Electroencephalogram-based Morse code training effect evaluation method and system

Abstract

The invention relates to the technical field of electroencephalogram analysis, in particular to an electroencephalogram-based Morse code training effect evaluation method and system.The method comprises the steps that firstly, an experimental normal form for evaluating the Morse code training effect is designed, and then a subject is recruited to carry out Morse code training; behavior data and electroencephalogram signals of the subject participating in the experiment at two time points before training is started and after training is finished are collected, and then the collected electroencephalogram signals are preprocessed; then the behavior data of the subject is analyzed, and the reaction time and the accuracy are analyzed; the significance change of the N200 component in the ERP before and after training and between different code speeds is explored; and analyzing brain network differences and brain network attribute change conditions before and after Morse code training by adopting a PLV brain network. According to the method, the change of the brain processing mode before and after MC training is discussed from the three dimensions of behavior data, an ERP curve and a brain network, and the method for objectively and effectively evaluating the MC training effect is found.

Description

Technical Field

[0001] The present invention relates to the technical field of electroencephalogram (EEG) analysis, and in particular to an EEG-based Morse code training effect evaluation method and system. Background Art

[0002] Morse code (MC) is a signal code that is intermittent and uses different arrangements of "dips" and "dashes" (also known as "dots" and "dashes") to express different English letters, numbers and punctuation marks. It is a common communication method used by radio enthusiasts and is also the most basic and reliable means of communication in military communications.

[0003] How to judge the effect of MC training? The commonly used method is to organize MC listening test and judge the listening ability according to the completion time and accuracy. However, these tests are largely affected by the subjective judgment of the subjects and lack reliable physiological indicators for objectively evaluating the auditory discrimination ability. Some researchers have tried to find biomarkers of neuroplastic changes brought about by MC training. T. Schmidt-Wilcke et al found that after MC training, the subjects increased the neural activity in the brain areas closely related to language perception and memory when they heard MC signals. Lara Schlaffke et al proved that MC learning helps to induce neuroplastic adaptation in areas related to reading, language comprehension and language learning, and evaluated the role of white matter plasticity in language learning. Anu Kujala et al explored the dominant hemisphere changes in Morse code representation in adult MC learners. Maria Uther et al used electroencephalogram (EEG) signals to study the changes in the P3a component in event-related potentials (ERP) under different abnormal stimuli.

[0004] However, existing work mainly focuses on the study of brain plasticity changes and language decoding models. There has been no research on objective and effective methods to evaluate the effectiveness of MC training, and no EEG-based Morse code training effect evaluation method has been proposed. Summary of the invention

[0005] The present invention aims to solve the current lack of an objective method for evaluating the effect of MC training. A method and system for evaluating the effect of Morse code training based on EEG is proposed. The changes in brain processing patterns before and after MC training are explored from three dimensions: behavioral data, ERP curves, and brain networks, in order to find an objective and effective method for evaluating the effect of MC training.

[0006] To achieve the above purpose, the technical solution adopted is:

[0007] A method for evaluating the effect of Morse code training based on electroencephalogram, comprising the following contents:

[0008] Design an experimental paradigm to evaluate the effectiveness of Morse code training and recruit subjects to conduct Morse code training;

[0009] Collect the behavioral data and EEG signals of the subjects participating in the experiment before and after the training;

[0010] Preprocessing the collected EEG signals;

[0011] Analyze the subjects' behavioral data, including reaction time and accuracy;

[0012] To explore the significant changes of N200 components in ERP before and after training and between different coding speeds;

[0013] PLV brain network was used to analyze the differences in brain networks and changes in brain network properties before and after Morse code training.

[0014] According to the EEG-based Morse code training effect evaluation method of the present invention, further, the experimental paradigm designed to evaluate the Morse code training effect specifically includes:

[0015] ①Prepare experimental materials: The experimental stimulus material is the real audio of Morse code of 26 English letters;

[0016] ② Practice phase: Each subject trained 10 trials, each trial contained 1 stimulus, each stimulus was a Morse code of 1 letter, and the training stimulus material used was Morse code with a code rate of 15 CPM;

[0017] ③ Experimental stage: This stage is divided into 3 blocks, each block corresponds to a different code rate, namely 15CPM, 30CPM and 40CPM, and each block contains 52 trials; each trial includes four steps of "prompt-stimulus playback-judgment-black screen". The prompt stage is to display a white "cross" on the screen, and the stimulus playback time is determined by the code rate; the judgment stage requires the subjects to feedback the number of "beeps" heard in Morse code; when the subject's feedback is completed or timeout occurs, the screen will go black.

