A self-awareness disorder auxiliary diagnosis system based on visual EEG signal analysis

Abstract

The present invention belongs to the technical field of medical equipment, and is specifically a self-awareness disorder auxiliary diagnosis system based on visual EEG signal analysis. The self-awareness disorder auxiliary diagnosis system of the present invention specifically includes an EEG signal acquisition system and a visual stimulation test analysis system; the EEG signal acquisition system includes a 64-lead EEG cap, a wireless EEG / ERP amplifier, a test laptop, an intelligent synchronization center, and a stimulation synchronizer; the visual stimulation test analysis system is placed in a computer, and is used to analyze and process the collected EEG signals, including a noise reduction processing module, a feature extraction module, and a classification and recognition module; finally, according to the classification and recognition results, the strength of the subject's self-awareness is analyzed. The system of the present invention tests the subject through a visual stimulation system, and analyzes the collected visual EEG signals. The test has good objectivity and high accuracy, and provides a scientific basis for the diagnosis of consciousness disorders.

Description

Technical Field

[0001] The present invention belongs to the technical field of medical equipment, and in particular relates to a self-awareness disorder auxiliary diagnosis system based on visual electroencephalogram signal analysis. Background Art

[0002] Self-awareness refers to an individual's confirmation of his or her current subjective state. Self-awareness mainly includes existence awareness, initiative awareness, unity awareness, unity awareness and boundary awareness. Self-awareness disorder refers to the fact that one or several of the above aspects are affected to varying degrees, so that the patient cannot correctly understand his or her current subjective state, including being unable to perceive his or her own existence, being unable to realize that he or she is a single, independent individual, being unable to correctly understand the difference between the current "I" and the past "I", and losing self-control and control over mental activities.

[0003] The main screening and diagnostic methods for self-awareness disorders are:

[0004] (1) Clinical classification: mainly giving verbal and various stimuli, observing the patient's reaction and making judgments; such as calling the patient's name, pushing and shaking the patient's shoulders and arms, pressing the supraorbital notch, acupuncture the skin, talking to the patient, and asking the patient to perform purposeful actions, etc.

[0005] (2) GLASGOW Coma Scale Assessment Method: This method mainly assesses the degree of consciousness disorder based on the response to eye opening, verbal stimulation and commanded movements.

[0006] The applications of vision-based brain-computer interfaces mainly include those based on stimulus-evoked potentials and those based on motor imagery. Among them, stimulus-evoked potentials include P300 potential, visual evoked potential (VEP), steady-state visual evoked potential (SSVEP), etc. P300 is a type of ERP (event-related potential), which is an endogenous special evoked potential related to cognitive function. P300 is a positive wave that appears about 300ms after an event (such as auditory or visual stimulation). SSVEP means that when a visual stimulus of a fixed frequency is received, the visual cortex of the human brain will produce a continuous response related to the stimulus frequency (at the base frequency or multiple of the stimulus frequency), which can give a positive visual stimulus in the experiment.

[0007] The above-mentioned brain-computer interface types of processing methods all analyze relevant experimental results by performing corresponding processing and classification identification on EEG signals.

[0008] In summary, the existing diagnostic methods for self-awareness disorders mainly evaluate and judge the patient's behavioral performance, which requires professional personnel to conduct testing and evaluation, and is highly dependent on specialization. In addition, the existing methods have one-sided reference indicators, lack scientific measurement indicators, and have low objectivity and accuracy. Summary of the invention

[0009] The purpose of the present invention is to provide a self-awareness disorder auxiliary diagnosis system with good objectivity and high accuracy.

[0010] The self-consciousness disorder auxiliary diagnosis system provided by the present invention is based on visual EEG signal analysis technology. It tests the subject through a visual stimulation system and analyzes the collected visual EEG signals to provide a scientific basis for the diagnosis of consciousness disorders. Specifically, it includes: an EEG signal acquisition system, a visual stimulation test analysis system; wherein:

[0011] The EEG signal acquisition system includes: 64-lead EEG cap 1, wireless EEG / ERP amplifier 2, subject laptop 3, intelligent synchronization center 4, stimulation synchronizer 6; see Figure 1 As shown; among which:

[0012] The 64-lead EEG cap 1 is used to receive EEG signals, one end of which is connected to the electrode and the other end is connected to the wireless EEG / ERP amplifier 2;

[0013] The 64-lead EEG cap 1 is made of waterproof fabric and looks like a hat. It is worn on the subject's scalp and has 64 wet electrodes. When using it, you need to apply conductive paste on the electrodes to reduce impedance.

