CN111643075A - EEG real-time detection and analysis platform based on micro-state analysis method - Google Patents
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Abstract
The invention relates to an EEG real-time detection and analysis platform based on a micro-state analysis method, which comprises an EEG electrode array (1) for collecting scalp electroencephalogram, a lower computer (2) for collecting, preprocessing and analyzing the scalp electroencephalogram and a micro-state and an upper computer (3) for displaying an analysis result, wherein the lower computer (2) comprises an ADC module (4), a DSP operation chip (5) and a USB communication module (6); the EEG electrode array (1) and the ADC module (4) realize the collection of scalp brain electrical signals; an EEG preprocessing module (7) in the DSP operation chip (5) performs a preprocessing process on the original EEG signal to obtain a clean EEG signal (10) for analysis; the preprocessed EEG signals are input into a micro-state analysis module (11) and further analyzed and processed to obtain analysis results and specific characteristic parameters.
Description
Technical Field
The invention relates to a biomedical engineering technology, in particular to an EEG real-time detection and analysis platform based on a micro-state analysis method.
Background
Parkinson's disease is a nervous system disease characterized mainly by neurodegenerative diseases, and clinical symptoms usually manifest as severe dyskinesia such as resting tremor, muscular rigidity, bradykinesia, and postural imbalance of patients. The incidence of parkinson's disease is as high as 26% in people over the age of 60 and will further worsen with the course of the disease, eventually leading to the loss of self-care and even disability of the patient. Therefore, the efficient real-time auxiliary diagnostic tool has great significance for treating the Parkinson disease patient. The scalp electroencephalogram micro-state analysis method is an effective means for diagnosing nervous system diseases (such as Parkinson's disease, epilepsy and the like) at present. The presence of a particular micro-condition and its change in frequency, coverage or duration of occurrence can be considered a characteristic status marker for different neurological conditions as an effective diagnostic aid.
The electroencephalogram micro-state analysis method is provided based on electroencephalogram micro-states and is used for analyzing the dynamic change of a global mode of a scalp potential topological structure along with time. The scalp potential topological structure can describe the dynamic change of brain activity in a resting state, and the research range of EEG in the field of researching neuropsychiatric diseases is widened. The microstate analysis method can reflect the change condition of the functional network of the brain in the resting state, the microstate obtained by the analysis method can explain 90% of the dynamic change of the brain, and the mutual conversion among several microstate can reveal the dynamic change of the brain activity in the level of sub-second individual, and has the advantages of strong operability, strong representation individual specificity, strong robustness and the like.
At present, there is no monitoring device integrating EEG acquisition and analysis, and there is a time delay in the mechanism of analyzing neuropsychiatric diseases through EEG signals, i.e. the analysis result can only reflect the state of illness at the moment of acquiring EEG signals, and there is no direct connection with the current state of illness development. The current microstate analysis method for EEG signals, although it can significantly describe the brain activity characteristics of parkinson's disease, cannot be used as a practical tool for clinical diagnosis because it cannot provide clinically-referable real-time information for the auxiliary diagnosis of diseases. Therefore, a set of real-time detection and analysis platform capable of simultaneously controlling signal acquisition and signal analysis is established, so that the acquisition and analysis of EEG signals are synchronously carried out, and the condition of the Parkinson patients is reflected in the future development direction in a real-time, efficient and visual manner.
A Digital Signal Processor (DSP) is a microprocessor commonly used for Digital Signal processing operations, and has the advantages of high precision, good stability, high speed, high integration level, and the like, so that the DSP processor has significant advantages in the data processing and analyzing processes. The DSP processor is combined with the preprocessing process and the micro-state analysis method of the EEG signal, so that the EEG signal acquisition and high-speed data processing capability is realized, and the effect of efficiently detecting and analyzing the EEG information in real time is achieved.
