System and method for data phase dependence analysis

Abstract

The invention relates to the technical field of data analysis software, in particular to a system and a method for data phase dependence analysis. A system for data phase dependence analysis comprises a view module, a trigger module, a filtering module, an interval data analysis module and a phase dependence averaging module and is suitable for rhythm signal analysis. And the advanced signal analysis module is used for processing large-scale and multi-channel data. A method for data phase dependence analysis is suitable for the system and comprises the following steps: S1, software initialization and environment setting; s2, data acquisition and real-time monitoring are carried out; s3, signal filtering setting; s4, setting a trigger event; s5, analyzing interval data; s6, carrying out phase dependence averaging; s7, performing advanced signal analysis; and S8, data backup and export. According to the data analysis system provided by the invention, by integrating the functions of multi-channel data synchronous processing, flexible signal filtering and trigger point management, phase dependence analysis and the like, complex experiment requirements can be met.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis software, and particularly to a system and method for data phase-dependent analysis. Background Art

[0002] In fields such as neurobiology, it is generally necessary to study behavioral responses, repetitive firing events, neurophysiological signals, etc. in the laboratory through software, which involves tasks such as signal processing and data analysis.

[0003] Many existing data analysis software is usually tightly bound to specific data acquisition hardware, which limits their applicability. That is, existing software requires users to use specific hardware devices for data acquisition, which may cause the software to be unusable in the case of hardware incompatibility. On the other hand, many existing data analysis software either focuses on online data analysis or can only perform offline analysis. Therefore, the above limitations also result in the problem that in the process of data processing and analysis of existing software, the filtering and triggering functions are relatively fixed or limited and cannot meet the requirements under complex experimental conditions.

[0004] In the prior art, for example, in sensory physiology, the design of experimental conditions can cause significant changes in the electrophysiological data sequence due to the spontaneous behavior of animals. In this case, in one recording, only a specific corresponding data part where the animal is in the same behavioral state can be selected for evaluation. Therefore, for sensory information acquisition analysis and movement pattern generation analysis in the same laboratory, highly general computer software is required. This software should be suitable for both stereotyped experimental recording conditions and variable experimental recording conditions. Modern microelectronic technology provides computers with fast central processing units, large storage capacities, and high-sampling-rate AND boards, which are used in most physiological laboratories. With these systems, the sampling conditions can provide sufficient resolution of neurophysiological data in amplitude and time domain. Therefore, different computer programs have been developed to process neurophysiological data. However, existing computer programs are tailored according to the needs of specific experiments for certain A / D boards, computer systems, and physical problems and do not have wide applicability. Although there has also emerged software with more than single applications in the prior art, such as comprehensive programs for analyzing neurophysiological and behavioral data, generally it only provides at most 19 filters or arithmetic units for data processing and can process sampling data of up to 16 channels, and its application fields and processing functions are still relatively limited. Summary of the Invention

[0005] To overcome the deficiencies of the prior art, the present invention provides a system for data phase-dependent analysis applicable to functions such as real-time signal processing, data visualization, trigger point calculation, signal filtering, and interval data evaluation. It can process data from up to 32 data channels, can flexibly analyze various rhythmic signals and trigger event-dependent signals, and can be applied to various experiments such as neurobiology and ethology. To solve the above technical problems, the present invention provides the following technical solutions: A system for data phase-dependent analysis, comprising the following modules that cooperate with each other: A view module, configured to display trigger points in real time for accurately calibrating the occurrence time of events; A trigger module, closely integrated with the view module, configured to support complex trigger point calculations and provide accurate time synchronization functions to ensure the timing consistency of multi-channel data; A filtering module, configured to provide different filter options to support complex signal processing and provide real-time feedback for dynamically adjusting experimental schemes and data processing; An interval data analysis module, configured to provide efficient interval data analysis for the timing analysis of multi-signal systems; A phase-dependent averaging module, configured to be applicable to rhythmic signal analysis to reveal the neural response patterns in periodic behaviors; An advanced signal analysis module, configured to process large-scale and multi-channel data and perform real-time data processing to ensure the immediacy of experimental adjustment and data analysis.

