Electroencephalogram data transmission method combining lossless and lossy compression

Abstract

The invention provides an electroencephalogram data transmission method combining lossless and lossy compression, belongs to the field of electroencephalogram data transmission and compression processing, and is used for solving the problems of low transmission efficiency of pure lossless compression and easy loss of key diagnosis information of pure lossy compression in related technologies. According to the method, multi-dimensional features of electroencephalogram data are extracted, a key area and a non-key area are divided, lossless compression and lossy compression processing are adopted respectively, the area division proportion is dynamically adjusted in combination with transmission bandwidth, signal features and application scenes, and corresponding data are transmitted through dual protocols. The transmission efficiency can be improved while the clinical diagnosis marker and the core characteristics are reserved, and the scene adaptability and the transmission reliability are achieved at the same time.

Description

Technical Field

[0001] The present application relates to the field of EEG data processing and transmission, and in particular to an EEG data transmission method combining lossless and lossy compression. Background Art

[0002] EEG data, characterized by high sampling rates and multiple channels, carries a significant amount of data, requiring high bandwidth for transmission. Existing technologies struggle to meet efficient transmission requirements with simple lossless compression, while simple lossy compression can lead to the loss of critical diagnostic information. This makes it difficult to strike a balance between compression rate and data availability. A method is urgently needed that can improve transmission efficiency while ensuring the validity of critical data. Summary of the Invention

[0003] The present application provides an EEG data transmission method that combines lossless and lossy compression, which can achieve a balance among compression rate, transmission efficiency and data availability by distinguishing between key and non-key areas of EEG data and adapting to different compression methods.

[0004] In a first aspect, the present application provides a method for transmitting EEG data that combines lossless and lossy compression. The method comprises obtaining raw EEG data; dividing the raw EEG data into key areas and non-key areas; processing the divided key area data using a lossless compression algorithm and processing the divided non-key area data using a lossy compression algorithm; and transmitting the compressed key area data and non-key area data.

[0005] By adopting the above technical solution, by obtaining the original EEG data and dividing it into critical and non-critical areas, lossless compression is used for the critical areas to retain the core information, and lossy compression is used for the non-critical areas to reduce the data volume. Finally, two types of compressed data are transmitted, thereby improving the overall compression rate and transmission efficiency while ensuring the availability of critical data.

[0006] Furthermore, the original EEG data is divided into key areas and non-key areas, including: extracting the time domain features, frequency domain features and spatial features of the original EEG data; determining the key feature threshold based on the time domain features, frequency domain features and spatial features; and dividing the original EEG data into key areas and non-key areas according to the key feature threshold.

[0007] By adopting the above technical solution and combining multi-dimensional features to determine the key feature threshold, the division between key areas and non-key areas can be more accurate, avoiding the division deviation caused by a single feature, and ensuring that the key areas can accurately cover the core information.

[0008] Furthermore, the key feature threshold is set based on the clinical diagnostic marker features and task-related core features, the key area is the EEG data area containing the clinical diagnostic marker features or task-related core features, and the non-key area is the EEG data area that does not contain the clinical diagnostic marker features and task-related core features.

[0009] By adopting the above technical solution, key feature thresholds are set based on clinical and task requirements, so that the definition of key areas is closely linked to actual application scenarios, ensuring that the lossless compressed areas directly correspond to core value information, and improving the targeted nature of the compression strategy.

[0010] Furthermore, it also includes: real-time monitoring of transmission bandwidth, EEG signal characteristics and application scenarios; dynamically adjusting the division ratio of the key area and the non-key area according to changes in the transmission bandwidth, EEG signal characteristics and application scenarios.

[0011] By adopting the above technical solution, through real-time monitoring and dynamic adjustment of the division ratio, the compression strategy can adapt to changes in transmission conditions, signal characteristics and scenarios, avoiding resource waste or loss of key information caused by a fixed ratio.

[0012] Furthermore, the division ratio is dynamically adjusted according to the change of the transmission bandwidth, including: when the transmission bandwidth is lower than the preset bandwidth threshold, reducing the proportion of the key area; when the transmission bandwidth is higher than or equal to the preset bandwidth threshold, increasing the proportion of the key area.