Disclosure of Invention
Aiming at the problem of delay error and invalidity of the EEG signal acquisition and micro-state analysis method, the invention aims to design and develop an EEG real-time detection and analysis platform based on the micro-state analysis method, and integrally acquire, detect and analyze the scalp electroencephalogram signals of the Parkinson's disease patient, thereby realizing real-time monitoring of the patient's condition and obtaining visual disease characteristics at the current moment, and providing an important practical tool for clinical auxiliary diagnosis.
In order to achieve the above object, the present invention adopts a structure and a technical solution to provide an EEG signal acquisition circuit and a data analysis algorithm with strong applicability, and develop a real-time visualization platform with integration of data acquisition, disease monitoring and data analysis, wherein: the detection and analysis platform comprises an EEG electrode array 1 for collecting scalp electroencephalogram, a lower computer 2 for collecting, preprocessing and analyzing micro-state of scalp electroencephalogram and an upper computer 3 for displaying analysis results, wherein the lower computer 2 comprises an ADC module 4, a DSP operation chip 5 and a USB communication module 6. The EEG electrode array 1 and the ADC module 4 realize the collection of scalp brain electrical signals; an EEG preprocessing module 7 in the DSP operation chip 5 performs a preprocessing process on the original EEG signal so as to obtain a clean EEG signal 10 for analysis; inputting the preprocessed EEG signal into a micro-state analysis module 11 and further analyzing and processing the EEG signal to obtain an analysis result and specific characteristic parameters; the real-time human-computer interaction interface 18 of the upper computer 3 is realized by VB programming, and the data communication between the upper computer 3 and the DSP operation chip 5 is realized by the USB communication module 6.
The DSP operating chip 5 comprises an EEG preprocessing module 7 and a micro-state analysis module 11, wherein the EEG preprocessing module 7 consists of an ICA electromyography and eye electrical noise removing module 8 and a band-pass filtering module 9, and a clean EEG time sequence for analysis can be obtained. The micro-state analysis module 11 comprises a GFP calculation and preprocessing module 12, an improved K-means clustering algorithm module 13, a micro-state reverse coupling module 14 and a micro-state feature parameter extraction module 15. The modules are modules with algorithm realization functions, are realized by C + + compiling and downloaded to the DSP operation chip 9 to realize the data analysis and processing functions in the step.
The micro-state analysis module 11 is a core component of the platform for analyzing and processing the EEG signals, and has a function of analyzing the micro-state of the EEG signals acquired in real time. The GFP calculation and preprocessing module 12 calculates the global field power time series GFP as the spatial standard deviation of the electroencephalogram topographic map at each given time. At the local GFP maxima, the brain electrical has the greatest signal-to-noise ratio, the spatial configuration at that time is considered stable, and the topographic map at the GFP maxima has a similar topology to the topographic map between its adjacent GFP maxima. Therefore, the electroencephalographic maps at all maxima of GFP were obtained and used to calculate the overall microstate structure. The improved K-means clustering algorithm module 13 is used for clustering and calculating all topographic maps formed by signals at all GFP maximum points by using an improved K-means clustering algorithm to obtain individual micro-state classes. The micro-state back-coupling module 14 marks the electroencephalogram topographic map of the entire EEG signal using the obtained micro-state as a mark template, and marks the topographic map at all times as a specific one of the micro-state classes. The micro-state characteristic parameter extraction module 15 performs statistical calculation on the results of the above processes to obtain characteristic parameters such as average duration, occurrence frequency per second, total coverage rate and transition probability between micro-state classes of each micro-state class, so as to describe abnormal brain activity and stable state of illness.
The invention has the advantages of realizing the integrated operation of real-time acquisition, detection and analysis of EEG signals, designing a multifunctional human-computer interface with operability and visualization, improving the convenience and flexibility of the system, monitoring the state of a Parkinson patient in real time, providing an operation platform for systematically analyzing disease characteristics, providing an important analysis tool for the pathogenesis of the Parkinson disease, and having great practical value for clinical auxiliary diagnosis. Its main advantages include: 1. by using a micro-state analysis method, the dynamic change of brain activity can be analyzed by using EEG (electroencephalogram), and the functional state of the brain can be described in a time domain; 2. a DSP chip is used, the maximum working frequency is 200MHz, and the requirements of real-time detection, analysis and operation can be met; 3. the platform can realize the synchronous integrated operation of acquisition and analysis, avoid the condition that the analysis result is not matched with the current state of an illness of a patient, and realize real-time diagnosis and treatment.
