CN116115238A - A Distributed EEG Signal Calculation Method Supporting Data Compression and Encryption - Google Patents

Abstract

Aiming at problems such as inflexible deployment of the brain-computer interface system, power consumption of EEG acquisition equipment, and unsafe data transmission, the present invention proposes a distributed EEG signal calculation method that supports data compression and encryption, and combines different calculations of the brain-computer interface system The process is deployed on different computing nodes in a distributed manner. The nodes transmit data through the wireless network, compress and encrypt the EEG signals sent by the EEG acquisition equipment, and encrypt the data transmitted between the computing nodes. The receiving and filtering program, the feature extraction and classification program, and the external device control program are respectively deployed on the computing nodes. The invention solves the problem of inflexibility caused by an EEG signal calculation process on one calculation node, increases the working time of a portable EEG acquisition device, and improves the security of EEG signal network transmission.

Description

A Distributed EEG Signal Calculation Method Supporting Data Compression and Encryption

technical field

The invention relates to a distributed EEG signal calculation method supporting data compression and encryption, which is mainly used in the field of brain-computer interface, and distributes the calculation process of EEG signals on different computing nodes, and the EEG data between nodes It is transmitted through the network, and the transmitted EEG data is compressed and encrypted to improve the practicability, security, and flexibility of the brain-computer interface system.

Background technique

Brain-computer interface (brain-computer interface, BCI) technology refers to the establishment of a connection between the human brain and external devices to realize the information exchange between the brain and the device. Usually, the computer processes the EEG signal and transmits the processing results. to external devices. EEG signals are the overall reflection of the electrophysiological activities of brain nerve cells in the cerebral cortex, which contains a large amount of physiological information, which can be used to provide diagnostic basis for certain brain diseases, and can also be used to analyze the real emotions of the brain, or to Control external devices. In the brain-computer interface system, there are special equipment to collect EEG signals and transmit them to the computer. After receiving the EEG signals, the computer obtains the real intention of the brain through signal processing, feature extraction and classification, and sends the intention to the computer. Provide external equipment to achieve the purpose of brain-controlled external equipment and brain awareness monitoring.

The components of a common brain-computer interface system include EEG signal acquisition equipment, EEG signal processing computer, external response equipment, etc., and data is generally transmitted between each device through a wireless network. EEG signal acquisition equipment is usually a portable device such as a helmet, which collects cerebral cortex signals in real time and sends them to the processing computer during work. In many application scenarios, the working time of EEG acquisition equipment is a very important requirement, and portable The job of the EEG acquisition equipment is to collect and send EEG signals. Due to the multi-channel and high sampling rate, the acquisition equipment needs to send a large amount of data to the processing computer through the wireless network, which causes most of the energy consumption of the acquisition equipment to be Caused by wireless transmission of EEG data. In order to prolong the working hours of the EEG acquisition equipment, the present invention adopts a data compression method to reduce the amount of network data transmission and improve the practicability of the brain-computer interface system.

In some scenarios, such as military scenarios, or the need to encrypt and protect the transmitted EEG signals, it is necessary to avoid being eavesdropped on the real brain thoughts by others during the EEG signal network transmission process. The existing EEG signal transmission The methods are all in experimental scenarios, without considering privacy protection and secure transmission. The present invention uses data encryption to protect the real intention of the brain, so that the brain can be safely used in scenarios with high privacy requirements. - Machine interface system.

The brain-computer interface system includes multiple steps such as collection, calculation, and response of the EEG signal. After the EEG signal processing computer receives the EEG signal, it needs to perform data preprocessing. EEG signals, EEG signals, etc. are filtered out, and then feature extraction and classification are performed on the filtered EEG signals, and finally the classification results are sent to the external device control program to achieve the purpose of brain-controlled external devices. In this process, the existing methods usually implement these programs on the same computing node, which limits the application flexibility of the brain-computer interface system. The invention deploys the EEG signal receiving and filtering program, the feature extraction and classification program, and the external device control program in a distributed manner to run on different computing nodes, thereby improving the flexibility of the brain-computer interface system.

The method proposed by the invention can improve the practicability, safety and flexibility of the brain-computer interface system.

Contents of the invention

The purpose of the present invention is to propose a distributed EEG signal calculation method that supports data compression and encryption, deploying different calculation processes of the brain-computer interface system to different computing nodes in a distributed manner, and transmitting data between nodes through a wireless network Data, the present invention supports data compression and encryption in the process of EEG signal calculation, which can improve the practicability, security, and flexibility of the brain-computer interface system.

In order to achieve the above object, the present invention divides the calculation process of the EEG signal into three programs, namely the receiving and filtering program, the feature extraction and classification program, and the external device control program. These three programs can run on different computing machines. On the node, there will also be a management program to configure parameters, process control, and EEG signal viewing for the three programs. The management program can also be deployed on different computing nodes, such as tablet computers.