[0006] This system is widely applied in fields such as neurobiology, ethology, kinematics, and environmental science, and is particularly applicable to the research of behavioral responses, repetitive trigger events, neurophysiological signals, or neural network models, etc. It is especially applicable to tasks such as signal processing, data analysis, and pattern recognition in laboratories.

[0007] On the basis of adopting the above technical solutions, the present invention can also adopt the following further technical solutions: The view module at least includes the following functional units: A data visualization functional unit, used to support the visualization of at least one or more types of data among raw data, filtered data, and trigger point markers, and display the analysis results in real time; A custom view functional unit, used to customize the displayed data content and support the overlay display of multiple types of data; A real-time update functional unit, used to provide visual feedback for real-time signal processing to immediately adjust the experimental scheme; A custom marking functional unit, used to mark points by itself to display the various values of the points, and the values at least include time and amplitude.

[0008] The trigger module at least includes the following functional units: The multi-channel data triggering functional unit is used to customize the triggering event marking for the data in each channel under different conditions; The trigger point calculation functional unit is used for the calculation based on the trigger conditions, and the trigger conditions can be customized and at least include threshold, maximum value, and minimum value; The trigger point management functional unit is used to save the trigger points as an editable list for insertion, deletion, or modification; The event synchronization functional unit is used for the event synchronization of multiple signal channels to ensure the consistency of events in all channels.

[0009] The filtering module at least includes the following functional units: The signal filtering functional unit is used to provide at least 20 types of filters and support the multi-step filtering superposition operation, and the filtering options at least include low-pass, high-pass, band-pass, and band-stop; The real-time filtering functional unit is used to view the filtered data in real time in the view module to quickly evaluate the filtering effect.

[0010] The interval data analysis module at least includes the following functional units: The interval data evaluation functional unit is used to analyze the signals within the interval according to the trigger point calculation parameters, and the parameters at least include time interval, peak value, and phase, and the analysis at least includes statistics and histogram generation; The cross-channel data analysis functional unit is used to cross-evaluate the interval data of multiple signal channels to deeply analyze the time relationship between different signals.

[0011] The phase-dependent averaging module at least includes the following functional units: The phase-dependent averaging functional unit for rhythmic signals is used to perform the phase-dependent averaging analysis of signals according to the period of rhythmic behavior to reveal the periodic pattern of neural activity; The averaging functional unit dependent on trigger events is used to average the different data corresponding to each trigger event according to the generation of stimulus events; The noise suppression functional unit is used to reduce signal noise through the averaging function to make the pattern of rhythmic neural activity clearer.

[0012] The advanced signal analysis functional module at least includes the following functional units: The multi-channel synchronous analysis functional unit is used for the synchronous analysis of up to 32 data channels to suit complex multi-signal experiments; The real-time signal processing functional unit is used to process real-time data, and the data can be analyzed while being collected to ensure timely feedback.

[0013] This system specially designs an intuitive and user-friendly user interface (UI) on the Windows operating system platform so that users can easily manage the data acquisition, signal processing, and analysis processes. Its main design features include: modern and intuitive interface design, customizable workspaces, simple navigation, and operation processes. At the same time, it provides multi-language support and advanced graphics display and interaction.

[0014] Furthermore, the present invention also provides the following technical solutions: A method for data phase-dependent analysis, applicable to the system as described above, includes the following steps: S1. Software initialization and environment setting, start the system software and perform basic parameter settings and channel configuration; S2. Data acquisition and real-time monitoring, start collecting data, open the view module and select the data types to be displayed, and can choose whether to mark specific events; S3. Signal filtering setting, select the filter type in the filtering module and set the filtering parameters, and adjust the parameters immediately according to the view feedback; S4. Set trigger events, set the trigger conditions in the trigger module, save the trigger points, and generate a trigger list; S5. Interval data analysis, select the analysis interval in the interval data analysis module and analyze the signal parameters, and multiple channels can be selected for comparison analysis; S6. Phase-dependent averaging, select the period of the rhythmic signal in the phase-dependent averaging module and set the phase parameters for average calculation; S7. Advanced signal analysis, select multiple channels for synchronous analysis in the advanced signal analysis module, or select real-time analysis with data acquisition and processing synchronized; S8. Data backup and export, automatically save the collected raw data, or export to generate experimental reports or charts.