Drawings
FIG. 1 is a schematic structural diagram of an EEG real-time detection and analysis platform system based on a micro-state analysis method according to the present invention;
FIG. 2 is an EEG signal pre-processing module of the present invention;
FIG. 3 is a block diagram of a data processing micro-status analysis module according to the present invention;
FIG. 4 is a human-computer interface of the present invention.
In the figure:
the EEG electrode array 2, the lower computer 3, the upper computer 4, the ADC module 5, the DSP operation chip 6, the USB communication module 7, the EEG preprocessing module 8, the ICA electromyographic and electrooculographic noise removing module 9, the band-pass filtering module 10, the preprocessed EEG time sequence 11, the micro-state analysis module 12, the GFP calculation and preprocessing module 13, the improved K-means clustering algorithm module 14, the micro-state reverse coupling module 15, the micro-state characteristic parameter extraction module 16, the USB data line 17, the USB data line 18, the human-computer interaction interface 19, the EEG signal and analysis result display interface 20, the operation option interface 21 and the acquisition device are provided with a selection interface
Detailed Description
The EEG real-time detection and analysis platform based on the micro-state analysis method is described with reference to the accompanying drawings.
The design idea of the EEG real-time detection and analysis platform based on the micro-state analysis method is that firstly, the human-computer interaction operation interface 18 in the upper computer 3 controls the ADC module 4 to collect EEG signals by utilizing the EEG electrode array 1, and the collected EEG electrophysiological signals are subjected to preprocessing steps of denoising, filtering and the like on original EEG electrophysiological signals by the EEG preprocessing module 7 in the DSP operation chip 5, so that an EEG time sequence 10 with noise interference removed and rhythm available for research and analysis is obtained; inputting the preprocessed EEG signal 10 into a micro-state analysis module 11 in the DSP operation chip 5 for micro-state analysis; the human-computer interaction operation interface 18 of the upper computer 3 receives the feature result of the analyzed data from the DSP operation chip 5 through the USB communication module 6, and displays the EEG electrophysiological signals, the microstate topographic map and the result of the feature parameters.
The EEG preprocessing module 7 is characterized in that: the EEG preprocessing module 7 consists of an ICA electromyography and eye electrical noise removing module 8 and a band-pass filtering module 9. An EEG time series with noise interference removed and with a range of rhythms consistent with the requirements of the data analysis can be obtained by the EEG pre-processing module 7.
The micro-state analysis module 11 comprises a GFP calculation and preprocessing module 12, an improved K-means clustering algorithm module 13, a micro-state reverse coupling module 14 and a micro-state feature parameter extraction module 15. The modules are modules with algorithm realization functions, are realized by C + + compiling and downloaded to the DSP operation chip 9 to realize the data analysis and processing functions in the step.
The data communication between the human-computer interaction interface 18 and the DSP operation chip 5 is realized through the USB communication module 6. The human-computer interaction interface 18 is provided with selectable operation options, the ADC module 4 and the DSP operation chip 5 are controlled by the upper computer 3 to realize the integrated operation of real-time acquisition and analysis of the EEG signals, and the data analysis result of the DSP operation chip 5 is displayed on the human-computer interaction interface 18.
The following describes the overall implementation of the real-time EEG detection and analysis platform based on the micro-state analysis method:
as shown in fig. 1, the EEG real-time detection and analysis platform based on the micro-state analysis method of the present invention is designed, and a DSP chip used as a core of data reading and analysis in the system is a 32-bit floating-point DSP processor chip of the model TMS320F28335 manufactured by Texas Instruments. Controlling an ADC module 4 to collect EEG signals through an EEG electrode array 1 by a man-machine interaction interface 18 in an upper computer 3, and carrying out preprocessing steps such as denoising, filtering and the like on the original EEG electrophysiological signals by an EEG preprocessing module 7 in a DSP operation chip 5 to obtain an EEG time sequence 10 for removing noise interference and providing a rhythm for research and analysis; inputting the preprocessed EEG signal 10 into a micro-state analysis module 11 in the DSP operation chip 5 for micro-state analysis; the human-computer interaction operation interface 18 of the upper computer 3 receives the feature result of the analyzed data from the DSP operation chip 5 through the USB communication module 6, and displays the EEG electrophysiological signals, the microstate topographic map and the result of the feature parameters.
