits is 3 bits, so in the process of data compression It is necessary to shift the EEG data in the data range 512 to 1023 to the right by 3 bits.

Since the EEG data in the data range 0 to 63 is smaller than the EEG data in other data ranges, the data range There is no reference data range from 0 to 63, that is, the initial value corresponding to the data range 0 to 63 is 0; similarly, the initial value corresponding to the data range 64 to 127 is 32; the initial value corresponding to the data range 128 to 511 is 64; data The initial value corresponding to the range 512~1023 is 128.

Referring to Figure 3, the abscissa is the target EEG data, and the ordinate is the compressed EEG data. The corresponding relationship between the target EEG data and the compressed EEG data is as follows:

When the target EEG data is less than 64, Y=X;

When the target EEG data is greater than or equal to 64 and less than 128:

When the target EEG data is greater than or equal to 128 and less than 512:

When the target EEG data is greater than or equal to 512:

Among them, X is the target EEG data; Y is the target EEG data.

According to the above correspondence, it can be obtained that the EEG data range 0 to 63 before compression corresponds to the EEG data range 0 to 63 after compression; the EEG data range 64 to 127 before compression corresponds to the EEG data range 64 to 95 after compression. ; The range of EEG data before compression 128~511 corresponds to the range of EEG data after compression 96~191; the range of EEG data before compression 512~1023 corresponds to the range of EEG data after compression 192~255. It can be seen from this that compression The maximum data range of the subsequent EEG data is 0~255.

In the above example, the maximum data range of the compressed EEG data is 0 to 255, and there is no overlapping data range, so the compressed EEG data can be stored using 8-bit storage space, and the compressed EEG data is 8 bits. , so the data compression algorithm provided by this application can be used to compress 10-bit EEG data into 8-bit, which can save the storage space of the storage device.

In addition, for a 32-bit storage device, one memory address can only store three 10-bit data, which will result in a waste of two data bits, while one register address can store four 8-bit data without causing any loss of data bits. The waste can improve the utilization of storage addresses, so the storage space of the storage device can be further saved.

For example, assuming the duration of an EEG data is 30s, when directly storing the EEG data, one storage address can store 3 EEG data, so one storage segment can store the EEG data for 90s; while in When storing compressed EEG data, since one storage address can store 4 pieces of EEG data, the duration of EEG data that can be stored in one storage segment is 120 seconds. Therefore, although the 10-bit EEG data is compressed into 8 bits, the compression only reduces the size of the EEG data by 20%, but it increases the storage space utilization by 33%, so storage space can be further saved.

In addition, due to the increased duration of EEG data that the storage device can store, it can meet the data duration requirements of different EEG data analysis and processing algorithms, providing a more flexible algorithm choice for the analysis and processing of EEG data.

It should be supplemented that this embodiment only introduces the complete EEG data compression process once. In actual implementation, one EEG data can be compressed multiple times according to actual needs, that is, the compressed EEG data can be re-formatted. The compressed EEG data is determined to be the target EEG data to be compressed again. This embodiment does not limit the number of compression times of the EEG data.

For example: the number of compressions is three times, and the target EEG data is 20 bits. The compression process is: first, use the compression algorithm provided by this application to compress the 20-bit target EEG data into 18 bits to obtain the first compressed EEG data; then, determine the first compressed EEG data as the target EEG data. Data, again using the compression algorithm provided by this application, will be 18bit The target EEG data is compressed to 16 bits to obtain the second compressed EEG data; finally, the second compressed EEG data is determined as the target EEG data, and the compression algorithm provided by this application is used again to convert the 16-bit target EEG data into 16 bits. The EEG data is compressed to 14 bits, the third compressed EEG data is obtained, and the third compressed EEG data is stored.

In summary, the data compression method for EEG data provided in this embodiment obtains the target EEG data; determines the target data range to which the target EEG data belongs from each data range; the data range is the maximum range of the EEG data. Obtained by dividing the data range; determine the target data compression rate corresponding to the target data range; the data compression rate corresponding to the m-th data range is greater than the data compression rate corresponding to the n-th data range, and the EEG data in the m-th data range Less than the EEG data in the nth data range, m and n are positive integers; use the target data compression rate to compress the target EEG data to obtain the compressed EEG data to store the compressed EEG data; you can Solve the problem that due to the slow change of EEG data, the brain-computer interface device usually collects the EEG data for a long time, which will generate a large amount of EEG data, resulting in a large amount of data stored in the BCI device; due to The target EEG data is compressed first and then the EEG data is stored, so the size of the stored EEG data can be reduced, thereby reducing the amount of data stored by the brain-computer interface device and saving storage space.