[0018] According to the EEG-based Morse code training effect evaluation method of the present invention, further, collecting the behavioral data and EEG signals of the subjects participating in the experiment before and after the training begins includes:

[0019] The experimental paradigm was written using E-Primer3.0 software, and the subjects' behavioral data were recorded through the software. The EEG signals were collected using a 64-channel HIamp active electrode EEG cap and g.Recoder software.

[0020] According to the EEG-based Morse code training effect evaluation method of the present invention, further, the collected EEG signals are preprocessed including electrooculogram removal, re-reference calculation, signal filtering, data segmentation, baseline correction and bad segment removal of the original EEG data.

[0021] According to the EEG-based Morse code training effect evaluation method of the present invention, further, the behavioral data of the subjects are analyzed, and the analysis of reaction time and accuracy includes: analyzing the significant differences in RT and ACC based on the collected behavioral data based on t-test, and performing statistical tests on the differences in RT and ACC between different stimulation materials in the same experiment and at the same code speed before and after training.

[0022] According to the EEG-based Morse code training effect evaluation method of the present invention, further, the significant changes of the N200 component in the ERP before and after training and between different code speeds are explored, including:

[0023] Firstly, the average brain topography of the N200 component before and after training at different code rates was analyzed, and then the difference brain topography and ERP curves at different code rates were evaluated using t-test analysis; when the N200 amplitude in the ERP curve increased significantly, it indicated that the Morse code training effect was good.

[0024] According to the EEG-based Morse code training effect evaluation method of the present invention, further, the PLV brain network analysis of the brain network differences before and after Morse code training includes:

[0025] The phase-locked value PLV between the EEG signals of any two leads was calculated to obtain the PLV connection matrix. Based on the PLV connection matrix, the PLV network difference map was calculated to compare the changes in the brain network before and after training. When the brain network connection was significantly enhanced, it indicated that the Morse code training effect was good.

[0026] According to the EEG-based Morse code training effect evaluation method of the present invention, further, using PLV brain network analysis to analyze changes in brain network attributes includes:

[0027] Based on the PLV connection matrix, the network properties of each subject were calculated, including clustering coefficient, path length, global efficiency and local efficiency. The changes in network properties before and after training were compared and analyzed. When there were significant differences in clustering coefficient, path length and local efficiency before and after training, and the global efficiency showed an upward trend, it indicated that the Morse code training effect was good.

[0028] Furthermore, the present invention also provides an electroencephalogram-based Morse code training effect evaluation system, comprising an experimental paradigm design module, a data acquisition module, a data preprocessing module, a behavior data evaluation module, an ERP evaluation module and a brain network evaluation module, wherein:

[0029] The experimental paradigm design module is used to design an experimental paradigm to evaluate the effect of Morse code training and recruit subjects to carry out Morse code training;

[0030] The data collection module is used to collect the behavioral data and EEG signals of the subjects participating in the experiment before and after the training;

[0031] A data preprocessing module is used to preprocess the collected EEG signals;

[0032] Behavioral data evaluation module, used to analyze the behavioral data of the subjects, including reaction time and accuracy;

[0033] ERP evaluation module, used to explore the significant changes of N200 components in ERP before and after training and between different coding speeds;

[0034] The brain network evaluation module is used to use PLV brain network to analyze the differences in brain networks and changes in brain network properties before and after Morse code training.