[0013] By adopting the above technical solution, the proportion of key areas is dynamically adjusted based on the transmission bandwidth. When the bandwidth is insufficient, transmission efficiency is prioritized, and when the bandwidth is sufficient, the integrity of key data is prioritized, thus achieving the optimal configuration under different bandwidth conditions.

[0014] Furthermore, the division ratio is dynamically adjusted according to the change of the EEG signal characteristics, including: when the EEG signal characteristics have a preset transient event, the range of the key area is expanded; when the EEG signal characteristics are in a stable state, the range of the key area is reduced.

[0015] By adopting the above technical solution, the range of key areas can be dynamically adjusted according to the characteristics of the EEG signal itself, the lossless compression range can be expanded when transient events (such as pathological waves) occur, the compression rate can be improved in a stable state, and the dynamic changes of signal characteristics can be adapted.

[0016] Furthermore, the division ratio is dynamically adjusted according to changes in the application scenario, including: when the application scenario is a clinical diagnosis scenario, controlling the proportion of the key area to be no less than a preset high threshold; when the application scenario is a real-time monitoring scenario, controlling the proportion of the key area to be no less than a preset low threshold, and the preset low threshold is less than the preset high threshold.

[0017] By adopting the above technical solutions, the thresholds of the proportion of key areas are set for different application scenarios to meet the high-fidelity requirements of clinical diagnosis and the efficiency requirements of real-time monitoring, thereby improving the scenario adaptability of the method.

[0018] Furthermore, the divided key area data is processed using a lossless compression algorithm, including: performing linear prediction coding on the key area data to obtain a prediction error sequence; and processing the prediction error sequence using an entropy coding algorithm to obtain lossless compressed data.

[0019] By adopting the above technical solution, the temporal redundancy of key area data is reduced through linear predictive coding, and statistical redundancy is eliminated through entropy coding, thereby improving compression efficiency under the premise of losslessness and reducing the transmission volume of key data.

[0020] Furthermore, the divided non-critical area data is processed using a lossy compression algorithm, including: performing wavelet transform on the non-critical area data to obtain low-frequency coefficients and high-frequency coefficients; determining a quantization step size based on the energy value of the non-critical area data, the quantization step size is positively correlated with the energy value; and quantizing the high-frequency coefficients using the quantization step size to obtain lossy compressed data.

[0021] By adopting the above technical solution, the quantization step size is dynamically adjusted based on the energy value of the non-critical area data, reducing the quantization distortion when the energy is high and increasing the compression rate when the energy is low, thereby maximizing the compression effect of non-critical data within an acceptable range.

[0022] Furthermore, the transmission of the compressed key area data and non-key area data includes: configuring a first transmission protocol for the compressed key area data and configuring a second transmission protocol for the compressed non-key area data; based on the first transmission protocol and the second transmission protocol, transmitting the corresponding compressed data respectively, wherein the reliability of the first transmission protocol is higher than the reliability of the second transmission protocol.

[0023] By adopting the above technical solutions, a high-reliability protocol is used to transmit critical data, and an adapted protocol is used to transmit non-critical data. This ensures the integrity of critical data while improving overall transmission efficiency and balancing reliability and transmission speed.

[0024] In summary, this application has at least the following beneficial effects:

[0025] 1. Provides an EEG data transmission method that balances the availability of key data with transmission efficiency, balancing compression rate and core information retention;

[0026] 2. Through dynamic adjustment mechanisms and multi-dimensional division, the method's adaptability to different scenarios and signal characteristics is improved;

[0027] 3. Through differentiated compression and transmission protocol configuration, the overall performance of data processing and transmission is further optimized.

[0028] It should be understood that the contents described in the Summary of the Invention are not intended to limit the key or important features of the embodiments of the present application, nor are they intended to limit the scope of the present application. Other features of the present application will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The above and other features, advantages and aspects of the embodiments of the present application will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0030] Figure 1 A schematic diagram of an exemplary operating environment in which embodiments of the present application can be implemented is shown.

[0031] Figure 2 A flowchart of an EEG data transmission method combining lossless and lossy compression in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0032] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0033] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the related objects are in an "or" relationship.

[0034] This application provides an EEG data transmission method that combines lossless and lossy compression, which can ensure the integrity of key EEG data to ensure availability, improve compression rate and transmission efficiency, and dynamically adapt to scene changes to balance data quality and transmission performance.

[0035] Figure 1 A schematic diagram of an exemplary operating environment in which embodiments of the present application can be implemented is shown.

