Cognitive load real-time regulation and control method

Abstract

The invention relates to a cognitive load real-time regulation and control method and device, electronic equipment and a storage medium. The method comprises the following steps: carrying out a preset high cognitive load induction experiment on a preset number of testees, and collecting electroencephalogram data of the testees; establishing a cognitive load monitoring model, identifying a high cognitive load state based on the cognitive load monitoring model, and generating a model identification result; outputting the model identification result to a transcranial electrical stimulation control code; a transcranial electrical stimulation control code is operated after the high cognitive load identification tag is received; regulating and controlling transcranial direct current stimulation nerves based on the transcranial electrical stimulation control code; if the recognition result output of the cognitive load monitoring model is in a low-load state, the transcranial direct current stimulation regulation takes effect. The cognitive load condition can be effectively monitored in real time, real-time regulation and control are performed through transcranial electrical stimulation, the cognitive state is improved, the cognitive load level is controlled, and good operation performance is improved or maintained.

Description

Technical Field

[0001] This disclosure relates to the field of cognitive neuroscience, and more specifically, to a method, apparatus, electronic device, and computer-readable storage medium for real-time regulation of cognitive load. Background Technology

[0002] In the field of human-computer interaction, commonly used methods for cognitive load regulation involve optimizing the human-computer interface based on the task itself, or optimizing the information and complexity associated with the task, thereby reducing or improving the cognitive load of operators or decision-makers. However, this method is slow to take effect and lacks real-time capability. Transcranial Direct Current Stimulation (tDCS) is a non-invasive neuromodulation technique that applies weak direct current to specific areas of the brain by placing electrodes on the scalp. This directly acts on the neuronal cell membranes, altering their excitability and thus regulating brain activity, thereby affecting brain function and behavior to regulate cognitive load. This technique is a direct physical intervention that inputs specific electrical stimulation signals to the brain through external devices, bypassing the visual pathway to directly and actively regulate brain activity. It is highly targeted and immediate, with typically significant and rapid effects. In some studies, tDCS has shown that in improving the cognitive function of patients with depression, symptom relief and cognitive improvement can be observed in a relatively short period.

[0003] Therefore, one or more methods are needed to solve the above problems.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this disclosure is to provide a method, apparatus, electronic device, and computer-readable storage medium for real-time control of cognitive load, thereby overcoming, at least to some extent, one or more problems caused by the limitations and defects of related technologies.

[0006] According to one aspect of this disclosure, a method for real-time regulation of cognitive load is provided, comprising:

[0007] A pre-set high cognitive load induced experiment was conducted on a pre-set number of subjects, and the electroencephalogram (EEG) data of the subjects were collected.

[0008] A cognitive load monitoring model is established, and high cognitive load states are identified based on the cognitive load monitoring model, generating model identification results;

[0009] The model recognition results are output to the transcranial electrical stimulation control code;

[0010] After receiving a high cognitive load identification tag, transcranial electrical stimulation control code is executed;

[0011] The transcranial electrical stimulation control code is used to regulate the transcranial direct current stimulation of nerves;

[0012] If the cognitive load monitoring model outputs a low load state, it indicates that the transcranial direct current stimulation modulation is effective.

[0013] In one exemplary embodiment of this disclosure, the method further includes:

[0014] Ten participants were selected for a high cognitive load induced experiment. The experiment used a simulated flight combat mission in which the participants controlled one aircraft and the enemy controlled three aircraft. The experiment was repeated cyclically, and the participants' electroencephalogram (EEG) data were collected.

[0015] In one exemplary embodiment of this disclosure, the method further includes establishing a cognitive load monitoring model, identifying high cognitive load states based on the cognitive load monitoring model, and generating model identification results.

[0016] Cognitive load comparison experiment and EEG data acquisition;

[0017] The cognitive load level was classified by combining the subjective rating results after the task was collected with the task difficulty.

[0018] EEG signal preprocessing and power spectral density index calculation were performed to obtain the training dataset;

[0019] Utilize machine learning algorithms to train and test training data and models;

[0020] Identification of high cognitive load states.

[0021] In one exemplary embodiment of this disclosure, the EEG signal preprocessing in the method further includes:

[0022] Preprocessing of EEG signals includes rereference, filtering, segmentation, interpolation of bad leads, removal of bad segments, and artifact removal through independent component analysis.

[0023] In one exemplary embodiment of this disclosure, the calculation of the EEG signal power spectral density index in the method further includes:

[0024] Calculate the power spectral density at different frequency bands within the trial period;

[0025] The power spectral density of the four frequency bands was averaged for all trials.