[0014] The wireless EEG / ERP amplifier 2 is used to collect EEG potential information and perform basic processing such as amplification on the collected EEG signals; the wireless EEG / ERP amplifier 2 is magnetically attached to the rear end of the 64-lead EEG cap 1;

[0015] The test laptop computer 3 is equipped with a visual stimulation system for directly stimulating the subject's vision;

[0016] The intelligent synchronization center 4 realizes wireless communication between devices and wirelessly connects the wireless EEG / ERP amplifier 2 and the stimulation synchronizer 6 to the computer 5 via Wi-Fi;

[0017] The stimulus synchronizer 6 cooperates with the subject laptop computer 3 to synchronize with the configured visual stimulus system, and marks while stimulating, for event marking of stimulus signals;

[0018] The visual stimulation test analysis system is used to analyze and process the collected EEG signals, and includes: a noise reduction processing module, a feature extraction module, and a classification and recognition module; the visual stimulation test analysis system is configured with a computer 5, wherein:

[0019] The noise reduction processing module uses wavelet transform to perform noise reduction on the collected EEG signal;

[0020] The feature extraction module uses an independent component analysis algorithm to remove signals from sources different from the EEG and highlights the features through downsampling;

[0021] The classification and recognition module uses a support vector machine (SVM) algorithm to classify and recognize the extracted features; first, two coarse-grained classifications of oneself and others are performed; then three classifications of oneself, unfamiliar others, and familiar people are performed; and then fine-grained classifications of different people at different times are performed; finally, based on the classification and recognition results, the strength of the subject's self-awareness is analyzed.

[0022] The wireless EEG / ERP amplifier 2 and the stimulation synchronizer 6 are both wirelessly connected to the computer 5 through the intelligent synchronization center 4 to achieve experiment labeling and EEG data transmission.

[0023] In the present invention, the visual stimulation system is actually a specially constructed picture library for testing subjects; the picture library includes:

[0024] (1) 15 photos of children aged 3-6 years old; including 5 photos of strangers, 5 photos of well-known celebrities, and 5 photos of the subject himself;

[0025] (2) 15 photos of people aged 10-14 years old; including: 5 photos of strangers, 5 photos of well-known celebrities, and 5 photos of the subject himself;

[0026] (3) 15 photos of young people aged 18-25; including: 5 photos of strangers, 5 photos of well-known celebrities, and 5 photos of the subject himself.

[0027] The self-awareness disorder auxiliary diagnosis system provided by the present invention has a test process system as follows:

[0028] (i) The subject wears the EEG cap 1, initializes the brain-computer interface device, and establishes communication between the wireless EEG / ERP amplifier 2 and the computer 5 through the intelligent synchronization center 4; the subject is placed in a quiet and undisturbed environment;

[0029] (ii) The test was started and EEG signal collection was started at the same time; the subject stared at the computer screen, and a photo of the subject's childhood randomly appeared on the screen every 3 seconds, for a total of 60 photos (20 of which were photos of 5 different strangers, 20 of which were photos of 5 different well-known celebrities, and 20 of 5 different photos of the subject himself); when collecting signals, each time a photo appeared, the system would mark the time when the photo appeared; in addition, in order to enhance stimulation, all pictures would flash at the same frequency (12 Hz);

[0030] (iii) Stop signal collection, rest for one minute, and repeat step 2, but change the test photo to a photo of the adolescent period;

[0031] (iv) Stop signal collection, rest for one minute, and repeat step 2, but change the test photo to a photo of the youth;

[0032] (V) Stop signal acquisition, rest for two minutes, repeat steps 2 to 4 twice, and complete three sets of tests in total;

[0033] After the test, the EEG signal data of the subject is obtained;

[0034] (VI) Computer 5 analyzes and processes the collected EEG signals; after the visual stimulation test analysis system is used, a classification and recognition result is obtained, and the strength of the subject's self-awareness is given.

[0035] Compared with the prior art, the present invention has the following advantages:

[0036] (1) The present invention provides a new self-awareness disorder auxiliary diagnosis system;

[0037] (2) The hardware equipment technology involved in the present invention is relatively mature and can be directly obtained and used;

[0038] (3) The present invention analyzes and processes physiological spontaneous EEG signals, which is more accurate and objective than the experimental results obtained by traditional testing methods;

[0039] (4) The processing and classification of EEG signals in the present invention can be improved according to different experimental contents and has strong plasticity;

[0040] (5) The present invention can be widely applied to various tests related to self-awareness, and will help promote the research and development of self-awareness;

[0041] (6) The present invention does not require a professional evaluator and has no special requirements for the testing location. It has low professional dependence and strong popularity. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 Schematic diagram of the EEG signal acquisition system.