[0035] The beneficial effects achieved by adopting the above technical solution are:

[0036] The present invention proposes a method for evaluating the effect of Morse code training based on electroencephalogram. The method first designs an experimental paradigm for evaluating the effect of Morse code training, then recruits subjects to carry out Morse code training, and collects the behavioral data and electroencephalogram signals of the subjects participating in the experiment at two time points before (Pre) and after (Post) the training, and then pre-processes the electroencephalogram signals. Afterwards, the three dimensions of behavioral data, ERP curve and brain network are integrated to study the changes in brain processing patterns before and after MC training. This method well proves that Morse code training induces changes in brain plasticity, finds biomarkers that characterize the enhancement of Morse hearing recognition ability, constructs an objective and effective method for evaluating the effect of MC training, enriches the neuroscience theory of auditory cognition, and provides a scientific basis and practical guidance for subsequent EEG signal decoding in the MC hearing recognition state. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings of the embodiments of the present invention, wherein the drawings are only used to illustrate some embodiments of the present invention, but not to limit all embodiments of the present invention thereto.

[0038] Figure 1 It is the Morse code table of the 26 English letters;

[0039] Figure 2 is a flow chart of a method for evaluating the effect of Morse code training based on electroencephalogram (EEG) according to an embodiment of the present invention;

[0040] Figure 3is a flow chart of an experimental paradigm of an embodiment of the present invention;

[0041] Figure 4 RT and ACC distribution diagrams at different bit rates before and after training according to an embodiment of the present invention;

[0042] Figure 5 It is the difference statistics of RT and ACC at different code rates before and after training of the embodiment of the present invention;

[0043] Figure 6 It is the N200 brain topography and difference statistics before and after training of the embodiment of the present invention;

[0044] Figure 7 : N200 brain topography at different code rates according to an embodiment of the present invention;

[0045] Figure 8 : The difference brain topography and ERP curves at different code rates of the embodiment of the present invention;

[0046] Fig. 9 is a PLV network mode difference diagram before and after training of an embodiment of the present invention;

[0047] Fig.10 1 is a graph showing changes in network properties before and after training according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings of specific embodiments of the present invention to clearly and completely describe the exemplary scheme of the embodiment of the present invention. Unless otherwise defined, the technical terms or scientific terms used in the present invention should be the common meanings understood by people with ordinary skills in the field.

[0049] This embodiment discloses a method for evaluating the effect of Morse code training based on EEG. The Morse code table of 26 English letters is as follows: Figure 1 The data processing flow of this method is as shown in Figure 2 As shown, the following steps are included:

[0050] Step S1, design an experimental paradigm for evaluating the effect of Morse code training, and recruit subjects to carry out Morse code training. The experimental paradigm is designed for evaluating the effect of Morse code training, and effectively solves the problem of basic data collection for evaluation.

[0051] Using actual Morse code communication signals as stimulus materials, the subjects were organized to carry out MC learning training for 2 months. Each subject participated in experimental data collection once before (Pre) and after (Post) Morse code training. The experimental paradigm process is as follows: Figure 3As shown. The experiment is divided into two stages, the practice stage and the experimental stage. The practice stage is to let the subjects familiarize themselves with the basic experimental process. The experimental stage is divided into three blocks, each block corresponds to a different code rate (that is, the number of letters played per minute, that is, Character Per Minute, referred to as CPM), which are 15CPM, 30CPM and 40CPM respectively. Each Block contains 52 trials, each trial contains 1 stimulus, and each stimulus is a Morse code of 1 letter. Therefore, there are 52 English letter Morse codes in total, which are presented by randomly playing 26 English letter Morse codes twice. The specific experimental process is as follows:

[0052] Step S101, prepare experimental materials: The experimental stimulus material is the real audio of Morse code of 26 English letters, where "di" is 1 unit length, "da" is 3 units length, and the interval between dits and dats within a letter is 1 unit length, such as Figure 1 shown.

[0053] Step S102, experimental preparation: before the experiment, inform the subjects of experimental precautions, operation procedures and the Morse code rate to be sent in each block.

[0054] Step S103, practice phase: when the subject is fully prepared, press any key to start the experiment. Each subject is trained for 10 trials, mainly to make the subject familiar with the experimental content, and the training stimulus material uses Morse code with a code rate of 15 CPM.