[0036] Reference Figure 1 The operating environment includes an EEG signal acquisition device, which is used to collect raw EEG data. The device includes a multi-lead electrode array and a signal preprocessing module. The multi-lead electrode array can synchronously collect EEG signals of multiple channels. The signal preprocessing module is used to filter and denoise the raw signal to remove power frequency interference and environmental noise.

[0037] The operating environment also includes a data processing unit, which is connected to the EEG signal acquisition device, used to receive pre-processed EEG data and perform the division of key areas and non-key areas, as well as corresponding lossless compression and lossy compression processing. The unit has built-in algorithm modules and compression algorithm modules for feature extraction. The feature extraction module can extract the time domain, frequency domain and spatial features of EEG data, and the compression algorithm module includes lossless compression algorithm and lossy compression algorithm.

[0038] The operating environment includes a communication transmission module, which is connected to the data processing unit and is used to receive and transmit compressed key area data and non-key area data. The module supports multiple transmission protocols and can select the corresponding protocol according to the data type. A high-reliability protocol is used for key area data, and an efficient transmission protocol is used for non-key area data.

[0039] The operating environment also includes a monitoring and adjustment unit, which is connected to the data processing unit and the communication transmission module respectively, and is used to monitor the transmission bandwidth, EEG signal characteristics and application scenarios in real time, and send adjustment instructions to the data processing unit based on the monitoring results to dynamically adjust the division ratio between key areas and non-key areas.

[0040] In addition, the operating environment includes a terminal device, which is connected to the communication transmission module and is used to receive the transmitted compressed data and decode and display it. The terminal device can further analyze or display the decoded EEG data according to the requirements of the application scenario.

[0041] Figure 2 FIG1 shows a flow chart of a method for transmitting EEG data combining lossless and lossy compression in an embodiment of the present application. Figure 1 Execute in the operating environment.

[0042] Reference Figure 2 , the method specifically comprises the following steps:

[0043] S1: Obtain raw EEG data.

[0044] The method in this step is implemented through a multi-channel EEG acquisition device, which must meet the following technical parameters: sampling rate for (According to the Nyquist sampling theorem, ensure that the effective frequency band of the EEG signal is covered ), number of channels (Commonly used in clinical scenarios channel), analog-to-digital conversion resolution bit (to ensure the accuracy of microvolt signal acquisition, such as epileptic spike amplitude is usually During the acquisition process, the device suppresses common mode interference through active electrode technology, and the common mode rejection ratio (CMRR) , to reduce the power frequency and myoelectric noise impact.

[0045] The original data is stored in the form of a time series matrix, recorded as ,in Time points is the acquisition time (in seconds), matrix elements Indicates the Channel No. The EEG signal amplitude at the moment (unit: To ensure data quality, the signal-to-noise ratio of each channel needs to be calculated in real time. ,in For the Channel signal power (by comparing the signal frequency band Integral calculation), is the noise power (by Frequency band integral calculation). When the device is triggered to adjust the gain or re-collect, it can ensure the reliability of subsequent processing.

[0046] During the acquisition process, external event markers (such as manual marking of epileptic seizures and switching of sleep stages) are recorded synchronously. The marker signal is in binary sequence. Storage, where Indicates the There are always event triggers, which are used for timing anchoring when dividing key areas later. Digital sequences can also be used to store marking signals. Indicates that there is no time trigger at time t. If it is non-zero, it means that there is an event trigger at time t, and it can be used After data collection is completed, it is transmitted to the edge computing unit via USB3.0 or wireless transmission (such as Wi-Fi6). The original data is verified before transmission. The verification formula is: , ensuring data transmission integrity.

[0047] S2: Divide the original EEG data into key areas and non-key areas.

[0048] The method of this step specifically includes: extracting the time domain features, frequency domain features and spatial features of the original EEG data; determining the key feature threshold based on the time domain features, frequency domain features and spatial features; and dividing the original EEG data into key areas and non-key areas according to the key feature threshold.

[0049] Among them, the key feature threshold is set based on the clinical diagnostic marker characteristics and task-related core features, the key area is the EEG data area containing the clinical diagnostic marker characteristics or task-related core features, and the non-key area is the EEG data area that does not contain the clinical diagnostic marker characteristics and task-related core features.