[0043] Figure 2 The figure shows the test process.

[0044] Numbers in the figure: 1 is a 64-lead EEG cap, 2 is a wireless EEG / ERP amplifier, 3 is a laptop computer for the subject, 4 is an intelligent synchronization center, 5 is a computer, and 6 is a stimulation synchronizer. DETAILED DESCRIPTION

[0045] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0046] like Figure 1 As shown, this embodiment provides an EEG signal acquisition system and a visual stimulation test analysis system.

[0047] The EEG signal acquisition system includes: a 64-lead EEG cap 1, a wireless EEG / ERP amplifier 2, a subject laptop 3, an intelligent synchronization center 4, a computer 5, and a stimulation synchronizer 6. Among them, the 64-lead EEG cap 1 is made of waterproof fabric and looks like a hat. It is used to be worn on the subject's scalp. It has 64 wet electrodes. When in use, it is necessary to apply conductive paste on the electrodes to reduce impedance. It is used to receive EEG signals. One end is connected to the electrodes and the other end is connected to the wireless EEG / ERP amplifier 2; the wireless EEG / ERP amplifier 2 is magnetically adsorbed on the back end of the 64-lead EEG cap 1 to collect EEG potential information and perform basic processing such as amplification on the collected EEG signals; the subject laptop 3 is equipped with a visual stimulation system to directly stimulate the subject's vision; the intelligent synchronization center 4 wirelessly connects the wireless EEG / ERP amplifier 2 and the stimulation synchronizer 6 to the computer 5 through Wi-Fi to achieve wireless communication between devices; the stimulation synchronizer 6 cooperates with the subject laptop 3 to synchronize with the configured visual stimulation system, and marks while stimulating, which is used to mark the stimulation signal for event.

[0048] The visual stimulation test and analysis system includes: a noise reduction processing module, a feature extraction module, and a classification and recognition module; the visual stimulation test and analysis system is configured with a computer 5, and the wireless EEG / ERP amplifier 2 and the stimulation synchronizer 6 are wirelessly connected to the computer 5 through the intelligent synchronization center 4 to realize experimental annotation and EEG data transmission. Among them: the noise reduction processing module uses wavelet transform to perform noise reduction processing on the collected EEG signals; the feature extraction module uses an independent component analysis algorithm to remove signals from different sources from the EEG, and highlights the features through downsampling. The classification and recognition module uses a support vector machine (SVM) algorithm to classify and recognize the extracted features; among them: first, two coarse-grained classifications of oneself and others are performed; then three classifications of oneself, unfamiliar others, and familiar people are performed; and fine-grained classifications of different people at different times are added; finally, according to the classification and recognition results, the strength of the subject's self-awareness is analyzed.

[0049] In the present invention, the visual stimulation system is actually a specially constructed picture library for testing subjects; the picture library includes:

[0050] (1) 15 photos of children aged 3-6 years old; including 5 photos of strangers, 5 photos of well-known celebrities, and 5 photos of the subject himself;

[0051] (2) 15 photos of people aged 10-14 years old; including: 5 photos of strangers, 5 photos of well-known celebrities, and 5 photos of the subject himself;

[0052] (3) 15 photos of young people aged 18-25; including: 5 photos of strangers, 5 photos of well-known celebrities, and 5 photos of the subject himself.

[0053] The test steps for this example are as follows:

[0054] (i) The subject wears the EEG cap 1, initializes the brain-computer interface device, and establishes communication between the wireless EEG / ERP amplifier 2 and the computer 5 through the intelligent synchronization center 4; the subject is placed in a quiet and undisturbed environment;

[0055] (ii) The test was started and EEG signal collection was started at the same time; the subject stared at the computer screen, and a photo of the subject's childhood randomly appeared on the screen every 3 seconds, for a total of 60 photos (20 of which were photos of 5 different strangers, 20 of which were photos of 5 different well-known celebrities, and 20 of 5 different photos of the subject himself); when collecting signals, each time a photo appeared, the system would mark the time when the photo appeared; in addition, in order to enhance stimulation, all pictures would flash at the same frequency (12 Hz);

[0056] (iii) Stop signal collection, rest for one minute, and repeat step 2, but change the test photo to a photo of the adolescent period;

[0057] (iv) Stop signal collection, rest for one minute, and repeat step 2, but change the test photo to a photo of the youth;

[0058] (V) Stop signal acquisition, rest for two minutes, repeat steps 2 to 4 twice, and complete three sets of tests in total;

[0059] After the test, the EEG signal data of the subject is obtained;

[0060] (VI) Computer 5 analyzes and processes the collected EEG signals; after the visual stimulation test analysis system, a classification and recognition result is obtained to assist in determining the self-awareness state of the subject.