[0015] The parameters in step S1 at least include the sampling rate, the number of channels, and the data storage path. The channel configuration includes selecting the channels to be collected and configuring the parameters for each channel. The parameters of the channels at least include gain and filters.

[0016] The data types in step S2 at least include raw data, filtered data, and trigger point data.

[0017] The filter types in step S3 at least include low-pass filters, high-pass filters, band-pass filters, and band-stop filters. The filtering parameters at least include the cut-off frequency and the filtering order.

[0018] The signal parameters in step S5 at least include the time interval, the peak value, and the phase.

[0019] Compared with the prior art, the beneficial effects that the present invention can achieve are as follows: The data analysis system provided by the present invention can meet the requirements of complex experiments by integrating functions such as multi-channel data synchronous processing, flexible signal filtering and trigger point management, and phase-dependent analysis. At the same time, its optimized Windows operating system user interface design improves the usability and user experience of the software, enabling researchers to perform data analysis more efficiently and conveniently, and enhancing the accuracy and efficiency of research. The specific beneficial effects are as follows: 1. Seamless compatibility with multiple data sources: The system of the present invention does not depend on specific acquisition hardware or platforms and supports processing data from different data acquisition systems. Users can use a variety of commercial acquisition software for data acquisition and then perform subsequent analysis through this software. This cross-platform and cross-system compatibility is a major innovative advantage of the system of the present invention. The system of the present invention can support data acquired through commercially available programs, which usually support various A / D boards, thus allowing different laboratories to use different hardware platforms. Different from other programs, the system of the present invention does not store any filtered data on the hard disk, avoiding waste of data storage space and making the processing speed more efficient.

[0020] 2. Flexible triggering and filtering mechanisms: The system of the present invention provides flexible triggering algorithms and highly customizable filters, which can precisely define data screening and processing rules according to specific experimental conditions or requirements, enabling users to perform precise customized analysis on the original signals.

[0021] 3. Principle of lossless data processing: The system of the present invention adopts the design principle of not storing filtered data. Different from many other software that needs to store filtered and processed data files, the system of the present invention avoids multiple replications and storages of data during the analysis process, thus maintaining the integrity of the original data, avoiding data redundancy and loss, and is particularly suitable for the analysis of large-scale data sets.

[0022] 4. Phase and time-dependent signal analysis: The system of the present invention provides an average analysis method based on phase and time specifically for rhythmic signal analysis, which is relatively rare in many existing software. Many analysis tools only support conventional time-domain or frequency-domain analysis, while the system of the present invention can deeply analyze signals in multiple dimensions (time, phase, etc.), and is particularly suitable for neurobiological and behavioral experiments studying rhythmic and periodic behaviors.

[0023] 5. Object - Oriented Programming Architecture and Modular Design: The system of the present invention adopts object - oriented programming methods, featuring high scalability and modular design. Users can conveniently add new filters and data analysis modules according to their research needs. This flexible architecture enables the system of the present invention to rapidly iterate and expand its functions along with technological progress and changes in research requirements, far superior to many software developed based on traditional methods.

[0024] 6. Multi - channel Data Processing Capability: Compared with some existing data analysis software, the system of the present invention can efficiently process real - time data from up to 32 data channels and support synchronous analysis. This enables it to perform excellently in large - scale experiments requiring multi - channel synchronous analysis and is suitable for complex fields such as neuroscience, ethology, and environmental monitoring.