[0050] When extracting time domain features, the original EEG data matrix , calculate the local peak value of each channel : ( For the window size, take sampling points, corresponding to , adapting to the time scale of EEG transient events), and defining the peak threshold (For example, the peak threshold of epileptic spikes in clinical diagnosis is set as ); while calculating the absolute value of the first-order difference , used to characterize the signal mutation degree, mutation threshold Set as (Based on the dynamic adjustment of the average mutation rate over the entire period, is the mean function).

[0051] Frequency domain feature extraction uses short-time Fourier transform (STFT) to frame each channel data (frame length 256 sampling points, overlap rate ), calculate the frequency resolution ( is the frame length), and the frequency domain matrix ( is the frequency point number, The extracted features include the energy proportion of each frequency band: ( The energy proportion of the band corresponds to the key characteristics of sleep stages). ( The energy proportion of the band corresponds to the core characteristics of the motor imagery task), and the frequency band energy threshold is set (like Band energy accounts for more than is marked as a key feature of sleep).

[0052] Spatial features are calculated by cross-channel correlation to define channels and Pearson correlation coefficient ,in is the covariance, is the standard deviation. Synchronicity threshold Set to 0.7 (based on clinical data statistics, the synchronization of adjacent lesion channels during epileptic seizures is usually ),when When , it is determined that there is a strong spatial correlation between the two channels.

[0053] The determination of key feature thresholds requires integration of clinical priors and data statistics: Peak threshold Take the peak distribution of EEG of healthy people Quantiles (such as ), frequency band energy threshold Based on task requirements (such as sleep stages Band ), synchronicity threshold When dividing, generate a feature vector for each segment of data (time window length 1s) ( is the average correlation coefficient with adjacent channels, is a vector combination symbol, which is used to combine multiple features in brackets into a vector as needed). When at least one feature exceeds the corresponding threshold, the period is marked as a critical area, otherwise it is a non-critical area, and a binary mask matrix is ​​finally generated. ( Indicates the Channel No. time is the critical area).

[0054] S3: A lossless compression algorithm is used to process the data in the divided key areas, and a lossy compression algorithm is used to process the data in the divided non-key areas.

[0055] In the method of this step, the divided key area data is processed using a lossless compression algorithm, including: performing linear prediction coding on the key area data to obtain a prediction error sequence; and processing the prediction error sequence using an entropy coding algorithm to obtain lossless compressed data.

[0056] The method of processing the divided non-critical area data using a lossy compression algorithm also includes: performing wavelet transform on the non-critical area data to obtain low-frequency coefficients and high-frequency coefficients; determining a quantization step size based on the energy value of the non-critical area data, wherein the quantization step size is positively correlated with the energy value; and quantizing the high-frequency coefficients using the quantization step size to obtain lossy compressed data.

[0057] Key area data through the mask matrix Extraction, i.e. ( is the element-wise product), where ( is the number of key area time points). In linear prediction coding, the key data of each channel use Order linear prediction model: ,in is the prediction coefficient, Characterize the key data of each channel by minimizing the mean square error Solution, It is the mathematical expectation symbol, which is used to average the random variables in the brackets (such as the squared error term) in the probability space or sample set, to measure the average level of the random quantity in an overall and stable manner, and to provide an indicator reflecting the long-term or global performance for algorithm optimization (such as minimizing the mean square error). It is estimated using the Yule-Walker equation:

[0058] , , For the Autocorrelation function of channel key data, The forecast error sequence is , when performing Huffman entropy coding, first count the frequency distribution of the error value , is a counting function used to count the number of times the error value e appears. The number of key area time points mentioned above is used to construct a prefix code tree based on the distribution, so that the high-frequency error value corresponds to the short code word, and the encoded data length satisfies (Close to the lower limit of information entropy).