In the present invention, the work sequence and the main process between each program are as shown in Figure 1, forming a brain-computer interface system, the main steps are as follows:

① The EEG signals from the cerebral cortex collected by the EEG acquisition equipment will be sent to the receiving and filtering program through the wireless network;

②The receiving and filtering program filters and preprocesses the received EEG signal, and then sends it to the feature extraction and classification program;

③The feature extraction and classification program extracts the features of the received EEG signals, recognizes the real intention of the brain, classifies them into specific categories, and sends the categories to the external device control program;

④ The external device control program converts the received category information into specific control instructions, such as controlling seat movement, displaying spelling content, game command control, etc., and realizes interaction with external devices by calling the external device programming interface.

The invention separates each step of the brain-computer interface system into different programs and distributes them on different computing nodes, which can improve the flexibility of the brain-computer interface system. Since the data volume of EEG signals is usually large, especially in the case of multi-channel high sampling rate, the traffic can reach 7Gb in one hour, and the transmission of such a large amount of data through wireless networks will seriously affect the working hours of portable EEG acquisition equipment. . The present invention performs data compression in step ① in FIG. 1 , that is, compresses the EEG signal before sending it, and reconstructs the received data at the receiving and filtering program to obtain the original EEG signal. Since the present invention distributes the various steps of the brain-computer interface system on different computing nodes, and the EEG signals are transmitted through the wireless network, in order to protect the privacy of users and improve the security of data transmission such as EEG signals, Encrypted transmission is performed when transmitting data between EEG acquisition equipment and computing nodes, that is, the data transmission is encrypted in the second and third steps. Step ④ of the present invention usually calls the programming interface of the external device, and the secure data transmission is completed by the programming interface of the external device.

The specific process of the distributed EEG signal calculation method supporting data compression and encryption proposed by the present invention is shown in Figure 2. The EEG acquisition equipment, receiving and filtering program, feature extraction and classification program, and external device control program will be respectively By performing different steps, compared with the traditional brain-computer interface system, the present invention not only supports the distributed deployment of each program, but also supports the data compression and encryption of the communication between each program. Each concrete step of the present invention is as follows:

(1) EEG signal acquisition: EEG acquisition equipment obtains EEG signals from various positions in the cerebral cortex by contacting with the cerebral cortex, samples at the set sampling rate, and performs data combination according to the number of channels used, such as In the case of 250Hz sampling rate, 8 channels are sampled at the same time, then the 8 channels will sample 250 times of data per second, and the acquisition device will combine the data according to a fixed time window N. For example, if N is 6, it means that the EEG signals collected 6 times Combined together, T1 ₁ , T2 ₁ , T3 ₁ , T4 ₁ , T5 ₁ , T6 _{1 , T7 1 , T8 1 ,..., T1 6 , T2 6 , T3 6 ,} T4 ₆ , T5 ₆ , _{T6 6.} T7 ₆ , T8 ₆ , where T1 ₁ represents the data collected for the first time on the first channel, and T8 ₆ represents the data collected for the sixth time on the eighth channel, and follow-up with the EEG signals collected for the 6 times as the granularity processing and network transmission;

(2) EEG signal compression: for the EEG signal in the fixed time window N, the present invention performs data compression of the EEG signal, and the specific compression method can adopt a mature existing algorithm;

(3) EEG signal encryption: for the compressed EEG signal, the present invention uses a symmetric cryptographic algorithm to encrypt, and the encrypted key is negotiated between the sending end and the receiving end, and can be obtained quickly if the domestic SM4 symmetric cryptographic algorithm is adopted. encrypted data;

(4) EEG signal decryption: After receiving the encrypted data transmitted by the EEG acquisition device through the network, the receiving and filtering program uses the same key to decrypt according to the symmetric cryptographic algorithm to obtain the compressed data;

(5) EEG signal reconstruction: the receiving and filtering program reconstructs the compressed data into the original EEG signal according to the decompression algorithm;

(6) Filtering preprocessing: use commonly used filtering algorithms for EEG signal preprocessing, including the use of low-pass filters or notch filters to eliminate power frequency interference in EEG signals, etc. The result obtained is a more realistic and accurate brain image. electric signal;

(7) EEG signal encryption: the preprocessed EEG signal is encrypted using a symmetric cryptographic algorithm, and the encrypted key is negotiated between the sending end and the receiving end;

(8) EEG signal decryption: After the feature extraction and classification program receives the encrypted data transmitted from the network, it uses the same key to decrypt according to the symmetric cryptographic algorithm to obtain the EEG signal without interference;

(9) Feature extraction and classification: feature extraction of EEG signals from the time domain and frequency domain, classify the EEG signals based on the extracted features, and identify the true intention of the brain;

(10) Classification result encryption: the classification result is encrypted using a symmetric cryptographic algorithm, and the encrypted key is negotiated between the sending end and the receiving end;

(11) Classification result decryption: After receiving the encrypted data transmitted from the network, the external control program uses the same key to decrypt according to the symmetric cryptographic algorithm to obtain the result of the classification of the EEG signal;

(12) External device control: convert the results of EEG signal classification into control instructions for external devices, or spelled target characters, etc., and then call the programming interface of the external device to complete the external control or intention presentation of the brain's true intentions, etc. .