[0025] 7. Cross - field Application Potential: The powerful functions of the system of the present invention are not limited to the field of neurobiology. Its data analysis capabilities and flexibility endow it with great application potential in multiple fields such as biomedicine, physics, engineering, and environmental monitoring. Especially in aspects such as rhythmic and periodic signal analysis, repetitive trigger event analysis, and synchronous signal processing, it can meet a wide range of scientific research and industrial needs.

[0026] 8. Parallel Support for Real - time and Offline Analysis: The system of the present invention provides flexible real - time and offline data analysis options and can switch between different analysis modes according to users' experimental needs, making it flexible in various experimental scenarios.

[0027] 9. Do not Depend on Specific Hardware Devices: The system of the present invention solves this problem by supporting different A / D boards and acquisition systems, enabling it to work in a variety of hardware environments and greatly expanding its scope of application.

[0028] 10. Advanced Signal Averaging and Data Processing: The system of the present invention provides more refined time - dependent and phase - dependent signal averaging techniques. Especially when processing rhythmic neural signals and behavioral signals, it can effectively analyze periodic fluctuations, amplitude changes, and the mutual relationships between signals. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a logic block diagram of a system and method for data phase - dependent analysis of the present invention.

[0030] Figure 2 It is an analysis flow chart of a system and method for data phase - dependent analysis of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0031] In combination with the accompanying drawings, a system and method for data phase - dependent analysis provided by the present invention will be further described.

[0032] As Figure 1-2 shown, a system for data phase-dependent analysis includes the following six modules: 1. View module, which is tightly integrated with the trigger module, can display trigger points in real time, facilitating users to accurately calibrate the occurrence time of events. It supports synchronous display of multi-channel data and is suitable for processing large-scale experimental data.

[0033] The view module includes the following functional units: Data visualization functional unit: Supports various types of data visualization, including raw data, filtered data, trigger point markers, etc., and can display analysis results in real time.

[0034] Custom view functional unit: Allows users to customize the displayed data content according to their needs, supports overlay display of multiple types of data, and can display / hide different channels and data.

[0035] Real-time update functional unit: Provides visual feedback for real-time signal processing to help users immediately adjust the experimental plan.

[0036] Custom marking functional unit: Users can mark points themselves to display various values at that point (such as time, amplitude, etc.).

[0037] 2. Trigger module, which supports complex trigger point calculations, can meet the requirements of various experimental designs, provides accurate time synchronization functions, and ensures the timing consistency of multi-channel data.

[0038] The trigger module includes the following functional units: Multi-channel data trigger functional unit: Users can customize trigger event markers for data in each channel under different conditions.

[0039] Flexible trigger point calculation functional unit: Supports calculations based on trigger conditions such as thresholds, maximum values, minimum values, etc., and users can customize trigger conditions.

[0040] Trigger point management functional unit: Trigger points are saved as editable lists, and users can perform insert, delete, or modification operations on them.

[0041] Event synchronization functional unit: Supports time synchronization of multi-signal channels to ensure the consistency of events in all channels.

[0042] 3. Filter module, which provides a rich selection of filters to meet the needs of different experiments, supports complex signal processing, and can provide real-time feedback to help users dynamically adjust the experimental plan and data processing.

[0043] The filter module includes the following functional units: Signal filtering function unit: It provides no less than 20 common filters, including various filtering options such as low-pass, high-pass, band-pass, and band-stop. It supports multi-step filtering superposition operation and can add new filters according to needs later.

[0044] Real-time filtering function unit: Users can view the filtered data in the view module in real time and quickly evaluate the filtering effect.

[0045] 4. Interval data analysis module. This module provides efficient interval data analysis, which is applicable to the time series analysis of multi-signal systems. It provides a variety of statistical analysis methods to help users deeply understand the neural mechanism behind the data.

[0046] The interval data analysis module includes the following function units: Interval data evaluation function unit: It calculates parameters such as time interval, peak value, and phase according to the trigger point, and supports the analysis of signals within the interval, such as statistics and histogram generation.