[0059] Non-critical area data is , is the non-critical area data, X is the original EEG data matrix mentioned above, M is the mask matrix for extracting the key area data mentioned above, first perform a two-dimensional wavelet transform (time channel), using DB4 wavelet basis to decompose 3 layers and obtain the low-frequency approximate coefficients (Low-frequency approximation coefficient is used to preserve the overall trend of the signal) and high-frequency detail coefficient ( is the number of decomposition layers, for direction), It is based on the original EEG time sampling points, and after mask screening, the number of samples in the non-critical area time dimension is naturally determined. It is an inherent property after the EEG data dimension is split (similar to the logic of the key area time point number, implicit in the data processing flow). Calculate the energy value of the non-critical area data , C is the number of channels mentioned above, and the quantization step size according to Sure is an empirical coefficient, and the high-frequency detail coefficient is used to ensure finer quantization when the energy is high). To quantify: (Rounded to the nearest Integer multiples), is the high frequency detail coefficient The result after quantization, low-frequency approximation coefficient Do not quantize to preserve the basic features. The quantized data is spliced ​​with the low-frequency coefficients to form lossy compressed intermediate data, where the quantization error of the high-frequency coefficients satisfies (Based on the uniform distribution assumption), that is, assuming that the values ​​of the original data (such as high-frequency detail coefficients) are uniformly distributed within the quantization interval, It is the mathematical expectation of the square of the error generated when quantizing high-frequency detail coefficients based on the uniform distribution assumption. In theory, it is approximately 1 / 12 of the square of the quantization step size and is used to measure the degree of quantization distortion.

[0060] S4: Transmitting the compressed key area data and non-key area data.

[0061] The method of this step specifically includes: configuring a first transmission protocol for the compressed key area data, and configuring a second transmission protocol for the compressed non-key area data; based on the first transmission protocol and the second transmission protocol, transmitting the corresponding compressed data respectively, wherein the reliability of the first transmission protocol is higher than the reliability of the second transmission protocol.

[0062] The compressed key area data is recorded as (data block after lossless compression), non-critical area data is recorded as (data blocks after lossy compression), both need to be encapsulated with additional metadata: metadata includes compression parameters (prediction order of key areas , entropy coding dictionary version; number of wavelet decomposition layers in non-critical areas , quantization step size ), timestamp (data start time), check information (CRC32 check code is used in key areas , simple checksum is used in non-critical areas

[0063] The first transport protocol uses TCP (Transmission Control Protocol) and is configured as follows: Sliding window size (Unit: byte), where is the real-time bandwidth (unit: bps), is the round-trip delay (measured by ICMP protocol, taking the average of nearly 10 times), Indicates rounding down; retransmission timeout , when the key data message is lost (more than The second transmission protocol uses UDP (User Datagram Protocol), disables the checksum field (reduces protocol overhead), and sets the maximum message length. Bytes (adapting to Ethernet MTU limit), no retransmission is triggered when non-critical data packets are lost.

[0064] Bandwidth allocation is required before transmission, and priority weights of key data are defined , non-critical data weight , then the actual allocated bandwidth satisfies ( is the current available bandwidth, Allocate bandwidth for critical data transmission. is the bandwidth allocated for non-critical data transmission). ( Minimum bandwidth required for critical data, , When the maximum allowable transmission delay is reached, priority is given to ensuring the transmission of critical data, and non-critical data transmission is suspended until bandwidth is restored. It is an operation to calculate the amount of data. Indicates key data The amount of data.

[0065] During data transmission, a control frame is sent synchronously, which contains the current compression parameters (such as the entropy coding dictionary index for lossless compression of key areas, the quantization step size for non-key areas, and the quantization step size for non-key areas). ) and transmission status flags (such as Indicates that critical data is being transmitted. Indicates that non-critical data is being transmitted). The receiving end calls the corresponding decoding algorithm by parsing the control frame, where the CRC32 checksum is required to be verified for critical data decoding ( ,when Non-critical data is directly reconstructed based on inverse wavelet transform without the need for verification and matching.

[0066] Furthermore, the method also includes: real-time monitoring of transmission bandwidth, EEG signal characteristics and application scenarios; and dynamically adjusting the division ratio between the key area and the non-key area according to changes in the transmission bandwidth, EEG signal characteristics and application scenarios.

[0067] The partitioning ratio is dynamically adjusted according to the change of the transmission bandwidth, including: when the transmission bandwidth is lower than the preset bandwidth threshold, reducing the proportion of the key area; when the transmission bandwidth is higher than or equal to the preset bandwidth threshold, increasing the proportion of the key area.

[0068] The division ratio is dynamically adjusted according to the change of the EEG signal characteristics, including: when the EEG signal characteristics have a preset transient event, the range of the key area is expanded; when the EEG signal characteristics are in a stable state, the range of the key area is reduced.

