A novel method and system for detecting personnel concentration based on EEG signals

Abstract

The present invention discloses a novel method and system for detecting the degree of concentration of personnel based on electroencephalogram signals, which belongs to the field of concentration detection research and technology. The steps of the concentration detection method include collecting brain wave signals, analyzing electroencephalogram signals, designing a concentration index unique to the subject, and applying the unique concentration index to the daily work of the subject. The concentration detection system includes an electroencephalogram acquisition module, a microprocessor module, a storage module, a power supply module, and a display module. Through the present invention, a concentration detection model of the subject is trained, and the model no longer simply uses the energy ratio within a frequency band of an agreed frequency range to represent the concentration detection index, but takes into account the differences in the specific range of the rise and fall of brain wave energy between individuals in a state of concentration to determine the concentration detection model. The subject calculates the concentration index result through his own unique concentration monitoring model, which greatly improves the accuracy of concentration detection.

Description

Technical Field

[0001] The present invention relates to a novel method and system for detecting the degree of concentration of a person based on electroencephalogram signals, and belongs to the technical field of concentration detection research. Background Art

[0002] EEG signals can be divided into two categories. One is called time-correlated brain waves, also known as induced brain waves, which are brain waves produced by the brain in response to stimulation. The other is called spontaneous brain waves, which are the opposite of induced brain waves and refer to regular potential signals spontaneously generated by the brain. The research on EEG signals has a history of nearly 80 years. It has significant applications in science and technology, military, entertainment, virtual reality and many other fields, especially in clinical medicine. As an important indicator reflecting the functional state of the brain, it has always been valuable in clinical diagnosis and scientific research in the past, present and future.

[0003] Existing studies have shown that the EEG frequency range of clinical EEG recording analysis is roughly 1.0-100 Hz, with the main frequency band concentrated between 0.3-30 Hz, which can be divided into four important bands, namely δ (0.5-3 Hz), θ (4-7 Hz), α (8-13 Hz), and β (14-30 Hz), which are delta, theta, alpha, and beta rhythms, respectively. Their frequency ranges and waveforms are shown in Figure 2. Figure 1 shown.

[0004] EEG signals are generated at a relatively rich frequency, and EEG signals of different bands are related to different states of thought or activity. For example, beta waves appear when one is nervous or excited, and theta waves are more prominent when one is tired or depressed. It can be seen that brain waves are like indicators of brain function status, and different EEG signal characteristics are displayed at different ages, physical conditions, and psychological states. The significance of further classifying brain wave signals is self-evident. This is not only closely related to people's mental and physical health, but also creates a theoretical foundation for the research and development of brain wave control equipment.

[0005] According to the research results of neuroscience, the correlation between the state of attention and the rhythm of EEG signals has been generally recognized. In 1985, William et al. found that in EEG signals, the alpha and beta bands reflect the subject's level of concentration. When entering a state of concentration, the subject's alpha band energy decreases and the beta band energy increases. A large number of other studies have investigated the changes in the energy ratio between beta, alpha and other different frequency bands caused by attention. In general, many studies believe that increased activity in high-frequency bands such as beta is an indicator of attention awakening. In contrast, the energy of the alpha and theta bands decreases when attention is awakened. Therefore, in practice, researchers often use the ratio of beta band energy to alpha band energy as a mathematical indicator of concentration. Similarly, the ratio of beta band energy to the sum of alpha and theta band energies can also be used as another mathematical indicator of concentration.

[0006] However, there is a problem with using the energy ratio of different frequency bands of EEG signals as an indicator of concentration detection, which greatly limits its application in practice. That is, in actual detection, the effectiveness of this indicator varies from person to person, and the calculation method that treats all subjects equally ignores the differences between individuals. For some subjects, this indicator often cannot accurately detect the concentration level. In order to solve this problem, some researchers have proposed combining multiple indicators to measure the concentration level, such as taking a weighted average of the β / α and β / θ indicators to obtain a new indicator to detect the concentration level.

[0007] The above method based on combining multiple indicators has solved the problem of concentration detection accuracy to a certain extent. However, since each indicator is still a traditional frequency band energy indicator, the detection of concentration level is still not accurate and stable enough.

[0008] Some researchers have also proposed using the complexity of EEG signals as another indicator for concentration detection. Complexity indicators include approximate entropy, sample entropy, cross entropy, etc. The measure of complexity should be close to the difficulty of constructing and describing an object or a system. Related studies have also confirmed that there are significant statistical differences in complexity indicators in the state of concentration and non-concentration.