[0047] Cross-channel data analysis function unit: It supports the cross-evaluation of interval data of multiple signal channels to help users deeply analyze the time relationship between different signals.

[0048] 5. Phase-dependent averaging module. This module is applicable to the analysis of rhythmic signals, helps to reveal the neural response pattern in periodic behavior, is applicable to the signal analysis of repetitive events, improves the accuracy of signal analysis, and is especially applicable to low-signal-noise data.

[0049] The phase-dependent averaging module includes the following function units: Phase-dependent averaging function unit of rhythmic signals: According to the period of rhythmic behavior, it performs phase-dependent averaging analysis of signals to reveal the periodic pattern of neural activity.

[0050] Average function unit dependent on trigger events: According to the generation of stimulus events, it averages different data corresponding to each trigger event.

[0051] Noise suppression function unit: It reduces signal noise through the averaging function, making the pattern of rhythmic neural activity clearer.

[0052] 6. Advanced signal analysis function module. This module processes large-scale, multi-channel data and provides support for complex experiments. It performs real-time data processing to ensure the immediacy of experimental adjustment and data analysis.

[0053] The advanced signal analysis function module includes the following function units: Multi-channel synchronous analysis function unit: It supports the synchronous analysis of up to 32 data channels, which is suitable for complex multi-signal experiments.

[0054] Real-time signal processing functional unit: Supports processing of real-time data. The data can be analyzed while being collected to ensure timely feedback.

[0055] The operating method and process of the above system software are as follows: Among them, as Figure 1 shown, the executable shortcut keys in the main menu of this system software and their corresponding functions are as follows: F: File manager; T: Trigger manager; C: Calibration; M: Filter manager; L: List sampled data; A: Analog data; V: Display data; - / +: Sound switch; S: Save configuration file; E: Exit program.

[0056] I. Software initialization and environment settings.

[0057] 1. Start the software: Open the program software and ensure that the computer meets the hardware requirements for software operation (CPU, memory, etc.).

[0058] 2. Basic parameter settings: Enter the "Settings" menu and configure the following parameters: Sampling rate: Set the sampling rate according to the experimental requirements (e.g., 1000 Hz). Number of channels: Set according to the number of channels to be collected (e.g., single channel or multi-channel). Data storage path: Specify the data saving location to ensure sufficient storage space.

[0059] 3. Channel configuration: Enter "Channel Settings", select the channels to be collected, and configure the parameters of each channel (such as gain, filter).

[0060] II. Data collection and real-time monitoring.

[0061] 1. Start data collection: Select the "Data Collection" menu and click "Start Collection". The program software will automatically generate a raw data file and display the real-time waveform of the data on the interface.

[0062] 2. Data real-time visualization: Open the "View" module and select the data type to be displayed (raw data, filtered data, trigger point data). Set the display mode in the view module (such as overlaying and displaying data from different channels) to help observe the signal quality in real time. Different channels can be displayed / hidden in real time during collection to facilitate observing specific signals.

[0063] 3. Data point marking: If you need to mark specific events, click the "Mark" tool to add time marks to the data for subsequent analysis.

[0064] III. Signal filtering settings.

[0065] 1. Select Filter: Enter the "Filter" module and select the filter type, such as: Low-pass filter: Filters out high-frequency noise. High-pass filter: Filters out low-frequency interference. Band-pass filter: Retains signals within a specific frequency range. Band-stop filter: Removes interference at specific frequencies.

[0066] 2. Set Filter Parameters: Configure parameters in the filter settings, such as cut-off frequency and filter order. After applying the filter, return to the "View" module to view the filtering effect.

[0067] 3. Real-time Adjustment: If the filtering effect is not good, the parameters can be adjusted immediately according to the view feedback to ensure the signal processing quality.

[0068] IV. Set Trigger Events.