At the same time, because the smaller the EEG data is, the data range to which the EEG data belongs corresponds to a greater data compression rate, and the greater the data compression, the higher the accuracy of the compressed data, so the low-level data is in a state of high compression rate or even no compression. status, which can effectively ensure the accuracy of low-order data.

The data compression method for EEG data provided by this application is introduced in detail below.

Figure 4 is a flow chart of a data compression method for EEG data provided by an embodiment of the present application. This application takes the application of this method to the data compression device 120 in the EEG data data compression system shown in Figure 1 as an example. The method at least includes the following steps:

Step 401: Obtain target EEG data.

Step 402: Divide the maximum data range according to a preset dividing method to obtain at least two data ranges.

Step 403: Determine the target data range to which the target EEG data belongs from each data range, and execute steps 404 and 406.

Step 404: Determine the number of bits to shift the target EEG data according to the target compression rate.

Step 405: Shift the target EEG data to the right by the number of shifting digits to obtain the shifted EEG data, and execute step 407.

Step 406: Determine the target initial value corresponding to the target data range, and execute step 407.

Step 407: Determine the sum of the shifted EEG data and the target initial value corresponding to the target data range as the compressed EEG data to store the compressed EEG data.

Optionally, step 404 may be executed before step 406, or may be executed after step 406, or may be executed simultaneously with step 406. This embodiment does not limit the execution order of step 404 and step 406.

The relevant description of this embodiment refers to the above-mentioned embodiment, and this embodiment will not be described again here.

In this embodiment, since the process of compressing the target EEG data is by shifting and adding the target data, It can be realized by using adder and shifter to compress the target EEG data. The hardware requirements are low and it is conducive to the application of data compression algorithm.

Figure 5 is a block diagram of a data compression device for EEG data provided by an embodiment of the present application. This application uses the data compression device 120 in the data compression system for EEG data shown in Figure 1 as an example to illustrate the device. The device at least includes the following modules: a data acquisition module 510, a first determination module 520, The second determination module 530 and the data compression module 540.

Data acquisition module 510, used to acquire target EEG data;

The first determination module 520 is used to determine the target data range to which the target EEG data belongs from each data range; the data range is obtained by dividing the maximum data range of the EEG data;

The second determination module 530 is used to determine the target data compression rate corresponding to the target data range; the data compression rate corresponding to the m-th data range is greater than the data compression rate corresponding to the n-th data range. The data is less than the EEG data in the nth data range, m and n are positive integers;

The data compression module 540 is used to compress the target EEG data using a target data compression rate to obtain compressed EEG data, and to store the compressed EEG data.

Relevant details refer to the above method and system embodiments.

It should be noted that when the data compression device for EEG data provided in the above embodiment performs data compression of EEG data, the division of the above functional modules is only used as an example. In practical applications, the above mentioned functions can be used as needed. Function allocation is completed by different functional modules, that is, the internal structure of the data compression device for EEG data is divided into different functional modules to complete all or part of the functions described above. In addition, the data compression device for EEG data and the data compression method for EEG data provided in the above embodiments belong to the same concept. The specific implementation process can be found in the method embodiments and will not be described again here.

Figure 6 is a block diagram of an electronic device provided by an embodiment of the present application. This application takes the electronic device applied to the data compression device 120 in the data compression system for EEG data shown in Figure 1 as an example. The electronic device at least includes a processor 601 and a memory 602.

The processor 601 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 601 can adopt at least one hardware form among DSP (Digital Signal Processing, digital signal processing), FPGA (Field-Programmable Gate Array, field programmable gate array), and PLA (Programmable Logic Array, programmable logic array). accomplish. The processor 601 may also include a main processor and a co-processor. The main processor is a processor used to process data in the wake-up state, also called CPU (Central Processing Unit, central processing unit); the co-processor is A low-power processor used to process data in standby mode. In some embodiments, the processor 601 may be integrated with a GPU (Graphics Processing Unit, image processor), and the GPU is responsible for rendering and drawing the content that needs to be displayed on the display screen. In some embodiments, the processor 601 may also include an AI (Artificial Intelligence, artificial intelligence) processor, which is used to process computing operations related to machine learning.

Memory 602 may include one or more computer readable and writable storage media, which may be non-volatile. Memory 602 may also include high-speed random access memory, and non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments, the non-volatile computer readable and writable storage medium in the memory 602 is used to store at least one instruction, and the at least one instruction is used to be executed by the processor 601 to implement the method embodiments provided in this application. Data compression method for EEG data.