[0026] All collected leads were divided into a preset number of brain regions according to brain region division rules;

[0027] The power spectral density of the four frequency bands in all leads within each brain region is averaged to obtain the required power spectral density index.

[0028] The extracted features are standardized or normalized using the Min-Max normalization method.

[0029] In one exemplary embodiment of this disclosure, the method for calculating the power spectral density of different frequency bands within a trial further includes:

[0030] Divide a signal of length N into L segments, then each segment has a length of M, i.e., N = LM. After adding a Hamming window w(n) to each data window, perform a Fourier transform and calculate its power spectrum, as shown in the following formula:

[0031]

[0032] Calculate the power spectral density (PSD) value for each data segment, and then average the calculated PSD values. Afterwards, the power spectral density of the signal was estimated for each epoch of the full-lead signal using the Welch method, and the average values ​​were taken for the four frequency bands (delta band 1-4Hz, theta band 4-8Hz, alpha band 8-13Hz, beta band 13-30Hz).

[0033] In one exemplary embodiment of this disclosure, the method further includes modulating transcranial direct current stimulation of nerves based on the transcranial electrical stimulation control code:

[0034] The brain region stimulated was the orbitofrontal region of the superior frontal gyrus, with the stimulation electrodes arranged in a manner with the anode at the center and the FP1 cathode surrounding it. During stimulation, the current rose from 0 to 1.5 mA over a period of 15 seconds, was maintained at 1.5 mA for 15 minutes, and then gradually decreased to 0 over a period of 15 seconds.

[0035] In one aspect of this disclosure, a real-time cognitive load control device is provided, comprising:

[0036] The EEG data acquisition module is used to conduct a preset high cognitive load induced experiment on a preset number of subjects and collect the EEG data of the subjects.

[0037] The monitoring model building module is used to build a cognitive load monitoring model, identify high cognitive load states based on the cognitive load monitoring model, and generate model identification results.

[0038] The recognition result output module is used to output the model recognition result to the transcranial electrical stimulation control code;

[0039] The control code execution module is used to run transcranial electrical stimulation control codes after receiving high cognitive load identification tags.

[0040] A nerve stimulation modulation module is used to modulate the transcranial direct current stimulation of nerves based on the transcranial electrical stimulation control code;

[0041] The model result recognition module is used to indicate that the transcranial direct current stimulation modulation is effective if the cognitive load monitoring model outputs a low load state.

[0042] In one aspect of this disclosure, an electronic device is provided, comprising:

[0043] Processor; and

[0044] A memory storing computer-readable instructions that, when executed by the processor, implement the method according to any one of the preceding claims.

[0045] In one aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method according to any one of the preceding claims.

[0046] An exemplary embodiment of this disclosure provides a method for real-time regulation of cognitive load. The method includes: conducting a pre-set high cognitive load induced experiment on a pre-set number of subjects and collecting electroencephalogram (EEG) data from the subjects; establishing a cognitive load monitoring model and identifying high cognitive load states based on the cognitive load monitoring model, generating a model identification result; outputting the model identification result to transcranial electrical stimulation (TES) control code; running the TES control code after receiving a high cognitive load identification tag; regulating transcranial direct current (DC) stimulation of nerves based on the TES control code; if the cognitive load monitoring model identification result outputs a low load state, it indicates that the TES regulation is effective. This disclosure can effectively monitor cognitive load in real time and regulate it via transcranial electrical stimulation, improving cognitive state, controlling cognitive load levels, and improving or maintaining good work performance.

[0047] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0048] The above and other features and advantages of this disclosure will become more apparent from the detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0049] Figure 1 A flowchart of a real-time cognitive load control method according to an exemplary embodiment of the present disclosure is shown;

[0050] Figure 2A flowchart illustrating real-time cognitive load control method according to an exemplary embodiment of the present disclosure is shown.

[0051] Figure 3 A flowchart illustrating the construction of a cognitive load monitoring model for a real-time cognitive load regulation method according to an exemplary embodiment of the present disclosure is shown.

[0052] Figure 4 A flowchart illustrating the EEG signal preprocessing and feature extraction process of a real-time cognitive load modulation method according to an exemplary embodiment of the present disclosure is shown.

