Experimental design, data acquisition and data processing integrated electroencephalogram measurement method

Abstract

The invention discloses an electroencephalogram measurement method integrating experimental design, data acquisition and data processing, and belongs to the technical field of electroencephalograph, and the method comprises the following steps: S1, designing an electroencephalogram experiment: selecting a preset experimental process or a user-defined experimental process; s2, data acquisition: performing electroencephalogram acquisition according to the selected experimental process, and marking the acquired electroencephalogram data; s3, data processing: performing time synchronization on the marked labels, and analyzing the electroencephalogram data according to the labels. According to the electroencephalogram measurement method integrating experimental design, data acquisition and data processing, a user does not need to learn extra software, do not need to access extra hardware and do not need to input extra manpower, the threshold of electroencephalogram research is lowered, and the user experience is improved. Therefore, great convenience is provided for the majority of clinicians, initial or cross-field scientific researchers and other electroencephalogram research enthusiasts, many unnecessary investments are avoided, and the method has wide application prospects.

Description

Technical Field

[0001] The present invention belongs to the field of electroencephalogram technology, and specifically relates to an electroencephalogram measurement method integrating experimental design, data acquisition and data processing. Background Art

[0002] To complete an EEG study, three links are required: experimental design, data collection, and data processing. Experimental design refers to designing task-state or resting-state experiments, data collection refers to collecting EEG data from subjects in the experiment, and data processing refers to processing and analyzing the collected EEG data. Currently, almost all EEG acquisition systems on the market can only realize the acquisition function when working independently, and the experimental design link often needs to rely on other software to implement, which leads to two problems:

[0003] (1) Users with limited time and energy can only complete the collection of resting state data and give up the collection and analysis of task state data, resulting in limited research dimensions of the problem;

[0004] (2) Users need to relearn the experimental design software to complete the collection of task-state data, which increases the user's learning cost; or, users need to seek cooperation from other people who are familiar with external software, which increases other costs, such as communication costs, cooperation fees, etc., and also increases the risk of data leakage. In addition, although the task-state experimental design is completed and the corresponding data is collected through external software, users still need to rely on other software to achieve data segmentation, such as extracting task-state data and superimposing and averaging. Therefore, similar to the experimental design link, users who do not have professional capabilities need to relearn or rely on the cooperation of other people, which also increases additional costs. Based on this, the development of an EEG measurement system that integrates experimental design, data acquisition and data processing has broad application prospects.

[0005] Task-based experimental design and task-based data processing are highly dependent on the labeling function of the EEG measurement system. Generally speaking, when a task starts, the system needs to mark the data with the start of the task so that data can be segmented and classified during subsequent data processing. Currently, most EEG measurement systems on the market need to rely on additional triggers to mark task-based experiments. Although this ensures time synchronization, it also increases the debugging cost of the system and reduces the system's fault tolerance. Therefore, it is more practical to develop an EEG measurement system based on system time synchronization and without external triggers.

[0006] In terms of labeling, in addition to relying on software for automatic labeling, users usually need to customize the label content according to the actual situation of the subject, especially in clinical environments. Currently, the custom labeling function of most software often requires users to first type in the specific event content and then mark it by pressing a key, which causes the labeling time to lag behind the actual event time, which interferes with the subsequent analysis. Therefore, it is very important to develop a faster and more accurate custom labeling function for the EEG acquisition system. Summary of the invention

[0007] In view of the above-mentioned deficiencies in the prior art, the EEG measurement method integrating experimental design, data acquisition and data processing provided by the present invention solves the following problems existing in the prior art:

[0008] (1) Currently, almost all EEG acquisition systems on the market can only realize the acquisition function when working independently. A few platforms also have analysis functions, and the experimental design link often needs to rely on other software to implement, which affects the user experience.

[0009] (2) Currently, most EEG measurement systems on the market rely on additional triggers to mark task-state experiments. Although this ensures time synchronization, it also increases the system debugging cost and reduces the system's fault tolerance.

[0010] (3) Currently, the custom labeling function of most software often requires users to first type in the specific event content and then mark it by pressing a key. This causes the labeling time to lag behind the actual event time, which interferes with the subsequent analysis process.