[0009] However, the calculation of concentration indicators based on complexity calculation is complicated, the calculation model needs to obtain data through special experiments for training and calibration, and the effect is unstable. Summary of the invention

[0010] In order to solve the above technical problems, the purpose of the present invention is to provide a new method and system for detecting the degree of concentration of a person based on EEG signals, which can train different individuals according to their EEG characteristics to obtain a unique concentration detection model, and obtain the concentration index result through new concentration index calculation, thereby greatly improving the accuracy of detecting the degree of concentration of different individuals.

[0011] As shown in the above background technology section, the existing methods for detecting concentration based on the frequency band energy of EEG signals are all unstable. Specifically, when using the energy ratio of each EEG band as the basis for calculating the concentration index, in general practice, the brain wave division standards adopted are as described in the prior art, namely δ (0.5-3Hz), θ (4-7Hz), α (8-13Hz), β (14-30Hz), which are delta, theta, alpha and beta rhythms respectively. However, even if the above division standards are statistically applicable to a wide range of people, they may not be applicable to every individual. For example, for a subject, the brain wave energy in the focused state decreases in the range of 8-15Hz compared to the brain wave energy in the non-focused state, and increases in the range of 16-30Hz. It can be seen that using the traditional β band (14-30Hz) and α band (8-13Hz) energy ratio as a digital indicator of concentration is not the optimal concentration indicator. Therefore, in order to solve this problem, the present invention proposes a concentration level indicator which is adjustable according to the individual condition band range based on training and a corresponding complete concentration state detection system.

[0012] A novel method for detecting the concentration level of a person based on EEG signals comprises the following steps:

[0013] Step 1: Collect brain wave signals

[0014] Conduct a concentration induction experiment on the subjects, use different experimental tasks to make the subjects produce two states: non-concentration and concentration, and record the subjects' EEG signals in the two states;

[0015] Step 2: Analyze EEG signals

[0016] Calculate the ratio of the power spectral density of the EEG signal in the two states in step 1, that is, P 专注 / P 非专注 According to the ratio, the frequency range of the brain wave energy increase when the subject's EEG signal is induced in the state of concentration is recorded, that is, W 升 , and the frequency range where brain wave energy decreases, namely W 降 ;

[0017] Step 3: Design a subject-specific concentration indicator

[0018] According to the calculation and recording results of step 2, a concentration index suitable for the subject is designed, that is, the ratio of the energy of the EEG signal in the rising frequency band to the energy in the falling frequency band, that is, W 升 / W 降, an increase in this index indicates that the subject is in a state of concentration, while a decrease in this index indicates that the subject is in a state of non-concentration;

[0019] Step 4: Apply the unique concentration index to the subjects’ daily tasks

[0020] The concentration index designed in step three is applied to a conventional concentration monitoring system, and the subjects use the system to monitor their concentration status during daily tasks.

[0021] As a preferred example, the concentration induction experiment includes the Continuous Performance Test (CPT), which is a test for attention maintenance, selective attention and impulsiveness. There are multiple versions, which require continuous responses to different numbers or symbols, and the results are scored according to the number of correct responses, the number of omissions, the number of errors and the time.

[0022] As a preferred example, the power spectrum density calculation method includes the Welch method and the Yule-Walker method.

[0023] As a preferred example, the conventional concentration monitoring system includes an EEG acquisition device, a microprocessor for processing and analyzing EEG signals, a power supply device, and an alarm for receiving electrical signals from the microprocessor. The EEG acquisition device and the alarm are electrically connected to the microprocessor respectively, and the power supply device supplies power to the EEG acquisition device, the microprocessor, and the alarm.

[0024] As a preferred example, the alarm includes a voice module, a warning light module and a vibration module. The alarm is used to receive an alarm electrical signal sent by a microprocessor and trigger an alarm. The alarm electrical signal is obtained by converting an electroencephalogram signal when the monitored person is in a non-concentrated state.