The EEG pre-processing module 7 as shown in fig. 2 consists of an ICA myoelectric and ocular noise removal module 8 and a band-pass filtering module 9. The ICA electromyography and electrooculogram noise removing module 8 carries out denoising processing on the collected original EEG signals to obtain signals with noise interference removed, and the signals are processed by the band-pass filtering module 9 to obtain an EEG time sequence 10 which accords with the rhythm range required by data analysis.
The microstate analysis module 11 shown in fig. 3 is used to implement the analysis process for the preprocessed EEG time sequence, and is characterized by: the micro-state analysis module 11 comprises a GFP calculation and preprocessing module 12, an improved K-means clustering algorithm module 13, a micro-state reverse coupling module 14 and a micro-state feature parameter extraction module 15. The data analysis is carried out on the EEG time series through the four modules, and a characteristic microstate topographic map and a statistical analysis characteristic parameter are extracted. Finally, the DSP operating chip 5 uploads the results of the characteristic results of the analyzed data, the EEG electrophysiological signals, the microstate topographic map and the characteristic parameters to the human-computer interaction interface 18 of the upper computer 3 through the USB communication module 6.
The human-computer interaction interface 18 shown in fig. 4 includes an EEG signal and analysis result display interface 19, an operation option interface 20, and a collection device setting selection interface 21. The EEG signal and analysis result display interface 19 is mainly used for displaying EEG electrophysiological signals, microstate topographic maps and results of characteristic parameters; the operation option interface 20 starts, stops, pauses, refreshes, saves data, derives results, helps and other all basic control instructions for the human-computer interaction operation interface; the acquisition device sets up selection interface 21 and is used for selecting the electrode array dimension, sets up specification information.
Claims (1)
1. An EEG real-time detection and analysis platform based on a micro-state analysis method comprises an EEG electrode array (1) for collecting scalp electroencephalogram, a lower computer (2) for collecting, preprocessing and analyzing the scalp electroencephalogram and a micro-state and an upper computer (3) for displaying an analysis result, wherein the lower computer (2) comprises an ADC module (4), a DSP operation chip (5) and a USB communication module (6); the EEG electrode array (1) and the ADC module (4) realize the collection of scalp brain electrical signals; an EEG preprocessing module (7) in the DSP operation chip (5) performs a preprocessing process on the original EEG signal to obtain a clean EEG signal (10) for analysis; inputting the preprocessed EEG signal into a micro-state analysis module (11) and further analyzing and processing to obtain an analysis result and specific characteristic parameters; the real-time human-computer interaction interface (18) of the upper computer (3) is realized by VB programming, and the data communication between the upper computer (3) and the DSP operation chip (5) is realized through a USB communication module (6);
the DSP operation chip (5) comprises an EEG preprocessing module (7) and a micro-state analysis module (11), wherein the EEG preprocessing module (7) consists of an ICA electromyography and eye electric noise removal module (8) and a band-pass filtering module (9) and is used for obtaining a clean EEG time sequence for analysis; the micro-state analysis module (11) comprises a GFP calculation and preprocessing module (12), a K-means clustering algorithm module (13), a micro-state reverse coupling module (14) and a micro-state characteristic parameter extraction module (15); the modules are modules with algorithm realization functions, are realized by C + + compiling and downloaded into a DSP operation chip (9) to realize the data analysis and processing functions in the step;