[0061] In summary, the present invention can detect the brain wave signals of the subject through the visual stimulation test analysis system, and can analyze the self-consciousness state of the subject in combination with the signal analysis technology to assist in the diagnosis of self-consciousness disorders and improve the accuracy of consciousness disorder diagnosis.

Claims

1. A testing method for a self-awareness disorder auxiliary diagnosis system based on visual EEG signal analysis, characterized in that: The system tests the subjects through a visual stimulation system and analyzes the collected visual EEG signals to provide a scientific basis for the diagnosis of consciousness disorders. It specifically includes: an EEG signal acquisition system and a visual stimulation test and analysis system; among which: The EEG signal acquisition system includes: a 64-lead EEG cap, a wireless EEG / ERP amplifier, a test laptop, an intelligent synchronization center, and a stimulation synchronizer; wherein: The 64-lead EEG cap is made of waterproof fabric and is worn on the subject's scalp to receive EEG signals. It has 64 wet electrodes and one end is connected to a wireless EEG / ERP amplifier. The wireless EEG / ERP amplifier is used to collect EEG potential information and amplify the collected EEG signals; the wireless EEG / ERP amplifier is magnetically attached to the rear end of the 64-lead EEG cap; The tested laptop computer is equipped with a visual stimulation system for directly stimulating the subject's vision; The smart synchronization center wirelessly connects the wireless EEG / ERP amplifier to the computer via Wi-Fi; The stimulus synchronizer cooperates with the subject's laptop computer to synchronize with the configured visual stimulus system, marking while stimulating, and is used to mark the stimulus signal for event marking; The visual stimulation test analysis system is used to analyze and process the collected EEG signals, and includes: a noise reduction processing module, a feature extraction module, and a classification and recognition module; the visual stimulation test analysis system is configured with a computer, wherein: The noise reduction processing module uses wavelet transform to perform noise reduction on the collected EEG signal; The feature extraction module uses an independent component analysis algorithm to remove signals from sources different from the EEG and highlights the features through downsampling; The classification and recognition module uses a support vector machine (SVM) algorithm to classify and recognize the extracted features; first, a coarse-grained classification of self and others is performed; then, a three-level classification of self, unfamiliar others, and familiar people is performed; and then a fine-grained classification of different people at different times is performed; finally, based on the classification and recognition results, the strength of the subject's self-awareness is analyzed; The wireless EEG / ERP amplifier and stimulation synchronizer are both wirelessly connected to the computer through the intelligent synchronization center to achieve experimental annotation and EEG data transmission; The visual stimulation system is a constructed picture library for testing subjects; the picture library includes: (1) 15 photos of children aged 3-6 years old; including 5 photos of strangers, 5 photos of well-known celebrities, and 5 photos of the subject himself; (2) 15 photos of people aged 10-14 years old; including: 5 photos of strangers, 5 photos of well-known celebrities, and 5 photos of the subject himself; (3) 15 photos of young people aged 18-25, including 5 photos of strangers, 5 photos of well-known celebrities, and 5 photos of the subject himself; The specific process of the test method is as follows:

1. The subjects put on the EEG cap, initialized the brain-computer interface device, and established communication between the wireless EEG / ERP amplifier and the computer through the intelligent synchronization center; the subjects were placed in a quiet and undisturbed environment; (ii) The test was started and EEG signal collection was started at the same time; the subject stared at the computer screen, and a photo of the subject's childhood randomly appeared on the screen every 3 seconds, for a total of 60 photos; 20 of which were photos of 5 different strangers, 20 were photos of 5 different well-known celebrities, and 20 were 5 different photos of the subject himself; when collecting signals, each time a photo appeared, the system would mark the time when the photo appeared; (iii) Stop signal collection, rest for one minute, and repeat step 2, but change the test photo to a photo of the adolescent period; (iv) Stop signal collection, rest for one minute, and repeat step 2, but change the test photo to a photo of the youth; (V) Stop signal acquisition, rest for two minutes, repeat steps 2 to 4 twice, and complete three sets of tests in total; After the test, the EEG signal data of the subject is obtained; (VI) The computer analyzes and processes the collected EEG signals; after the visual stimulation test analysis system is used, the classification and recognition results are obtained, and the strength of the subject's self-awareness is given.

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