[0069] 1. Define Trigger Conditions: Set trigger conditions in the "Trigger" module. Select the channel and trigger type, for example: Threshold Trigger: Set the voltage or frequency threshold. Maximum Trigger: Trigger when the signal reaches the maximum value.

[0070] 2. Trigger Point Management: The program software will automatically save the trigger points and generate a trigger list. View or edit the trigger point list in the trigger module, and trigger points can be added or deleted manually.

[0071] 3. Multi-channel Synchronization: In multi-channel experiments, select the "Synchronous Trigger" function to ensure that the trigger points of all channels are consistent.

[0072] V. Interval Data Analysis.

[0073] 1. Select Analysis Interval: Enter the "Interval Data Analysis" module and select specific trigger points or time periods.

[0074] 2. Analyze Signal Parameters: Use the interval analysis function to select parameters for analysis: Time Interval: Calculate the time difference between trigger points. Peak Value: Calculate the peak value of each interval. Phase: Analyze the phase distribution of periodic signals.

[0075] 3. Cross-channel Data Analysis: If comparative analysis of multiple channels is required, select the "Cross-channel" function to analyze the timing relationship between different channels.

[0076] 4. Data Visualization and Statistics: Generate histograms or statistical charts to display data characteristics and distributions, helping users intuitively understand the analysis results.

[0077] VI. Phase-dependent Averaging.

[0078] 1. Set Phase-dependent Averaging Parameters: Enter the "Phase-dependent Averaging" module, select the period of the rhythmic signal, and set the phase parameters for the average calculation.

[0079] 2. Event-triggered averaging: For a specific event (such as a stimulation event), the signal is averaged based on the event time points to analyze the repetitive patterns of the signal.

[0080] 3. Noise suppression: Phase averaging helps to remove noise, improve the stability and clarity of the signal, and is suitable for periodic data with a low signal-to-noise ratio.

[0081] VII. Advanced signal analysis.

[0082] 1. Multi-channel synchronous analysis: Enter the "Advanced Signal Analysis" module, select multiple channels for synchronous analysis, which is applicable to large-scale multi-signal experiments.

[0083] 2. Real-time signal processing: The program software supports real-time analysis during data acquisition. Select the real-time processing function in advanced signal analysis, and data acquisition and processing are carried out synchronously.

[0084] 3. Result export and data management: After the experiment is completed, select the export function to save the analysis results in different formats (such as CSV, Excel) for subsequent data management and report generation.

[0085] VIII. Data backup and export.

[0086] 1. Data saving: The program software automatically saves the collected raw data.

[0087] 2. Export analysis report: Select the "Export" menu to generate an experiment report or export charts, which is convenient for data reporting or further analysis.

[0088] The above methods and processes can be summarized as the following steps: S1. Open the original file S2. Click "View" to view the original data, filtered data, trigger point markers, etc., and the displayed data content can be customized according to needs, and the overlay display of multiple data is supported. Different channels and data can be viewed by clicking Show / Hide. The values of each point (such as time, amplitude, etc.) at the point can be marked manually by moving the cursor.

[0089] S3. Click "Filter" to filter the data of different channels. Under the filter menu, there are 19 common filter options, and multiple filter options can be selected simultaneously, and the overlay operation is supported. The filtered signal will be displayed in the "View" module in the form of a new channel to quickly evaluate the filtering effect.

[0090] S4. Click on "Trigger" to mark trigger events for data in each channel under different conditions. First, select the channel number you want to trigger, then select the trigger calculation method, such as threshold, maximum value, minimum value, etc. Then, by adding trigger conditions, such as global / local, maximum trigger count, trigger interval, trigger start, etc., a series of conditions are used to mark the trigger points of the data.

[0091] S5. Select a list of trigger points with saved and marked trigger points, such as list A. These trigger points can be viewed and marked in the view by clicking on "Show Trigger Points" and selecting the trigger list. When viewing the trigger points in the view, specific trigger points can be selected by mouse for insertion, deletion, or modification operations.