[0025] A novel system for detecting the concentration level of personnel based on EEG signals, which implements the above method, includes:

[0026] EEG acquisition module, used for:

[0027] Obtain EEG signals in both non-focused and focused states;

[0028] Microprocessor module for:

[0029] Processing the EEG signals acquired by the EEG acquisition module in a focused state and a non-focused state, and calculating the ratio of the power spectral density of the EEG signals in the two states;

[0030] Based on the ratio, the frequency range of the increase and decrease of brain wave energy when the EEG signal is induced in the state of concentration is obtained;

[0031] Based on the results of the power spectrum density ratio and the frequency band range, the concentration index is obtained, that is, the ratio of the energy of the EEG signal in the rising frequency band to the energy in the falling frequency band;

[0032] Storage modules for:

[0033] Storing the EEG signals acquired by the EEG acquisition module in a focused state and a non-focused state;

[0034] storing the power spectrum density ratio obtained by the microprocessing module;

[0035] Stores the frequency range of brain wave energy increase and decrease when the EEG signal is induced in a state of concentration;

[0036] Store focus metrics;

[0037] Power supply modules for:

[0038] Power is supplied to the EEG acquisition module, the microprocessing module and the storage module.

[0039] As a preferred example, the concentration detection system of the present invention further includes a display module, which is used to visually output the concentration index, and the display module is electrically connected to the microprocessor module and the power supply module respectively.

[0040] The beneficial effects of the present invention are:

[0041] Through the present invention, a new method and system for detecting the degree of concentration of personnel based on EEG signals are provided. Through training, a concentration detection model of the subject is obtained. The model no longer simply uses the energy ratio within the frequency band of the agreed frequency range to represent the concentration detection index, but takes into account the differences in the specific range of brain wave energy rise and fall between individuals in the state of concentration to determine the concentration detection model. The subject calculates the concentration index result through his own unique concentration monitoring model, which greatly improves the accuracy of concentration detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 The frequency range and waveform of the EEG signal;

[0043] Figure 2 A processing flow chart of a novel method for detecting the degree of concentration of a person based on EEG signals provided by the present invention;

[0044] Figure 3 It is a circuit principle block diagram of an existing conventional concentration monitoring system;

[0045] Figure 4 A circuit structure diagram of a novel personnel concentration detection system based on EEG signals provided by the present invention;

[0046] Figure 5 is a coordinate diagram of the ratio of power spectral density of the subject in the focused and non-focused states in an embodiment of the present invention;

[0047] Figure 6 This is a schematic diagram of the structure of an EEG acquisition device commonly used in the prior art. DETAILED DESCRIPTION

[0048] In order to make the technical means, creative features, objectives and effects of the present invention easier to understand, the present invention is further described below in conjunction with specific illustrations and embodiments.

[0049] Example:

[0050] The present invention is a novel method and system for detecting the degree of concentration of a person based on EEG signals. The traditional method for detecting the degree of concentration of a person based on EEG signals generally calculates the energy of each EEG band and further calculates the ratio between them to obtain a digital index characterizing the degree of concentration and the level of concentration. The typical concentration index is the energy ratio of the β band to the α band, and the division criteria of the β band and the α band are both the β band (14-30Hz) and the α band (8-13Hz). However, such an equal calculation method ignores the differences between individuals.

[0051] like Figure 2 As shown, it is a processing flow chart of the novel method for detecting the concentration level of personnel based on EEG signals provided by the present invention, which includes four steps:

[0052] Step 1: Collect brain wave signals

[0053] Conduct a concentration induction experiment on the subjects, use different experimental tasks to make the subjects produce two states: non-concentration and concentration, and record the subjects' EEG signals in the two states;

[0054] Step 2: Analyze EEG signals

[0055] Calculate the ratio of the power spectral density of the EEG signal in the two states in step 1, that is, P 专注 / P 非专注 According to the ratio, the frequency range of the brain wave energy increase when the subject's EEG signal is induced in the state of concentration is recorded, that is, W 升 , and the frequency range where brain wave energy decreases, namely W 降 ;

[0056] Step 3: Design a subject-specific concentration indicator

[0057] According to the calculation and recording results of step 2, a concentration index suitable for the subject is designed, that is, the ratio of the energy of the EEG signal in the rising frequency band to the energy in the falling frequency band, that is, W升 / W 降 , the increase of this index indicates that the subject is in a state of concentration, while the decrease of this index indicates that the subject is in a state of non-concentration;

[0058] Step 4: Apply the unique concentration index to the subjects’ daily tasks

[0059] The concentration index designed in step three is applied to a conventional concentration monitoring system, and the subjects use the system to monitor their concentration status during daily tasks.

[0060] Among them, the concentration induction experiment includes the Continuous Performance Test (CPT), which tests attention maintenance, selective attention and impulsiveness. There are multiple versions of the Continuous Performance Test, which requires continuous responses to different numbers or symbols. The results are scored according to the number of correct responses, the number of omissions, the number of errors and the time.