[0053] Figure 5 A schematic block diagram of a real-time cognitive load control device according to an exemplary embodiment of the present disclosure is shown;

[0054] Figure 6 A block diagram of an electronic device according to an exemplary embodiment of the present disclosure is shown schematically;

[0055] Figure 7 The illustration shows a schematic diagram of a computer-readable storage medium according to an exemplary embodiment of the present disclosure. Detailed Implementation

[0056] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted.

[0057] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of the specific details described, or other methods, components, materials, apparatuses, steps, etc., can be employed. In other instances, well-known structures, methods, apparatuses, implementations, materials, or operations are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0058] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, or in one or more software-hardened modules, or in different network and / or processor devices and / or microcontroller devices.

[0059] In this example embodiment, a method for real-time adjustment of cognitive load is first provided; refer to Figure 1 As shown, this method for real-time regulation of cognitive load may include the following steps:

[0060] Step S110: Conduct a pre-set high cognitive load induced experiment on a pre-set number of subjects and collect the electroencephalogram (EEG) data of the subjects;

[0061] Step S120: Establish a cognitive load monitoring model, identify high cognitive load states based on the cognitive load monitoring model, and generate model identification results;

[0062] Step S130: Output the model recognition result to the transcranial electrical stimulation control code;

[0063] Step S140: After receiving the high cognitive load identification tag, run the transcranial electrical stimulation control code;

[0064] Step S150: Modulate the transcranial direct current stimulation of the nerve based on the transcranial electrical stimulation control code;

[0065] Step S160: If the cognitive load monitoring model outputs a low load state, it indicates that the transcranial direct current stimulation modulation is effective.

[0066] An exemplary embodiment of this disclosure provides a method for real-time regulation of cognitive load. The method includes: conducting a pre-set high cognitive load induced experiment on a pre-set number of subjects and collecting electroencephalogram (EEG) data from the subjects; establishing a cognitive load monitoring model and identifying high cognitive load states based on the cognitive load monitoring model, generating a model identification result; outputting the model identification result to transcranial electrical stimulation (TES) control code; running the TES control code after receiving a high cognitive load identification tag; regulating transcranial direct current (DC) stimulation of nerves based on the TES control code; if the cognitive load monitoring model identification result outputs a low load state, it indicates that the TES regulation is effective. This disclosure can effectively monitor cognitive load in real time and regulate it via transcranial electrical stimulation, improving cognitive state, controlling cognitive load levels, and improving or maintaining good work performance.

[0067] The following will further explain a method for real-time control of cognitive load in this example embodiment.

[0068] Example 1:

[0069] In step S110, a preset high cognitive load induced experiment can be conducted on a preset number of subjects, and the electroencephalogram (EEG) data of the subjects can be collected.

[0070] In this example embodiment, the method further includes:

[0071] Ten participants were selected for a high cognitive load induced experiment. The experiment used a simulated flight combat mission in which the participants controlled one aircraft and the enemy controlled three aircraft. The experiment was repeated cyclically, and the participants' electroencephalogram (EEG) data were collected.

[0072] In step S120, a cognitive load monitoring model can be established, and a high cognitive load state can be identified based on the cognitive load monitoring model to generate model identification results.

[0073] In this example embodiment, the method further includes establishing a cognitive load monitoring model, identifying high cognitive load states based on the cognitive load monitoring model, and generating model identification results.

[0074] Cognitive load comparison experiment and EEG data acquisition;

[0075] The cognitive load level was classified by combining the subjective rating results after the task was collected with the task difficulty.

[0076] EEG signal preprocessing and power spectral density index calculation were performed to obtain the training dataset;

[0077] Utilize machine learning algorithms to train and test training data and models;

[0078] Identification of high cognitive load states.

[0079] In this example embodiment, the EEG signal preprocessing in the method further includes:

[0080] Preprocessing of EEG signals includes rereference, filtering, segmentation, interpolation of bad leads, removal of bad segments, and artifact removal through independent component analysis.

[0081] In this example embodiment, the calculation of the EEG signal power spectral density index in the method further includes:

[0082] Calculate the power spectral density at different frequency bands within the trial period;

[0083] The power spectral density of the four frequency bands was averaged for all trials.

[0084] All collected leads were divided into a preset number of brain regions according to brain region division rules;

[0085] The power spectral density of the four frequency bands in all leads within each brain region is averaged to obtain the required power spectral density index.

[0086] The extracted features are standardized or normalized using the Min-Max normalization method.