The beneficial effect of the present invention is to improve the flexibility, practicability, and safety of the brain-computer interface system, solve the problem of inflexible use of the EEG signal calculation process on one computing node, and improve the working hours of the portable EEG acquisition device , and encrypts the data transmitted between computing nodes in the brain-computer interface system through the network, which improves the security of EEG signal transmission.

Description of drawings

Fig. 1 is a diagram of the working process of each program in the distributed brain-computer interface system;

Fig. 2 is a specific process diagram of distributed EEG signal calculation supporting data compression and encryption;

Fig. 3 is a parameter configuration interface diagram on the management program;

FIG. 4 is an effect diagram of visually displaying EEG signals in the form of waveform diagrams on the interface of the management program.

Detailed ways

In order to better describe how the present invention implements the distributed EEG signal calculation method that supports data compression and encryption, the processes of data compression, data encryption, and distributed EEG signal calculation in the method are described separately.

(1) Data compression of EEG signals

The data compression in the present invention mainly compresses the EEG signals collected by the EEG acquisition equipment, reduces the network data transmission amount of the portable EEG acquisition equipment, and achieves the effect of reducing power consumption. There are many kinds of algorithms for data compression. The present invention adopts the defalte method suitable for EEG signal data compression. The idea of the method is: first replace the repeated character strings in the data into (distance, length) pairs; The data, using huffman encoding, re-encodes the characters according to their frequency. Since the EEG acquisition equipment is usually multi-channel, and the sampling rate can also be set according to different application requirements, the present invention tests the size of the data that the EEG acquisition equipment needs to send under different circumstances with or without data compression , see Table 1 (data obtained by taking the average value of 1000 experiments).

Table 1 Comparison of data size sent by EEG acquisition equipment per second with or without compression (kb)

The present invention calculates the data compression rate of the EEG signal under different conditions. The calculation formula of the compression rate is: the data size after compression is divided by the data size before compression, and the results are shown in Table 2. After the EEG signal is compressed, the data size does not exceed 50% of the original uncompressed condition, and as the number of channels increases, the compression ratio gradually decreases, indicating that the more channels there are, the more duplicate data exists and the compressed The better the effect. The results from Table 1 and Table 2 show that the present invention can effectively reduce the amount of network data transmission of the portable EEG acquisition device.

Table 2 Compression rate of EEG acquisition equipment in different situations per second

Sampling rate(Hz) 8 channels 16 channels 32 channels 64 channels 160 45.0% 35.2% 27.4% 22.5% 250 45.1% 35.0% 27.3% 22.6% 500 44.8% 34.8% 27.4% 22.6% 1000 44.9% 35.1% 27.4% 22.6%

Since the present invention needs to compress and reconstruct the EEG signal, compared with the way of directly transmitting the original EEG signal, it will bring some additional calculation operations, resulting in a time delay of the EEG signal. In the case of different channel numbers and different sampling rates, the present invention tests the extra time delay between the EEG acquisition device and the computing node due to EEG signal compression, as shown in Table 3. It can be seen from the results in Table 3 that the present invention compresses the EEG signal of the EEG acquisition device before transmitting it, and the delay brought by it is within 50 microseconds, which will not affect the normal use of the brain-computer interface system.

Table 3 The extra time delay (us) brought by EEG acquisition equipment compression and computing node reconstruction EEG signal

Sampling rate(Hz) 8 channels 16 channels 32 channels 64 channels 160 32.4 33.0 37.2 45.3 250 32.2 33.7 37.5 47.2 500 32.7 35.5 36.6 46.4 1000 34.9 35.0 37.1 46.5

(2) Encryption of EEG signals and classification results

In the present invention, various parts of the brain-computer interface system are distributed and deployed on different computing nodes, and the computing nodes transmit data to each other through the network. In order to protect the security of data transmission, the EEG signals or classification results transmitted through the network are processed encryption. The present invention uses the domestic encryption algorithm SM4 to encrypt, and tests the extra time delay caused by the encryption and decryption of the EEG signal, which does not exceed 17 microseconds, as shown in Table 4. It can be seen from the results in Table 4 that the present invention can improve the security after encrypting the EEG data without affecting the real-time performance of the brain-computer interface system.