[0061] Power spectral density calculation methods include Welch method and Yule-Walker method.

[0062] like Figure 3 As shown, the circuit principle block diagram of the existing conventional concentration monitoring system, the conventional concentration monitoring system includes an EEG acquisition device, a microprocessor for processing and analyzing EEG signals, a power supply device and an alarm for receiving electrical signals from the microprocessor. The EEG acquisition device and the alarm are electrically connected to the microprocessor respectively, and the power supply device supplies power to the EEG acquisition device, the microprocessor and the alarm.

[0063] The alarm includes a voice module such as a speaker, a warning light module such as a warning light, and a vibration module such as a vibrator. The alarm is used to receive an alarm electrical signal sent by a microprocessor and trigger an alarm to remind the monitored person. The alarm electrical signal is obtained by converting the EEG signal of the monitored person when the person is in a non-concentrated state.

[0064] like Figure 4 FIG. 1 is a circuit structure block diagram of a novel personnel concentration detection system based on EEG signals provided in the present invention. The concentration detection system of the present invention is used to execute the above-mentioned concentration detection method, and it includes:

[0065] The EEG acquisition module can use existing EEG signal acquisition equipment, such as Figure 6 The Epoc EEG signal acquisition device from Emotiv is shown, which is used for:

[0066] Obtain EEG signals in both non-focused and focused states.

[0067] Microprocessor module, using microprocessor controller, for:

[0068] Processing the EEG signals in the focused state and the non-focused state acquired by the EEG acquisition module, and calculating the ratio of the power spectral density of the EEG signals in the two states;

[0069] Then, based on the ratio, the frequency range of the brain wave energy increase and decrease when the EEG signal is induced in the state of concentration is obtained;

[0070] Then, based on the results of the power spectral density ratio and the frequency band range, the concentration index is obtained, that is, the ratio of the energy of the EEG signal in the rising frequency band to the energy in the falling frequency band.

[0071] The storage module uses a memory for:

[0072] Storing the EEG signals acquired by the EEG acquisition module in the focused state and the non-focused state;

[0073] storing the power spectrum density ratio obtained by the microprocessing module;

[0074] Stores the frequency range of brain wave energy increase and decrease when the EEG signal is induced in a state of concentration;

[0075] and storage focus indicators.

[0076] Power supply module, using battery, for:

[0077] Provide power to the EEG acquisition module, microprocessor module and storage module.

[0078] Display module, using a display, is used for:

[0079] Visualize the focus indicators.

[0080] Although the conventional concentration monitoring system and the concentration detection system in the present invention have different functions, the detection and monitoring functions can be realized by using the same set of equipment. The two systems commonly include an EEG signal acquisition device and a microprocessor.

[0081] For example, for a specific subject, the brain wave energy in the focused state decreases in the range of 8-15Hz, and increases in the range of 16-30Hz compared to the brain wave energy in the non-focused state. For this subject, a better indicator is the ratio of the brain wave energy in the range of 16-30Hz to the brain wave energy in the range of 8-15Hz. Compared with the traditional energy ratio of the β band (14-30Hz) and the α band (8-13Hz), it can collect more brain wave energy fluctuations that reflect changes in the subject's concentration, and thus can also obtain better prediction and characterization levels.

[0082] In order to achieve the above-mentioned concentration calculation index with adjustable band range for individual characteristics, the first step is to clarify the specific changes in the energy of brain waves in each band when the individual is in a state of concentration or with a high level of attention. We can achieve this goal through a customized concentration induction experiment. Specifically, each subject can participate in the following experiment, which is divided into two stages. The first stage lasts for 2 minutes. The subject keeps his eyes open in this stage, but maintains a resting state. The subject has no clear task. After the first stage, the subject immediately enters the second stage of the experiment; the second stage also lasts for 2 minutes, but the subject needs to complete a customized experimental task in this stage, called the CPT task. In order to complete this task, the subject needs to respond quickly to the English letters that appear randomly on the display in front of him within 2 minutes. When the English letter X appears on the screen, the subject needs to respond by tapping the keyboard in the shortest possible time. When other English letters except X appear on the screen, the subject does not need to tap the keyboard, and when the letter X appears on the screen after the letter A, the subject needs to inhibit the response to this X and does not need to tap the keyboard. The task is designed in this way to make the subjects stay as focused as possible during this stage, inducing a significant change in the level of concentration compared to the resting state. During the entire experiment, the subjects' EEG is continuously collected. A 4-minute experiment (2 minutes in the first stage and 2 minutes in the second stage) is a round. Multiple rounds of experiments can be performed and the final results can be averaged in the subsequent data processing stage to achieve higher estimation performance.