[0092] S6. By clicking on "Interval Data Analysis" and selecting different analysis modes, statistical analysis can be performed on the data. This includes post-stimulus time histogram, autocorrelation histogram, cross-correlation histogram, etc.

[0093] S7. Perform periodic average analysis of events by clicking on "Average Analysis". That is, at each trigger point, the corresponding signal intensity or pattern is averaged. For example, according to the period of rhythmic behavior, phase-dependent average analysis of the signal is performed to reveal the periodic pattern of neural activity. According to the generation of stimulus events, different data corresponding to each trigger event are averaged.

[0094] S8. In the "File" menu, click on Save to output any graphs or data under the "View" and "Analysis" modules.

[0095] Example: Explore the effect of a certain signal stimulus on the response of insect neurons. The same signal was used to stimulate the insects 50 times. The neuron response and the stimulus signal were recorded by the instrument and became two channels in the form of waves, namely channel 1 and channel 2.

[0096] For each stimulus, the neuron response will increase. First, filter and calculate the signal of neuron channel 1 through "Filtering" to reduce the noise signal and adjust the form of the data in this channel.

[0097] Subsequently, trigger point marking can be performed on the stimulation signal channel 2 within the trigger. By selecting channel 2, setting the trigger mode to rising, and the threshold to 50 mV, it is set that every time the signal in this channel rises above 50 mV, it will be marked once. In this way, the starting position of each stimulation signal can be marked. Next, the trigger point marking conditions can be set by setting the "condition" to global (i.e., marked in all data. If local is selected, the name of the previously manually selected marked block needs to be selected) and the trigger interval to 200 ms (the duration of the stimulation source is 200 ms). Finally, click "Trigger", and the software will display how many trigger points are found (50, the stimulation is performed 50 times). Click "Save Trigger Points" and select Trigger List A to save the found trigger points in Trigger List A this time.

[0098] Click "Average Analysis" and select "Waveform Averaging". In waveform averaging, set the average channels to "Channel 1" (nerve signal) and "Channel 2" (stimulation signal), the trigger point to "List A", "Display offset" to 50 ms (start displaying 50 ms before the trigger point), and "Display cutoff" to 500 ms (500 ms after the trigger point). Click "Process", and a new window will pop up, showing the content of two channels (1, 2), one is the average of 50 stimulation signals, and the other is the average of the nerve response after each stimulation. The display duration is from 50 ms before the stimulation signal to 500 ms after the stimulation signal. Subsequently, select save to save the graphical and data analysis results.

[0099] The present invention has been illustrated and described with reference to preferred embodiments. However, those of ordinary skill in the art should understand that various changes in form and details can be made within the scope of the claims.

Claims

1. A system for data phase dependency analysis, characterized in that Includes the following modules that work together: A view module is configured to display the trigger point in real time so as to accurately mark the time when the event occurs; The trigger module, which is tightly integrated with the view module, is configured to support complex trigger point calculations and provide precise time synchronization to ensure timing consistency of multi-channel data; The filtering module is configured to provide different filter options to support complex signal processing and provide real-time feedback to dynamically adjust the experimental plan and data processing; An interval data analysis module, configured to provide efficient interval data analysis suitable for timing analysis of a multi-signal system; Phase-dependent averaging module, configured for rhythmic signal analysis to reveal neural response patterns in periodic behaviors; Advanced signal analysis modules are configured to handle large-scale and multi-channel data, and perform real-time data processing to ensure instantaneous experiment adjustment and data analysis.

2. A system for data phase dependency analysis according to claim 1, characterized in that The view module includes at least the following functional units: A data visualization functional unit, used to support visualization of at least one or more types of data among raw data, filtered data, and trigger point marks, and to display analysis results in real time; Customized view function unit, used to customize the displayed data content and support the overlay display of multiple data; A real-time update function unit is used to provide visual feedback of real-time signal processing to adjust the experimental plan immediately; The custom marking function unit is used to mark the point by itself to display the various values ​​of the point, and the values ​​at least include time and amplitude.