[0055] Step S104, experimental stage: Each Trials contains 4 steps, namely "prompt-stimulation playback-judgment-black screen". The prompt stage is to display a white "cross" on the screen, and the presentation time is 300ms. The stimulation playback time is determined by the playback speed under different conditions, wherein 40CPM is 72ms to 936ms, and the time of 30CPM and 15CPM increases accordingly. The subjects do not make any movements when listening to Morse code, and try to keep still. The judgment stage requires the subjects to feedback the number of "di" heard in Morse code. Since the "di" sound is short, the number of "di" is selected as the experimental task. There are five choices of 0-4 for the number of "di" contained in 26 letters (wherein, 3 letters have 0 "di", 9 letters have 1 "di", 8 letters have 2 "di", 5 letters have 3 dot "di", and 1 letter has 4 "di"), and the task ends when the subjects feedback any 1 number from 0-4. The total time of the judgment phase is 3000ms. When the input is completed or times out, a black screen appears. The duration of the black screen is randomly displayed from 1000ms to 3000ms.

[0056] Step S2: Collect the behavioral data and EEG signals of the subjects before (Pre) and after (Post) the training.

[0057] During the experiment, the subjects sat in a comfortable and quiet shielded room. When collecting EEG data, the stimulus was presented to the subjects in the form of an external speaker, and the subjects were required to avoid obvious head movements as much as possible to ensure the quality of the collected data. During the data collection stage, the subjects needed to concentrate, listen carefully to the stimulus sound, and respond according to their own judgment. The experimental paradigm was written using E-Primer3.0 software, and the behavioral data of the subjects were recorded through the software, mainly recording the reaction time (RT) and task accuracy (ACC) of the subjects. The EEG signal was collected using the 64-channel HIamp active electrode EEG cap and g.Recoder software from Austrian g.tec. The sampling rate was set to 600Hz, the filter range was set to [0.1Hz, 100Hz], and the notch filter was set to 48Hz-52Hz. The EEG data collection included a total of 64 leads, of which lead 63 was connected to the right earlobe, lead 64 was empty, and the remaining valid EEG signal channels were 62 leads, all of which were used for subsequent analysis and research.

[0058] Step S3: pre-process the collected EEG signals.

[0059] For all subjects, the 62-channel raw EEG data were preprocessed according to the following steps:

[0060] ① Electrooculogram removal: The FastICA algorithm is used to decompose the EEG signal into independent components, select and remove the artifact components, and reconstruct the EEG signal without artifact interference.

[0061] ② Re-reference calculation: The data were re-referenced according to the reference electrode standardization technique (REST) ​​proposed by Professor Yao Dezhong and others.

[0062] ③Signal filtering: Use a bandpass filter and set the filter frequency to 1-10Hz.

[0063] ④ Data segmentation: Divide the continuous EEG data into several segments of equal length according to the stimulation events. Take the starting point of the experimental stimulation as the time point 0, and divide the EEG data into many data segments, each segment is 200ms before the stimulation to 1000ms after the stimulation.

[0064] ⑤Baseline correction: used to eliminate EEG noise caused by spontaneous EEG activity. The data 200ms before time point 0 is used as the baseline. It is assumed that the EEG signal before time point 0 represents the spontaneous EEG when receiving stimulation. The data after time point 0 is subtracted from the average value of the data points before time point 0, which can eliminate some of the spontaneous EEG noise.

[0065] ⑥ Bad segment elimination: Set the threshold value to ±100uv, define the data segments with amplitude greater than 100uV as bad segments, and eliminate them all.

[0066] Step S4: Analyze the behavioral data of the subjects, including reaction time (RT) and accuracy (ACC).

[0067] This protocol combines the collected behavioral data and focuses on evaluating RT and ACC. Based on the t-test, the significant differences in RT and ACC were analyzed. Statistical tests were performed on the differences in RT and ACC between different stimulus materials in the same experiment and at the same code rate before and after training. The results of the behavioral data analysis are shown in the figure. Figure 4 and Figure 5 As shown, the effect of training can be objectively evaluated based on behavioral data.

[0068] Step S5, explore the significant changes of the N200 component in the ERP before and after training and between different code speeds, and then characterize the differences in brain processing mechanisms before and after Morse code training.