[0083] After obtaining the EEG data of the subjects during the above experiment, the power spectrum density of the EEG of the subjects in the first stage (non-focused state) and the second stage (focused state) of the experiment can be estimated. The power spectrum estimation methods include the Welch method, the Yule-Walker method, etc. The data from the above multiple rounds of experiments can be averaged to improve the accuracy of power spectrum estimation. After obtaining the power spectrum density of the subjects in the non-focused state and the focused state, the difference between the two can be compared, and the change in brain wave energy of each frequency of the subjects after the focused state is induced can be obtained by dividing the power spectrum density of the corresponding frequency. In our preliminary experiments, we obtained the following Figure 5 The image shown reflects the changes in brain wave energy of each frequency after the subject was induced in the focused state. The horizontal axis is the frequency of the brain wave, and the vertical axis is the changes in brain wave energy of the frequency. The part of the curve above 1 indicates that the brain wave energy in this range is higher in the focused state than in the non-focused state, and the part of the curve below 1 indicates that the brain wave energy in this range is lower in the focused state than in the non-focused state. Based on this graph, we can observe the specific range of the rise and fall of brain wave energy in the focused state for each specific subject. Figure 5We can find that the range of brain wave energy decline of this subject in the state of concentration is slightly larger than the α band we generally recognize (the range of α band and β band is Figure 5 The experimental results reflect the validity of the questions we raised and provide a basis for designing a new concentration level indicator that can be adjusted according to the individual condition band range. Figure 5 As a result, for the specific subject, the range of brain wave energy decreases in the focused state is defined as , and the range of brain wave energy increases in the focused state is defined as , then the new concentration level index is.

[0084] In traditional concentration indicators, the energy ratio of the β band (14-30 Hz) and the α band (8-13 Hz) is generally calculated. Figure 5 The results in show that this choice is not optimal. Figure 5 It is the ratio of the power spectral density of the EEG of a subject in two different states: focused and non-focused. It can be seen that the energy of the EEG signal of the subject decreases in the range of 8-16Hz in the focused state, and increases in the range of 16-30Hz. Therefore, for this subject, a more appropriate concentration indicator is the EEG energy in the range of 16-30Hz divided by the EEG energy in the range of 8-16Hz, rather than the traditional energy ratio of the beta band (14-30Hz) to the alpha band (8-13Hz). For other subjects, the specific range of their brain wave energy rise and fall can also be calibrated according to their training data, and a new concentration indicator can be designed based on this, which can be applied to a complete concentration level monitoring and evaluation system.

[0085] Working principle:

[0086] The concentration detection method of the present invention is used to detect and monitor the subject. Specifically, a portable electroencephalogram collector and a portable computer are used, and the computer has a computer program that can implement the concentration detection method of the present invention.

[0087] First, the concentration of students in a classroom is tested in advance. The subject sits in the classroom and wears a portable EEG acquisition device. The invented concentration detection system is used to detect the concentration state of the subject. The subject conducts normal classroom learning behavior. At this time, the EEG acquisition device will collect the EEG signal of the subject in real time and send it to the computer's microprocessor for calculation. Through the concentration detection model of the subject obtained in the aforementioned training stage, the microprocessor analyzes and calculates the EEG signal, and finally outputs the concentration detection index result of the subject to the computer display and memory.

[0088] Then, continue to use the EEG acquisition device to monitor the student's EEG signals, connect the computer to an external alarm, the alarm can use an external speaker, warning light or vibrator to achieve the purpose of alarm, the concentration monitoring system can obtain the subject's current learning status through indicator result analysis and send the signal of the status to the computer microprocessor, if the indicator is lower than a certain threshold value (the threshold value can be adjusted by the teacher), the computer microprocessor sends an alarm signal to the alarm, the teacher can improve the subject's concentration level through external stimulation such as reminders or supervision, or formulate reward and punishment rules based on the overall concentration level of the entire class, to encourage students who listen carefully, thereby improving the learning enthusiasm of all students and achieving higher teaching efficiency.

[0089] The technical method and system of the present invention can be applied not only to classroom concentration monitoring, but also to scenarios such as athlete concentration assessment and dangerous worker concentration assessment.