3. A system for data phase dependency analysis according to claim 1, characterized in that The trigger module includes at least the following functional units: Multi-channel data triggering functional unit, used to customize the trigger event marking of different conditions for the data in each channel; A trigger point calculation functional unit, used for calculation based on trigger conditions, wherein the trigger conditions can be customized and include at least a threshold value, a maximum value, and a minimum value; A trigger point management functional unit, used to save the trigger points as an editable list so as to insert, delete or modify them; The event synchronization functional unit is used for event synchronization of multiple signal channels to ensure the consistency of events in all channels.

4. A system for data phase dependency analysis according to claim 1, characterized in that The filtering module at least includes the following functional units: The signal filtering function unit is used to provide at least 20 types of filters and support multi-step filtering superposition operations. The filtering options include at least low pass, high pass, band pass, and band stop; The real-time filtering function unit is used to view the filtered data in real time in the view module to quickly evaluate the filtering effect.

5. A system for data phase dependency analysis according to claim 1, characterized in that The interval data analysis module includes at least the following functional units: An interval data evaluation functional unit, used to calculate parameters according to the trigger point, and support the analysis of the signal in the interval, wherein the parameters at least include time interval, peak value, and phase, and the analysis at least includes statistics and histogram generation; The cross-channel data analysis functional unit is used to cross-evaluate the interval data of multiple signal channels to deeply analyze the time relationship between different signals.

6. A system for data phase dependency analysis according to claim 1, characterized in that The phase-dependent averaging module includes at least the following functional units: The phase-dependent average function unit of rhythmic signals is used to perform phase-dependent average analysis of signals according to the period of rhythmic behavior to reveal the periodic pattern of neural activity; The trigger event-dependent averaging functional unit is used to average different data corresponding to each trigger event according to the generation of the stimulus event; The noise suppression functional unit is used to reduce signal noise through averaging function, making the pattern of rhythmic neural activity clearer.

7. A system for data phase dependency analysis according to claim 1, characterized in that The advanced signal analysis function module includes at least the following functional units: Multi-channel synchronous analysis functional unit, used for synchronous analysis of up to 32 data channels, suitable for complex multi-signal experiments; The real-time signal processing functional unit is used to process real-time data. The data can be analyzed while being collected to ensure timely feedback.

8. A method for data phase dependency analysis, characterized in that A system according to any one of claims 1 to 7, comprising the following steps: S1. Software initialization and environment setting: start the system software and perform basic parameter setting and channel configuration; S2, data collection and real-time monitoring, start collecting data, open the view module and select the type of data to be displayed, and you can choose whether to mark specific events; S3, signal filtering settings, select the filter type and set the filter parameters in the filtering module, and adjust the parameters in real time according to the feedback; S4, set the trigger event, set the trigger condition in the trigger module, save the trigger point and generate a trigger list; S5, interval data analysis, in the interval data analysis module, select the analysis interval and analyze the signal parameters, and multiple channels can be selected for comparison and analysis; S6, phase-dependent averaging, selecting the period of the rhythmic signal and setting the phase parameter of the averaging calculation in the phase-dependent averaging module; S7, Advanced Signal Analysis, select multiple channels for simultaneous analysis in the Advanced Signal Analysis module, or select real-time analysis with simultaneous data acquisition and processing; S8. Data backup and export: automatically save the collected raw data, or export it to generate experimental reports or charts.

9. A method for data phase dependency analysis according to claim 8, characterized in that The parameters in step S1 include at least sampling rate, number of channels and data storage path, the channel configuration includes selecting the channel to be collected and configuring the parameters of each channel, the channel parameters include at least gain and filter; the data type in step S2 includes at least original data, filtered data and trigger point data; the filter type in step S3 includes at least low-pass filter, high-pass filter, band-pass filter and band-stop filter, and the filtering parameters include at least cut-off frequency and filtering order; the signal parameters in step S5 include at least time interval, peak value and phase.

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