the micro-state analysis module (11) is a core component for the platform to realize analysis and processing of EEG signals, and has the function of realizing micro-state analysis of EEG signals acquired in real time; the GFP calculation and preprocessing module (12) calculates a global field power time series (GFP) as a spatial standard deviation of an electroencephalogram topographic map at each given moment, the electroencephalogram has the maximum signal-to-noise ratio at a local GFP maximum point, the spatial configuration at the moment is considered to be stable, and the topographic maps between the topographic map at the GFP maximum point and an adjacent GFP maximum point have similar topological structures; obtaining the electroencephalogram maps at all maximum points of GFP to calculate the overall microstate structure; the K-means clustering algorithm module (13) is used for clustering and calculating all topographic maps formed by the signals at all GFP maximum points by using a K-means clustering algorithm to obtain individual micro-state classes; the micro-state back coupling module (14) is used for marking the brain electrical topographic map of the whole EEG signal by taking the obtained micro-state as a marking template, and marking the topographic map at all times as a specific one of the micro-state classes; the micro-state characteristic parameter extraction module (15) carries out statistical calculation on the results of the processes to obtain characteristic parameters such as average duration, occurrence frequency per second, overall coverage rate and transition probability among the micro-state classes of each micro-state class, and the characteristic parameters are used for describing abnormal activity of the brain and stable state of illness.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113367705A (en) * | 2021-04-07 | 2021-09-10 | 西北工业大学 | Motor imagery electroencephalogram signal classification method based on improved micro-state analysis |
| WO2022135449A1 (en) * | 2020-12-22 | 2022-06-30 | 北京航空航天大学 | Interictal epileptiform discharge activity detection apparatus and method for epileptic patient |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101032393A (en) * | 2006-03-09 | 2007-09-12 | 广西壮族自治区桂林茶叶科学研究所 | Multifunctional iatrical examining and monitoring method and device based on computer |
| CN102920453A (en) * | 2012-10-29 | 2013-02-13 | 泰好康电子科技（福建）有限公司 | Electroencephalogram signal processing method and device |
| CN108143411A (en) * | 2017-12-13 | 2018-06-12 | 东南大学 | A kind of tranquillization state brain electricity analytical system towards Autism Diagnostic |
| WO2018106038A1 (en) * | 2016-12-08 | 2018-06-14 | 정은지 | Training system using neurofeedback device and image content for improvement of developmental disorder function, and method thereof |
| CN108577835A (en) * | 2018-05-17 | 2018-09-28 | 太原理工大学 | A kind of brain function network establishing method based on micro- state |
-
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- 2020-01-17 CN CN202010051770.4A patent/CN111643075A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101032393A (en) * | 2006-03-09 | 2007-09-12 | 广西壮族自治区桂林茶叶科学研究所 | Multifunctional iatrical examining and monitoring method and device based on computer |
| CN102920453A (en) * | 2012-10-29 | 2013-02-13 | 泰好康电子科技（福建）有限公司 | Electroencephalogram signal processing method and device |
| WO2018106038A1 (en) * | 2016-12-08 | 2018-06-14 | 정은지 | Training system using neurofeedback device and image content for improvement of developmental disorder function, and method thereof |
| CN108143411A (en) * | 2017-12-13 | 2018-06-12 | 东南大学 | A kind of tranquillization state brain electricity analytical system towards Autism Diagnostic |
| CN108577835A (en) * | 2018-05-17 | 2018-09-28 | 太原理工大学 | A kind of brain function network establishing method based on micro- state |
Non-Patent Citations (1)
| Title |
|---|
| CHUNGUANG CHU ; XING WANG ; LIHUI CAI ; LEI ZHANG; JIANG WANG; CHEN LIU; XIAODONG ZHU: "Spatiotemporal EEG microstate analysis in drug-free patients with Parkinson\'s disease" * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022135449A1 (en) * | 2020-12-22 | 2022-06-30 | 北京航空航天大学 | Interictal epileptiform discharge activity detection apparatus and method for epileptic patient |
| CN113367705A (en) * | 2021-04-07 | 2021-09-10 | 西北工业大学 | Motor imagery electroencephalogram signal classification method based on improved micro-state analysis |
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