[0069] Based on the preprocessed EEG signals, the average whole-brain topography around 200ms (150-250ms) before and after training was drawn, such as Figure 6 (a)(b) shows that (a) is the average brain topography of the N200 component before training, and (b) is the average brain topography of the N200 component after training. It can be seen from the figure that the activation area of ​​the N200 component is mainly concentrated in the frontal lobe and parietal lobe. Then a t-test analysis was performed on the difference of the whole brain N200. The results are as follows Figure 6 As shown in (c), it can be seen from the figure that there are significant differences in the N200 components before and after training in the four leads Fz, Fc2, Fc4 and P8 (p<0.05). Select the Fz lead and focus on analyzing the changes in N200 before and after training, as shown in Figure 6 As shown in (d), from the ERP curve of the Fz lead, it can be seen that there is a significant difference in the N200 component after training (p<0.05), and the amplitude is significantly increased.

[0070] Referring to the above analysis method, we first analyze the average brain topography of the N200 component before and after training at different code rates, such as Figure 7 Then, the t-test was used to evaluate the difference in brain topography and ERP curves at different code rates, as shown in Figure 8 As shown in Figure 2, the N200 amplitude in ERP increased significantly, which can objectively and effectively characterize the effect of MC training.

[0071] Step S6: using PLV (Phase Locking Value) brain network analysis to analyze the differences in brain networks and changes in brain network properties before and after Morse code training.

[0072] This protocol uses the PLV brain network analysis method. PLV is a statistic used to analyze functional brain networks. It can reflect the synchronized changes of neural activity over long distances and is often used to reveal the mechanism of action of brain networks in cognition, emotion, and other brain functions. In order to reduce the amount of calculation and the connection complexity of the brain network and reduce the impact of the volume effect, this protocol selects 21 typical EEG leads distributed throughout the brain, including Fp1, Fpz, Fp2, F7, F3, Fz, F4, F8, T7, C3, Cz, C4, T8, P7, P3, Pz, P4, P8, O1, O2, and Oz. The phase-locked value PLV between the EEG signals of any two leads is calculated to obtain a 21*21 connection matrix. Based on the PLV connection matrix, the PLV network difference map is calculated to compare the changes in the brain network before and after training. The results of PLV brain network analysis are shown in the figure below. Fig. 9 As shown, the figure shows the difference in PLV network patterns before and after training (p<0.05). Figure (a) shows the difference in brain network patterns of all subjects before and after training; Figure (b) shows the difference in brain network patterns of all subjects before and after training under the 15CPM stimulation condition; Figure (c) shows the difference in brain network patterns of all subjects before and after training under the 30CPM code rate stimulation condition; Figure (d) shows the difference in brain network patterns of all subjects before and after training under the 40CPM code rate stimulation condition. Fig. 9 It can be seen that the brain network connection after training is significantly enhanced in the right hemisphere compared to before training. As the code speed continues to increase, this difference becomes smaller and smaller. It can be seen that after Morse code training, the cerebral cortex of the subjects has been reshaped. At the same time, with the temporal lobe T8 as the hub node, the connection with the prefrontal lobe and occipital lobe is significantly enhanced. It can be seen that the network connection has changed significantly after training, indicating that the cerebral cortex has plasticity. Long-term training may lead to the remodeling of neural pathways, forming new connections between neurons or strengthening existing connections, thereby making information transmission more efficient and accurate.

[0073] Next, based on the PLV connection matrix, the network properties of each subject, namely clustering coefficient (CC), path length (CPL), global efficiency (Ge) and local efficiency (Le), are calculated to better evaluate the changes in network characteristics before and after training. The calculation methods of the four network properties are as follows:

[0074]

[0075]

[0076] Among them, d ij is the shortest weighted path length between i and j, w ij is the coherence coefficient between channel i and channel j, where coherence represents the linear relationship between signals of two different channels in a specific frequency domain, T is the number of nodes, and N is the set of all nodes in the network in a single trial.

[0077] According to the above four formulas, the four network attributes of network clustering coefficient, path length, local efficiency and global efficiency were calculated, and the changes of network attributes before and after training were compared and analyzed. The changes of the four attributes on 21 leads were compared, and the paired sample t test was used. There were significant differences in the three attributes of clustering coefficient, path length and local efficiency before and after training (p<0.01), and the global efficiency showed an upward trend. The changes of the four network attributes on 21 leads are shown in Figure 2. Fig.10 shown.