Table 4 The extra time delay (us) caused by the EEG acquisition equipment using the SM4 cryptographic algorithm to encrypt and decrypt EEG signals

Sampling rate(Hz) 8 channels 16 channels 32 channels 64 channels 160 13.9 13.8 13.9 14.0 250 13.8 14.2 13.7 13.9 500 14.1 13.6 14.1 13.8 1000 16.2 13.8 13.9 14.0

(3) Distributed EEG signal calculation

In the present invention, a receiving and filtering program, a feature extraction and classification program, and an external device control program are designed to receive EEG signals, and realize preprocessing, feature extraction and classification, and external device responses of EEG signals on different computing nodes. The present invention also designs a management program to perform parameter configuration, process control, and EEG signal viewing for the three programs.

According to Fig. 1, it can be seen that there is a sequence relationship among the three programs of the present invention, the receiving and filtering program, the feature extraction and classification program, and the external device control program, and the transmitted data is unidirectional, including EEG signal data , program working status data, etc. The EEG signal data is the data collected by the EEG acquisition equipment; while the program working status data is used to indicate the status of the program on the current computing node, which is transmitted before the official work of the brain-computer interface system, and is used to help each program into the ready state.

The data transmitted between the management program and the other three programs in the present invention are two-way, and there are many types of transmitted data, including working status data, parameter data, and EEG data for visualization. The working status data is used to reflect whether there is a communication error between the computing nodes or whether the program on the computing node is running normally, etc.; the parameter data is mainly to help the user set different parameters on the management program to control the running information of the program on each computing node. Figure 3; the EEG data used for visualization is used to visualize the EEG signals on the computing nodes in the form of waveform diagrams on the interface of the management program, see Figure 4.

Claims (12)

1. A distributed EEG signal calculation method supporting data compression and encryption, characterized in that different calculation processes of the brain-computer interface system are distributed to different computing nodes, and the nodes are transmitted through a wireless network Data, compress and encrypt the EEG signals sent by the EEG acquisition equipment, encrypt the data transmitted between computing nodes, deploy receiving and filtering programs, feature extraction and classification programs, and external device control on different computing nodes program. 2. The distributed EEG signal calculation method supporting data compression and encryption according to claim 1, wherein the EEG acquisition device obtains the EEG signals at various positions of the cerebral cortex by contacting the cerebral cortex According to the set sampling rate and the number of channels used, data compression is performed on the EEG signals collected in a fixed time window, and a mature compression method is used to achieve. 3. the distributed EEG signal calculation method supporting data compression and encryption according to claim 1, wherein the EEG acquisition device encrypts the compressed EEG signal using a symmetric cryptographic algorithm, and the encrypted key The key is generated through negotiation between the data sender and the receiver. 4. The distributed EEG signal calculation method supporting data compression and encryption according to claim 1, wherein the receiving and filtering program uses the same key to decrypt the received encrypted data according to the symmetric cryptographic algorithm . 5. The distributed EEG signal calculation method supporting data compression and encryption according to claim 1, wherein the receiving and filtering program reconstructs the compressed data into the original EEG signal according to the decompression algorithm. 6. The distributed EEG signal calculation method supporting data compression and encryption according to claim 1, wherein the receiving and filtering program adopts commonly used filtering algorithms to perform EEG signal preprocessing, including low-pass filtering The filter or notch filter can eliminate the power frequency interference in the EEG signal, and the result is a more realistic and accurate EEG signal. 7. The distributed EEG signal calculation method supporting data compression and encryption according to claim 1, wherein the receiving and filtering program encrypts the preprocessed EEG signal using a symmetric cryptographic algorithm, and the encrypted The key is negotiated between the sender and the receiver. 8. The distributed EEG signal calculation method supporting data compression and encryption according to claim 1, wherein the feature extraction and classification program adopts a symmetric cryptographic algorithm after receiving the encrypted data transmitted from the network. The same key is used for decryption to obtain EEG signals without interference. 9. the distributed EEG signal computing method that supports data compression and encryption according to claim 1, is characterized in that, described feature extraction and classification program carry out feature extraction to EEG signal from time domain and frequency domain, based on the The extracted features classify EEG signals and identify the real intention of the brain. 10. The distributed EEG signal calculation method supporting data compression and encryption according to claim 1, characterized in that, the feature extraction and classification program uses a symmetric cryptographic algorithm to encrypt the classification results, and the encrypted key is sent by generated through negotiation between the end and the receiving end. 11. The distributed EEG signal calculation method supporting data compression and encryption according to claim 1, wherein the external control program adopts the same encryption method according to the symmetric encryption algorithm after receiving the encrypted data transmitted. Key to decrypt, get = classification result. 12. The distributed EEG signal calculation method supporting data compression and encryption according to claim 1, wherein the external control program converts the EEG signal classification results into control instructions for external devices, or spelling target character, etc., and then call the programming interface of the external device to complete the external control or intention presentation of the real intention of the brain.

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