[0087] In this example embodiment, the method for calculating the power spectral density of different frequency bands within a trial further includes:

[0088] Divide a signal of length N into L segments, then each segment has a length of M, i.e., N = LM. After adding a Hamming window w(n) to each data window, perform a Fourier transform and calculate its power spectrum, as shown in the following formula:

[0089]

[0090] Calculate the power spectral density (PSD) value for each data segment, and then average the calculated PSD values. Afterwards, the power spectral density of the signal was estimated for each epoch of the full-lead signal using the Welch method, and the average values ​​were taken for the four frequency bands (delta band 1-4Hz, theta band 4-8Hz, alpha band 8-13Hz, beta band 13-30Hz).

[0091] In step S130, the model recognition result can be output to the transcranial electrical stimulation control code.

[0092] In step S140, transcranial electrical stimulation control code can be run after accepting a high cognitive load identification tag.

[0093] In step S150, the transcranial direct current stimulation of the nerve can be modulated based on the transcranial electrical stimulation control code.

[0094] In this example embodiment, the method of modulating transcranial direct current stimulation of nerves based on the transcranial electrical stimulation control code further includes:

[0095] The brain region stimulated was the orbitofrontal region of the superior frontal gyrus, with the stimulation electrodes arranged in a manner with the anode at the center and the FP1 cathode surrounding it. During stimulation, the current rose from 0 to 1.5 mA over a period of 15 seconds, was maintained at 1.5 mA for 15 minutes, and then gradually decreased to 0 over a period of 15 seconds.

[0096] In step S160, if the cognitive load monitoring model outputs a low load state, it indicates that the transcranial direct current stimulation modulation is effective.

[0097] In the embodiments of this example, the real-time cognitive load regulation method of this disclosure has the following significant advantages. First, the method utilizes EEG characteristic indicators under high and low load states for machine learning modeling training, which can accurately monitor and identify different levels of cognitive load states, and promptly detect and regulate high cognitive load states. Second, the method employs transcranial electrical stimulation technology to timely and effectively regulate high cognitive load, reduce load levels, and improve or maintain task performance.

[0098] Example 2:

[0099] In this example embodiment, see Figure 2The flowchart illustrates a real-time cognitive load control method provided by an embodiment of the present invention, which is described in detail below:

[0100] (1) High cognitive load induced experiment and EEG data acquisition

[0101] Ten participants were selected for a high cognitive load induced experiment. A 1V3 (participant controls one aircraft, opponent controls three) simulated flight combat mission was used as the experimental paradigm, repeated cyclically. During the experiment, a professional 64-channel EEG acquisition device was used to collect the participants' task-state EEG data. The EEG electrodes were arranged according to the 10-20 international system, and all electrode impedances were maintained below 5kΩ. The collected EEG data was used for cognitive load identification and classification.

[0102] (2) The cognitive load monitoring model identifies high cognitive load states. The construction process of the cognitive load monitoring model is as follows: Figure 3 As shown:

[0103] In this example embodiment, the cognitive load comparison experiment and EEG data acquisition: 20 subjects were selected and simulated flight combat tasks were conducted in 1V1 (subject controls 1 aircraft, opponent controls 1 aircraft) and 1V3 (subject controls 1 aircraft, opponent controls 3 aircraft) as comparative experimental paradigms. During the experiment, a professional 64-channel EEG acquisition device was used to collect the subjects' task-state EEG data. The EEG electrodes were arranged according to the 10-20 international system, and the impedance of all electrodes was kept below 5kΩ. Each experiment was conducted for 6 rounds to obtain enough EEG data for model training.

[0104] In this example embodiment, the cognitive load level is classified by combining the subjective rating results collected after the task with the task difficulty: After each flight round, the subjects need to complete the Cognitive Load Scale (NASA-TXL) to subjectively evaluate the cognitive load level of this flight round. This scale is out of 10 points. A score greater than 6 points represents a high cognitive load state, and a score less than 4 points represents a low cognitive load state. Based on the scale rating results and combined with the task difficulty, that is, in the 1V1 experiment, a score less than 4 points indicates a low cognitive load state, and in the 1V3 experiment, a score greater than 6 points indicates a high cognitive load state.

[0105] In this example embodiment, EEG signal preprocessing and power spectral density index calculation are performed to obtain a training dataset, such as... Figure 4 As shown.