[0011] In order to achieve the above-mentioned purpose of the invention, the technical solution adopted by the present invention is: an EEG measurement method integrating experimental design, data acquisition and data processing, comprising the following steps:

[0012] S1. Design EEG experiment: select preset experimental process or customize experimental process;

[0013] S2. Data collection: Collect EEG data according to the selected experimental process and mark the collected EEG data;

[0014] S3. Data processing: Time synchronization of the labeled tags and analysis of EEG data based on the tags.

[0015] Further: In S1, the experimental process is provided with a block experiment of evoked potential, and the process method of the block experiment of evoked potential is specifically as follows:

[0016] Through the information of pictures, videos, audio or text provided by users, the users are guided to induce corresponding EEG activities according to the information, and the duration of each block corresponding to the information is set. The blocks include task blocks and rest blocks, and labels are set for the event nodes in each block.

[0017] Further: in said S2, the method of marking the collected EEG includes automatic marking and free marking;

[0018] Among them, the automatic labeling method specifically labels the EEG data according to the user-preset labels in each block of the experimental process;

[0019] The free labeling method is to label at any time in the experimental process through user voice, and convert the voice information into labeled event information.

[0020] Further: In S4, the method for time synchronization of the marked tags is specifically:

[0021] Tag-data synchronization is performed based on the system time of each tag and the system time when EEG data collection starts.

[0022] Further: In S4, the method for analyzing EEG data according to the label includes:

[0023] (1) Filter, notch, segment, and average the EEG data according to the labels;

[0024] (2) Calculate multi-dimensional indicators of EEG data based on the labels, including time, frequency, and space.

[0025] The beneficial effects of the present invention are as follows: the present invention provides an EEG measurement method integrating experimental design, data acquisition and data processing, which has the following effects compared with the prior art:

[0026] (1) Provide users with a plan for designing EEG experiments, so that users can complete EEG experiment design without relying on external software, thereby improving user experience.

[0027] (2) By utilizing system time synchronization technology, accurate data labeling of key events in the experimental design can be performed without the need for external triggers, thereby improving the practicality of EEG measurements.

[0028] (3) A marking solution that combines automatic marking and free marking is adopted. Users can mark any moment in the experiment through voice input. The voice content is recorded by the wireless marker and converted into detailed event information after AI voice recognition, thus achieving fast and accurate marking.

[0029] In summary, the EEG measurement method of the present invention integrates experimental design, data acquisition and data processing. Users do not need to learn additional software, connect to additional hardware, or invest additional manpower, which lowers the threshold for EEG research. This provides great convenience for clinicians, novice or cross-field scientific researchers, and other EEG research enthusiasts, avoids many unnecessary investments, and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 The present invention is a flow chart of the EEG measurement method integrating experimental design, data acquisition and data processing.

[0031] Figure 2 It is a functional schematic diagram of the method of the present invention. DETAILED DESCRIPTION

[0032] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0033] like Figure 1 As shown, in one embodiment of the present invention, the EEG measurement method integrating experimental design, data acquisition and data processing comprises the following steps:

[0034] S1. Design EEG experiment: select preset experimental process or customize experimental process;

[0035] S2. Data collection: Collect EEG data according to the selected experimental process and mark the collected EEG data;

[0036] S3. Data processing: Time synchronization of the labeled tags and analysis of EEG data based on the tags.

[0037] In S1, the experimental process is provided with a block experiment of evoked potentials, and the process method of the block experiment of evoked potentials is specifically as follows:

[0038] Through the information of pictures, videos, audio or text provided by users, the users are guided to induce corresponding EEG activities according to the information, and the duration of each block corresponding to the information is set. The blocks include task blocks and rest blocks, and labels are set for the event nodes in each block.

[0039] like Figure 2As shown, in this embodiment, the selected process is the block experiment of evoked potential. The user provides a specific picture, video, audio or edited text such as "imagine left hand movement" in the task block to guide the user to induce specific EEG activity and design labels for specific event nodes, among which, task 1 in the task block corresponds to label 1.

[0040] In said S2, the method of marking the collected EEG includes automatic marking and free marking;

[0041] Among them, the automatic labeling method specifically labels the EEG data according to the user-preset labels in each block of the experimental process;

[0042] The free labeling method is to label at any time in the experimental process through user voice, and convert the voice information into labeled event information.