In some embodiments, the electronic device optionally further includes: a peripheral device interface and at least one peripheral device. The processor 601, the memory 602 and the peripheral device interface may be connected through a bus or a signal line. Each peripheral device can be connected to the peripheral device interface through a bus, a signal line or a circuit board. Illustratively, peripheral devices include but are not limited to: radio frequency circuits, touch display screens, audio circuits, power supplies, etc.

Of course, the electronic device may also include fewer or more components, which is not limited in this embodiment.

Optionally, this application also provides a chip, which may at least include a processor and an internal memory, and optionally include an ADC. The processor can obtain program instructions through the internal memory and be used to run the program instructions to perform the data compression method for EEG data as in the foregoing method embodiment.

The processor may be a hardware circuit with information processing capabilities, or a software instruction, or a combination of hardware and software. During the implementation process, each step of the above method can be completed by instructions in the form of hardware integrated logic circuits or software in the processor. The chip can complete the sending or receiving of data, instructions or information. The processor can use the data, instructions or other information received by the interface to process it, or it can also send the processing completion information through the interface.

Optionally, this application also provides a computer-readable storage medium in which a program is stored, and the program is loaded and executed by the processor to implement the data compression method for EEG data in the above method embodiment.

Optionally, this application also provides a computer product. The computer product includes a computer-readable storage medium. A program is stored in the computer-readable storage medium. The program is loaded and executed by a processor to implement the EEG data of the above method embodiments. data compression method.

The technical features of the above embodiments can be combined in any way. To simplify the description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, all possible combinations should be used. It is considered to be within the scope of this manual.

The above embodiments only express several implementation modes of the present application, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be noted that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all fall within the protection scope of the present application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

A data compression method for EEG data, characterized in that the method includes:

Obtain target EEG data;

Determine the target data range to which the target EEG data belongs from each data range; the data range is obtained by dividing the maximum data range of the EEG data;

Determine the target data compression rate corresponding to the target data range; the data compression rate corresponding to the mth data range is greater than the data compression rate corresponding to the nth data range, and the EEG data in the mth data range is less than the EEG data in the nth data range, the m and n are positive integers;

The target EEG data is compressed using the target data compression rate to obtain compressed EEG data, and the compressed EEG data is stored.
The method according to claim 1, characterized in that before determining the target data range to which the target EEG data belongs from each data range, it further includes:

Divide the maximum data range according to the preset division method to obtain at least two data ranges; the EEG data in the kth data range is greater than the EEG data in the hth data range, and the kth data range The corresponding width is greater than or equal to the width of the h-th data range, and k and h are positive integers.
The method according to claim 1, characterized in that said using the target data compression rate to compress the target EEG data to obtain compressed EEG data includes:

Perform a shifting operation on the target EEG data according to the target compression rate to obtain shifted EEG data;

The sum of the shifted EEG data and the target initial value corresponding to the target data range is determined as the compressed EEG data; the target initial value is determined based on the target data range.
The method according to claim 3, characterized in that, performing a shifting operation on the target EEG data according to the target compression rate to obtain the shifted EEG data includes:

Determine the number of shifting bits of the shift operation according to the target compression ratio;

Shift the target EEG data to the right by the moving number to obtain shifted EEG data.
The method according to claim 4, characterized in that the target EEG data is expressed in p-ary system, and the p is an integer greater than 1;

Determining the number of shifting bits of the shift operation according to the target compression rate includes:

The number of moving bits is determined as the inverse of the logarithm of the data compression rate with p as the base.
The method according to claim 1, characterized in that before using the target data compression rate to compress the target EEG data, it further includes:

Determine the target initial value corresponding to the target data range.
The method of claim 6, wherein determining the target initial value corresponding to the target data range includes:

Determine from each of the data ranges a reference data range in which the EEG data is smaller than the EEG data in the target data range. circumference;

According to the target data compression rate, the width of each reference data range and the corresponding data compression rate, the target initial value corresponding to the target data range is determined.
The method of claim 7, wherein the target initial value corresponding to the target data range is determined based on the target data compression rate, the width of each reference data range and the corresponding data compression rate. ,include:

Determine the sum of the products of the width of each reference data range and the corresponding data compression rate as the first target value;

Determine the product of the sum of the widths of each of the reference data ranges and the target data compression rate as the second target value;

The difference between the first target value and the second target value is determined as the target initial value.
A chip, characterized in that the chip is used to perform the method according to any one of claims 1 to 8.
An electronic device, characterized in that the device includes a processor and a memory; a program is stored in the memory, and the program is loaded and executed by the processor to implement any one of claims 1 to 8 Data compression method for EEG data.
A computer-readable storage medium, characterized in that a program is stored in the storage medium, and when executed by a processor, the program is used to implement the data compression of EEG data according to any one of claims 1 to 8. method.