[0078] Depend on Fig.10 It can be seen that the clustering coefficients have increased significantly, indicating that the nodes in the local area of ​​the brain network are more closely connected, the information transmission efficiency of the local network has increased, and the information processing and transmission capabilities of the local area have been enhanced. The path lengths have all decreased to varying degrees, indicating that the information transmission paths between nodes in the network have become shorter and the information transmission efficiency has increased. The local efficiency has increased significantly, indicating that the information processing and transmission capabilities of the local area have been enhanced. The global efficiency has improved, indicating that the overall information transmission and parallel processing capabilities of the network have been improved. The differences in PLV brain networks and network properties can be used as objective physiological indicators to effectively evaluate the effect of MC training.

[0079] Corresponding to the above method, this embodiment also proposes an EEG-based Morse code training effect evaluation system, including an experimental paradigm design module, a data acquisition module, a data preprocessing module, a behavior data evaluation module, an ERP evaluation module and a brain network evaluation module, wherein:

[0080] The experimental paradigm design module is used to design an experimental paradigm to evaluate the effectiveness of Morse code training and recruit subjects to carry out Morse code training.

[0081] The data acquisition module is used to collect the behavioral data and EEG signals of the subjects participating in the experiment before and after the training.

[0082] The data preprocessing module is used to preprocess the collected EEG signals.

[0083] The behavior data evaluation module is used to analyze the behavior data of the subjects, including reaction time and accuracy.

[0084] The ERP evaluation module is used to explore the significant changes in the N200 component in the ERP before and after training and between different coding speeds.

[0085] The brain network evaluation module is used to use PLV brain network to analyze the differences in brain networks and changes in brain network properties before and after Morse code training.

[0086] The present invention designs a data collection experimental paradigm for evaluating the effect of Morse code training, carries out Morse code learning training, and collects EEG signals once before and after training. The experimental results show that after training, the average reaction time of the subjects is significantly shortened by 210.4ms, the recognition accuracy is significantly improved by 27.3%, the amplitude of the N200 component is significantly increased, and the connection of the right hemisphere brain network is significantly enhanced with the temporal lobe T8 lead as the Hub node, showing an obvious right hemisphere lateralization effect. It can be seen from the experimental results that the subjects show significant differences in behavioral data, ERP curves, and brain networks. The synaptic efficiency of relevant brain areas is improved, the neural signal transmission is faster and more accurate, and the MC hearing ability is significantly improved. Therefore, the three-dimensional indicators of behavioral data, ERP curves, and brain networks can be used as objective criteria for evaluating the effect of MC training.

[0087] The present invention has well demonstrated that MC training induces changes in brain plasticity, found biomarkers that characterize the enhancement of Morse hearing discrimination ability, constructed an objective and effective method to evaluate the effect of MC training, enriched the neuroscience theory of auditory cognition, and provided a scientific basis and practical guidance for the subsequent decoding of EEG signals under MC hearing discrimination state.

[0088] Unless otherwise specifically stated, the components, steps, numerical expressions and values ​​set forth in these embodiments do not limit the scope of the present invention.

[0089] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0090] The units and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. A person of ordinary skill in the art may use different methods to implement the described functions for each specific application, but such implementation is not considered to be beyond the scope of the present invention.

[0091] Those skilled in the art will appreciate that all or part of the steps in the above method can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, such as a read-only memory, a disk or an optical disk. Optionally, all or part of the steps in the above embodiment can also be implemented using one or more integrated circuits, and accordingly, each module / unit in the above embodiment can be implemented in the form of hardware or in the form of software function modules. The present invention is not limited to any specific form of combination of hardware and software.

[0092] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for evaluating the effect of Morse code training based on electroencephalogram, characterized in that: Contains the following: Design an experimental paradigm to evaluate the effectiveness of Morse code training and recruit subjects to conduct Morse code training; Collect the behavioral data and EEG signals of the subjects participating in the experiment before and after the training; Preprocessing the collected EEG signals; Analyze the behavioral data of the subjects, including reaction time and accuracy; To explore the significant changes of N200 components in ERP before and after training and between different coding speeds; PLV brain network was used to analyze the differences in brain networks and changes in brain network properties before and after Morse code training.