[0106] The preprocessing steps are as follows:

[0107] 1) Re-reference: Use the whole brain average reference;

[0108] 2) Filtering: Select a fourth-order Butterworth digital bandpass filter with a bandpass of 1-30Hz based on the main frequency bands related to cognitive load to filter out high and low frequency noise;

[0109] 3) Segmentation: Select 1000ms as the segment duration and segment the collected EEG signals;

[0110] 4) Interpolation errors: Interpolation replacement is performed on lead data that are significantly different from the EEG data of other electrodes;

[0111] 5) Remove bad segments: Examine all EEG data from all trials and remove trials with large artifacts or abnormal data distribution.

[0112] 6) Independent Component Analysis (ICA) artifact removal: Using independent component analysis, non-EEG components such as ECG, eye movement, blinking, electromyography, and head movement are identified and removed to obtain clean EEG data.

[0113] The calculation method for the power spectral density index is as follows:

[0114] 1) Calculate the power spectral density of different frequency bands within the trial: Divide the signal of length N into L segments, then the length of each segment is M, i.e., N = LM. Add a Hamming window w(n) to each data window and perform a Fourier transform to calculate its power spectrum, as shown in the following formula:

[0115]

[0116] Calculate the power spectral density (PSD) value for each data segment, and then average the calculated PSD values. Afterwards, the power spectral density of the signal was estimated for each epoch of the full-lead signal using the Welch method, and the average values ​​were taken for the four frequency bands (delta band 1-4Hz, theta band 4-8Hz, alpha band 8-13Hz, beta band 13-30Hz).

[0117] 2) Intra-trial averaging: The power spectral density of the four frequency bands is averaged for all trials;

[0118] 3) Brain region division: All collected leads were divided into 10 brain regions according to brain region division rules: frontal lobe (F1-F8, Fz); frontocentral region (FC1-FC6, FCz); central region (C1-C6, Cz); central parietal region (CP1-CP6, CPz); parietal lobe (P1-P8, Pz); parieto-occipital region (PO1-PO6, POz); occipital lobe (O1, O2, Oz); temporal lobe (T7, T8); frontotemporal region (FT7, FT8); and temporoparietal region (TP7, TP8).

[0119] 4) Obtain the average power spectral density of different frequency bands in each brain region: Average the power spectral density of the four frequency bands in all leads in each brain region to obtain the required power spectral density index.

[0120] 5) Normalization: The extracted features are normalized or standardized using the Min-Max normalization method to make them have similar scales and avoid certain features from dominating the model training due to their excessively large numerical range.

[0121] In this example embodiment, machine learning algorithms are used to train and test the training data and the model: high cognitive load and low cognitive load are used as two labels, and normalized features are used as input to construct a classification model (such as support vector machine, decision tree, neural network, etc.). The model is trained using a training set:test set ratio of 7:3, and cross-validation is used to improve the model's generalization performance and reliability. After multiple rounds of iterative training, a stable and reliable model is finally achieved. This model can effectively handle tasks related to the correspondence between power spectral density features and cognitive load levels, ensuring high accuracy and practicality.

[0122] In this example embodiment, the accurate identification of high cognitive load states is achieved by training a cognitive load monitoring and identification model, which can effectively and accurately detect high cognitive load states.

[0123] (3) Output the model recognition results to the transcranial electrical stimulation control code: Output the result label (high cognitive load: 1) to the transcranial electrical stimulation neuromodulation terminal.

[0124] (4) After receiving the high cognitive load identification tag, the transcranial electrical stimulation control code starts running: Connect the control terminal to the transcranial DC stimulation device with a control line, edit the cognitive load regulation stimulation template in the transcranial electrical stimulation software, set the stimulation position, stimulation current intensity and stimulation time and other parameters, edit the electrode impedance detection and stimulation template operation control code through MATLAB software, and connect the control terminal to the model monitoring terminal through a parallel port. When the cognitive load monitoring model identification result is a high cognitive load state, the output tag 1 is transmitted to the control terminal through the parallel port. After receiving tag 1, the control terminal starts running the stimulation model operation control code, the transcranial DC stimulation template is activated, and electrical stimulation regulation begins.

[0125] (5) Transcranial direct current stimulation neuromodulation: The brain region to be stimulated was the orbitofrontal region of the superior frontal gyrus. The stimulation electrodes were arranged in a ring around the anode (FP1) and the cathodes (FPz, AF3, 1 cm above the left brow bone, and equidistant from FP1-FPz on the left side). During stimulation, the current rose from 0 to 1.5 mA over a period of 15 seconds, was maintained at 1.5 mA for 15 minutes, and then gradually decreased to 0 over a period of 15 seconds. A fade-in / fade-out design (15 seconds each) was used at the beginning and end of stimulation to reduce potential sudden changes in skin sensation.