[0043] like Figure 2 As shown, in this embodiment, for the automatic labeling method, the EEG data is labeled according to the labels preset by the user. For example, the EEG data is labeled with label 1 according to task 1 in the task block, and the EEG data is labeled with label 2 according to task 2 in the task block. These labeled labels are synchronously recorded in the data file in the form of system time.

[0044] For the free marking method, the user describes the event content through voice input, which is converted into specific text information by AI voice recognition and recorded in the collection file in a table together with the marking time.

[0045] In S4, the method for performing time synchronization on the marked tags is specifically as follows:

[0046] Tag-data synchronization is performed based on the system time of each tag and the system time when EEG data collection starts.

[0047] In S4, the method for analyzing EEG data according to the label includes:

[0048] (1) Filter, notch, segment, and average the EEG data according to the labels;

[0049] (2) Calculate multi-dimensional indicators of EEG data based on the labels, including time, frequency, and space.

[0050] In this embodiment, the EEG data is labeled to calculate indicators of various dimensions, which facilitates the user to perform subsequent statistics and analysis.

[0051] The beneficial effects of the present invention are as follows: the present invention provides an EEG measurement method integrating experimental design, data acquisition and data processing, which has the following effects compared with the prior art:

[0052] (1) Provide users with a plan for designing EEG experiments, so that users can complete EEG experiment design without relying on external software, thereby improving user experience.

[0053] (2) By utilizing system time synchronization technology, accurate data labeling of key events in the experimental design can be performed without the need for external triggers, thereby improving the practicality of EEG measurements.

[0054] (3) A marking solution that combines automatic marking and free marking is adopted. Users can mark any moment in the experiment through voice input. The voice content is recorded by the wireless marker and converted into detailed event information after AI voice recognition, thus achieving fast and accurate marking.

[0055] In summary, the EEG measurement method of the present invention integrates experimental design, data acquisition and data processing. Users do not need to learn additional software, connect to additional hardware, or invest additional manpower, which lowers the threshold for EEG research. This provides great convenience for clinicians, novice or cross-field scientific researchers, and other EEG research enthusiasts, avoids many unnecessary investments, and has broad application prospects.

[0056] In the description of the present invention, it is necessary to understand that the orientation or positional relationship indicated by the terms "center", "thickness", "upper", "lower", "horizontal", "top", "bottom", "inner", "outer", "radial", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "first", "second", and "third" are used only for descriptive purposes, and cannot be understood as indicating or implying the relative importance or the number of implicitly specified technical features. Therefore, the features defined by "first", "second", and "third" may explicitly or implicitly include one or more of the features.

Claims

1. An EEG measurement method integrating experimental design, data acquisition and data processing, characterized in that: The following steps are involved: S1. Design EEG experiment: select preset experimental process or customize experimental process; S2. Data collection: Collect EEG data according to the selected experimental process and mark the collected EEG data; S3. Data processing: Time synchronization of the labeled tags and analysis of EEG data based on the tags.

2. The EEG measurement method integrating experimental design, data acquisition and data processing according to claim 1 is characterized in that: In S1, the experimental process is provided with a block experiment of evoked potentials, and the process method of the block experiment of evoked potentials is specifically as follows: Through the information of pictures, videos, audio or text provided by users, the users are guided to induce corresponding EEG activities according to the information, and the duration of each block corresponding to the information is set. The blocks include task blocks and rest blocks, and labels are set for the event nodes in each block.

3. The EEG measurement method integrating experimental design, data acquisition and data processing according to claim 2 is characterized in that: In S2, the method of marking the collected EEG includes automatic marking and free marking; Among them, the automatic labeling method specifically labels the EEG data according to the user-preset labels in each block of the experimental process; The free labeling method is to label at any time in the experimental process through user voice, and convert the voice information into labeled event information.

4. The EEG measurement method integrating experimental design, data acquisition and data processing according to claim 1 is characterized in that: In S4, the method for performing time synchronization on the marked tags is specifically as follows: Tag-data synchronization is performed based on the system time of each tag and the system time when EEG data collection starts.

5. The EEG measurement method integrating experimental design, data acquisition and data processing according to claim 1 is characterized in that: In S4, the method for analyzing EEG data according to the label includes: (1) Filter, notch, segment, and average the EEG data according to the labels; (2) Calculate multi-dimensional indicators of EEG data based on the labels, including time, frequency, and space.

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