2. The method for evaluating the effect of Morse code training based on electroencephalogram according to claim 1, characterized in that: The experimental paradigm designed to evaluate the effectiveness of Morse code training specifically includes: ①Prepare experimental materials: The experimental stimulus material is the real audio of Morse code of 26 English letters; ② Practice phase: Each subject trained 10 trials, each trial contained 1 stimulus, each stimulus was a Morse code of 1 letter, and the training stimulus material used was Morse code with a code rate of 15 CPM; ③ Experimental stage: This stage is divided into 3 blocks, each block corresponds to a different code rate, namely 15CPM, 30CPM and 40CPM, and each block contains 52 trials; each trial includes four steps of "prompt-stimulus playback-judgment-black screen". The prompt stage is to display a white "cross" on the screen, and the stimulus playback time is determined by the code rate; the judgment stage requires the subjects to feedback the number of "beeps" heard in Morse code; when the subject's feedback is completed or timeout occurs, the screen will go black.

3. The method for evaluating the effect of Morse code training based on electroencephalogram according to claim 1, characterized in that: The behavioral data and EEG signals of the subjects participating in the experiment before and after the training were collected, including: The experimental paradigm was written using E-Primer3.0 software, and the subjects' behavioral data were recorded through the software. The EEG signals were collected using a 64-channel HIamp active electrode EEG cap and g.Recoder software.

4. The method for evaluating the effect of Morse code training based on electroencephalogram according to claim 1, characterized in that: The preprocessing of the collected EEG signals includes removing the electrooculogram (EOG) from the original EEG data, re-reference calculation, signal filtering, data segmentation, baseline correction and bad segment removal.

5. The method for evaluating the effect of Morse code training based on electroencephalogram according to claim 1, characterized in that: The behavioral data of the subjects were analyzed, and the analysis of reaction time and accuracy included: analyzing the significant differences in RT and ACC based on the t-test on the collected behavioral data, and performing statistical tests on the differences in RT and ACC between different stimulus materials in the same experiment and at the same code speed before and after training.

6. The method for evaluating the effect of Morse code training based on electroencephalogram according to claim 1, characterized in that: The significant changes in the N200 component of the ERP before and after training and between different coding speeds include: Firstly, the average brain topography of the N200 component before and after training at different code rates was analyzed, and then the difference brain topography and ERP curves at different code rates were evaluated using t-test analysis; when the N200 amplitude in the ERP curve increased significantly, it indicated that the Morse code training effect was good.

7. The method for evaluating the effect of Morse code training based on electroencephalogram according to claim 1, characterized in that: The differences in brain networks before and after Morse code training using PLV brain network analysis include: The phase-locked value PLV between the EEG signals of any two leads was calculated to obtain the PLV connection matrix. Based on the PLV connection matrix, the PLV network difference map was calculated to compare the changes in the brain network before and after training. When the brain network connection was significantly enhanced, it indicated that the Morse code training effect was good.

8. The method for evaluating the effect of Morse code training based on electroencephalogram according to claim 7, characterized in that: The changes in brain network attributes analyzed using PLV brain network include: Based on the PLV connection matrix, the network properties of each subject were calculated, including clustering coefficient, path length, global efficiency and local efficiency. The changes in network properties before and after training were compared and analyzed. When there were significant differences in clustering coefficient, path length and local efficiency before and after training, and the global efficiency showed an upward trend, it indicated that the Morse code training effect was good.

9. A Morse code training effect evaluation system based on EEG, characterized in that: It includes experimental paradigm design module, data acquisition module, data preprocessing module, behavioral data evaluation module, ERP evaluation module and brain network evaluation module, among which: The experimental paradigm design module is used to design an experimental paradigm to evaluate the effect of Morse code training and recruit subjects to carry out Morse code training; The data collection module is used to collect the behavioral data and EEG signals of the subjects participating in the experiment before and after the training; A data preprocessing module is used to preprocess the collected EEG signals; Behavioral data evaluation module, used to analyze the behavioral data of the subjects, including reaction time and accuracy; ERP evaluation module, used to explore the significant changes of N200 components in ERP before and after training and between different coding speeds; The brain network evaluation module is used to use PLV brain network to analyze the differences in brain networks and changes in brain network properties before and after Morse code training.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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