[0069] The division ratio is dynamically adjusted according to changes in the application scenario, including: when the application scenario is a clinical diagnosis scenario, controlling the proportion of the key area to be no less than a preset high threshold; when the application scenario is a real-time monitoring scenario, controlling the proportion of the key area to be no less than a preset low threshold, and the preset low threshold is less than the preset high threshold.

[0070] When monitoring transmission bandwidth in real time, the sliding window averaging method is used to calculate the effective bandwidth ( is the window size, taking 10 sampling points, For the The instantaneous bandwidth measured in Mbps). Preset bandwidth threshold Based on the minimum transmission requirements of key data: ,in The minimum rate of compressed key data is the time window, which is 1 second). By increasing the key feature threshold (such as peak threshold from Adjust to , mutation threshold improve Reduce the proportion of key areas and adjust the formula to is the adjustment coefficient); when When the threshold is lowered (such as ) to increase the proportion of key areas.

[0071] EEG signal feature monitoring is achieved by real-time calculation of transient event detection indicators: defining transient event scores , is the weight, when When it is determined to be a preset transient event (such as epileptic spikes and sleep spindles), the key area is expanded to include the areas before and after the event. (corresponding to 51 sampling points at a sampling rate of 512Hz) included in the key area, that is, the mask matrix Expanded to the event time window (Before and after the original event window range). When If the duration exceeds 5 seconds, it is considered to be a stable state and the key area is narrowed: the mask value of the non-event area (In the original key area part).

[0072] Application scenario monitoring through scene recognition tags (0 for real-time monitoring, 1 for clinical diagnosis) is achieved, and the label is generated by external input (such as manual switching by the user) or scene recognition model (based on characteristics such as device location, data sampling rate, etc.). Preset high threshold (clinical diagnosis), preset low threshold (Real-time monitoring), proportion of key areas ( is the total time points) must meet the following requirements: hour, , by lowering all feature thresholds (e.g. ) to ensure that the standards are met; hour, , allowing the threshold to be raised to reduce the proportion while ensuring that the core features are not lost (such as in the motor imagery task Band energy area is still retained), It is the scene recognition label mentioned above, with a value of 0 (real-time monitoring) or 1 (clinical diagnosis), which is used to distinguish the application scenarios of EEG data and is generated by external input or scene recognition model. In clinical diagnosis scenarios, in order to meet the requirements of key area proportion, the original time threshold Adjusted value ( ), which is used to dynamically adapt the key area determination.

[0073] Dynamically adjusted smooth transition mechanism: All changes in thresholds and mask matrices are implemented using linear interpolation, i.e. ( To adjust the triggering moment, To complete the moment), avoid distortion of data features caused by mutations.

[0074] Based on the above content, the method of this step collects raw EEG data through multi-channel high-precision to provide a high-fidelity data source for subsequent processing, combines spatiotemporal feature extraction and quantization thresholds to divide key areas, so that core information containing clinical diagnostic markers can be accurately identified; lossless compression is performed on key areas through linear prediction and entropy coding, while eliminating temporal redundancy and retaining feature details, lossy compression is performed on non-critical areas with the help of wavelet transform and dynamic quantization step size, and efficient data reduction is achieved by utilizing spatial and frequency domain redundancy; the dual-protocol transmission mechanism adopts a reliable retransmission strategy for critical data to ensure that core information is not lost, and a lightweight protocol is used for non-critical data to improve transmission efficiency; the dynamic adjustment mechanism flexibly optimizes the area division ratio by real-time monitoring of bandwidth, signal characteristics and scene changes, so that the compression strategy adapts to different transmission environments and data characteristics.

[0075] The synergistic effect of the above-mentioned technical means can achieve the goal of significantly reducing the amount of data through layered compression to improve transmission efficiency during the transmission of EEG data, and ensuring that the core features related to clinical diagnosis and tasks are not lost through lossless processing of key areas and dynamic adaptation mechanisms. At the same time, with the help of dual-protocol transmission and bandwidth allocation strategies, the transmission reliability and real-time performance of different data are balanced, ultimately forming a set of EEG data transmission solutions that are both efficient, reliable and adaptable to scenarios, meeting the diverse application needs from clinical diagnosis to real-time monitoring.

[0076] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to the embodiments of this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required for this application.