[0090] The biggest difference between the personnel concentration detection system of the present invention and the traditional system is that it obtains the concentration detection model of the subject through training. The model no longer simply uses the energy ratio within the frequency band of the agreed frequency range to represent the concentration detection index, but takes into account the differences in the specific range of brain wave energy rise and fall between individuals in the state of concentration to determine the concentration detection model. The subject calculates the concentration index result through his own unique concentration detection model, which greatly improves the accuracy of concentration detection.

[0091] The preferred specific embodiments of the present invention are described in detail above. It can be seen from the description of the above implementation mode that those skilled in the art can clearly understand that the present invention can be implemented by means of software plus a necessary general hardware platform. It should be understood that those of ordinary skill in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, all technical solutions that can be obtained by those skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A new method for detecting the concentration level of personnel based on EEG signals, characterized in that: The following steps are involved: Step 1: Collect brain wave signals; Conduct a concentration induction experiment on the subjects, use different experimental tasks to make the subjects produce two states: non-concentration and concentration, and record the subjects' EEG signals in the two states; Step 2: Analyze EEG signals; Calculate the ratio of the power spectral density of the EEG signal in the two states in step 1, that is, P 专注 / P 非专注 According to the ratio, the frequency range of the brain wave energy increase when the subject's EEG signal is induced in the state of concentration is recorded, that is, W 升 , and the frequency range where brain wave energy decreases, namely W 降 ; Step 3: Design the subject concentration index; According to the calculation and recording results of step 2, after obtaining the power spectrum density of the subject in the non-focused state and the focused state, the changes in the brain wave energy of each frequency after the focused state is induced are plotted into a curve graph, wherein the horizontal axis of the curve graph is the frequency of the brain wave, and the vertical axis of the curve graph is the change in the brain wave energy of the frequency; wherein the content of the change in brain wave capacity is the ratio of the power spectrum density in the focused state to the non-focused state, that is, Pfocused / Pnon-focused; the part of the curve higher than 1 indicates that the brain wave energy within this range is higher in the focused state than in the non-focused state, and the part of the curve lower than 1 indicates that the brain wave energy within this range is lower in the focused state than in the non-focused state; Take a curve segment with a horizontal coordinate in the range of 8Hz-30Hz on the curve. The part of the curve segment where the recorded curve is higher than 1 indicates that the subject is in a state of concentration; the part of the curve segment where the recorded curve is lower than 1 indicates that the subject is in a state of non-concentration; the subject's EEG signal has reduced energy in the range of 8-16Hz and increased energy in the range of 16-30Hz when in a state of concentration; the concentration index is the EEG energy in the range of 16-30Hz divided by the EEG energy in the range of 8-16Hz.

2. According to the novel method for detecting the concentration level of a person based on EEG signals according to claim 1, it is characterized in that: Step 4: Apply the unique concentration index to the subjects’ daily tasks; According to the concentration index designed in step 3, it is applied to the conventional concentration monitoring system, and the subjects use the system to monitor their concentration status in daily tasks; After obtaining the EEG data of the subjects, the power spectral density of the EEG of the subjects in the first and second stages of the experiment was estimated. In the first stage of the experiment, the subjects were in a non-focused state; in the second stage, the subjects were in a focused state.

3. According to the novel method for detecting the concentration level of a person based on EEG signals according to claim 2, it is characterized in that: The concentration induction experiment includes a continuous performance test.

4. According to a novel method for detecting the concentration level of a person based on EEG signals according to claim 2, it is characterized in that: The power spectral density calculation method includes the Welch method and the Yule-Walker method.

5. According to a novel method for detecting the concentration level of a person based on EEG signals as described in claim 2, it is characterized in that: The conventional concentration monitoring system includes an EEG acquisition device, a microprocessor for processing and analyzing EEG signals, a power supply device, and an alarm for receiving electrical signals from the microprocessor. The EEG acquisition device and the alarm are electrically connected to the microprocessor respectively, and the power supply device supplies power to the EEG acquisition device, the microprocessor, and the alarm.

6. According to the novel method for detecting the concentration level of a person based on EEG signals according to claim 5, it is characterized in that: The alarm includes a voice module, a warning light module and a vibration module. The alarm is used to receive an alarm electrical signal sent by a microprocessor and trigger an alarm. The alarm electrical signal is obtained by converting an electroencephalogram signal when the monitored person is in a non-concentrated state.

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