[0077] In summary, this application has at least the following beneficial effects:

[0078] 1. By adopting a layered strategy of lossless compression for critical areas and lossy compression for non-critical areas, the data transmission volume is significantly reduced while ensuring the complete preservation of clinical diagnostic markers and core task features, thus balancing data availability and transmission efficiency.

[0079] 2. A dynamic adjustment mechanism based on transmission bandwidth, signal characteristics, and application scenarios can flexibly optimize the division ratio between key and non-key areas, enabling the method to adapt to different transmission environments and data characteristics, improving scenario adaptability.

[0080] 3. High-reliability protocols and lightweight protocols are used for transmission of critical data and non-critical data respectively, which not only ensures that core information is not lost, but also improves the overall transmission speed, taking into account the reliability and real-time performance of transmission to meet diverse application needs.

[0081] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this application is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the aforementioned disclosed concepts. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A method for transmitting EEG data combining lossless and lossy compression, characterized in that: include: Obtain raw EEG data; Dividing the original EEG data into key areas and non-key areas; The data in the key areas after division are processed by lossless compression algorithm, and the data in the non-key areas after division are processed by lossy compression algorithm; Transmitting compressed key area data and non-key area data; The raw EEG data is divided into key areas and non-key areas, including: Extracting time domain features, frequency domain features and spatial features of the original EEG data; Determining a key feature threshold based on the time domain features, frequency domain features, and spatial features; The original EEG data is divided into a key area and a non-key area according to the key feature threshold.

2. The EEG data transmission method combining lossless and lossy compression according to claim 1, characterized in that: The key feature threshold is set based on the clinical diagnostic marker features and task-related core features. The key area is the EEG data area that contains the clinical diagnostic marker features or task-related core features, and the non-key area is the EEG data area that does not contain the clinical diagnostic marker features and task-related core features.

3. The EEG data transmission method combining lossless and lossy compression according to claim 1, characterized in that: Also includes: Real-time monitoring of transmission bandwidth, EEG signal characteristics and application scenarios; The division ratio between the key area and the non-key area is dynamically adjusted according to changes in the transmission bandwidth, EEG signal characteristics and application scenarios.

4. The EEG data transmission method combining lossless and lossy compression according to claim 3, characterized in that: Dynamically adjusting the division ratio according to changes in the transmission bandwidth includes: When the transmission bandwidth is lower than a preset bandwidth threshold, reducing the proportion of the key area; When the transmission bandwidth is higher than or equal to the preset bandwidth threshold, the proportion of the key area is increased.

5. The EEG data transmission method combining lossless and lossy compression according to claim 3, characterized in that: Dynamically adjusting the division ratio according to changes in the EEG signal characteristics includes: When a preset transient event occurs in the EEG signal feature, expanding the scope of the key area; When the EEG signal feature is in a stable state, the scope of the key area is narrowed.

6. The EEG data transmission method combining lossless and lossy compression according to claim 3, characterized in that: Dynamically adjust the division ratio according to changes in the application scenario, including: When the application scenario is a clinical diagnosis scenario, controlling the proportion of the key area to be no less than a preset high threshold; When the application scenario is a real-time monitoring scenario, the proportion of the key area is controlled to be no less than a preset low threshold, and the preset low threshold is less than the preset high threshold.

7. The EEG data transmission method combining lossless and lossy compression according to claim 1, characterized in that: The data in the divided key areas are processed using a lossless compression algorithm, including: Performing linear prediction coding on the key area data to obtain a prediction error sequence; The prediction error sequence is processed using an entropy coding algorithm to obtain lossless compressed data.

8. The EEG data transmission method combining lossless and lossy compression according to claim 1, characterized in that: The data in the non-critical areas after division is processed using a lossy compression algorithm, including: Performing wavelet transform on the non-critical area data to obtain low-frequency coefficients and high-frequency coefficients; determining a quantization step size based on the energy value of the non-critical area data, wherein the quantization step size is positively correlated with the energy value; The high frequency coefficients are quantized using the quantization step size to obtain lossy compressed data.

9. The EEG data transmission method combining lossless and lossy compression according to claim 1, characterized in that: Transmit compressed key area data and non-key area data, including: Configuring a first transmission protocol for compressed data in the key area and configuring a second transmission protocol for compressed data in the non-key area; Based on the first transmission protocol and the second transmission protocol, corresponding compressed data is transmitted respectively, wherein the reliability of the first transmission protocol is higher than the reliability of the second transmission protocol.

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