[0126] (6) Transcranial direct current stimulation takes effect and the cognitive load monitoring model outputs a low load state: The cognitive monitoring model continuously monitors the person's EEG data. When the monitoring result is low load, it indicates that the transcranial direct current stimulation is effective.

[0127] It should be noted that although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0128] Furthermore, in this example embodiment, a real-time cognitive load control device is also provided. (Refer to...) Figure 5 As shown, the real-time cognitive load control device 200 may include: an EEG data acquisition module 210, a monitoring model establishment module 220, a recognition result output module 230, a control code execution module 240, a stimulation neuromodulation module 250, and a model result recognition module 260. Wherein:

[0129] The EEG data acquisition module 210 is used to conduct a preset high cognitive load induced experiment on a preset number of subjects and collect the EEG data of the subjects.

[0130] The monitoring model establishment module 220 is used to establish a cognitive load monitoring model, identify high cognitive load states based on the cognitive load monitoring model, and generate model identification results.

[0131] The recognition result output module 230 is used to output the model recognition result to the transcranial electrical stimulation control code;

[0132] The control code execution module 240 is used to run transcranial electrical stimulation control code after receiving a high cognitive load identification tag.

[0133] The nerve stimulation modulation module 250 is used to regulate the transcranial direct current stimulation of nerves based on the transcranial electrical stimulation control code;

[0134] The model result recognition module 260 is used to indicate that transcranial direct current stimulation (DCS) is effective if the cognitive load monitoring model outputs a low-load state.

[0135] The specific details of each of the above-mentioned cognitive load real-time control device modules have been described in detail in the corresponding cognitive load real-time control method, so they will not be repeated here.

[0136] It should be noted that although several modules or units of a real-time cognitive load control device 200 have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0137] Furthermore, in an exemplary embodiment of this disclosure, an electronic device capable of implementing the above-described method is also provided.

[0138] Those skilled in the art will understand that various aspects of the present invention can be implemented as systems, methods, or program products. Therefore, various aspects of the present invention can be specifically implemented as entirely hardware embodiments, entirely software embodiments (including firmware, microcode, etc.), or embodiments combining hardware and software aspects, collectively referred to herein as “circuit,” “module,” or “system.”

[0139] The following reference Figure 6 To describe an electronic device 500 according to such an embodiment of the present invention. Figure 6 The electronic device 500 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0140] like Figure 6 As shown, the electronic device 500 is manifested in the form of a general-purpose computing device. The components of the electronic device 500 may include, but are not limited to: at least one processing unit 510, at least one storage unit 520, a bus 530 connecting different system components (including storage unit 520 and processing unit 510), and a display unit 540.

[0141] The storage unit stores program code that can be executed by the processing unit 510, causing the processing unit 510 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 510 can perform actions such as... Figure 1 Steps S110 to S160 are shown in the figure.

[0142] Storage unit 520 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 5201 and / or cache memory 5202, and may further include a read-only memory (ROM) 5203.

[0143] Storage unit 520 may also include a program / utility 5204 having a set (at least one) program module 5205, such program module 5205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0144] Bus 530 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0145] Electronic device 500 can also communicate with one or more external devices 570 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 500, and / or with any device that enables electronic device 500 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 550. Furthermore, electronic device 500 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 560. As shown, network adapter 560 communicates with other modules of electronic device 500 via bus 530. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0146] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0147] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible embodiments, various aspects of the invention may also be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of the invention described in the "Exemplary Methods" section above.

[0148] refer to Figure 7 As shown, a program product 600 for implementing the above-described method according to an embodiment of the present invention is described. It may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, the readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0149] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0150] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0151] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0152] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0153] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0154] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

[0155] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for real-time control of cognitive load, characterized in that, The method includes: A pre-set high cognitive load induced experiment was conducted on a pre-set number of subjects, and the electroencephalogram (EEG) data of the subjects were collected. A cognitive load monitoring model is established, and high cognitive load states are identified based on the cognitive load monitoring model, generating model identification results; The model recognition results are output to the transcranial electrical stimulation control code; After receiving a high cognitive load identification tag, transcranial electrical stimulation control code is executed; The transcranial electrical stimulation control code is used to regulate the transcranial direct current stimulation of nerves; If the cognitive load monitoring model outputs a low load state, it indicates that the transcranial direct current stimulation modulation is effective.

2. The method as described in claim 1, characterized in that, The method further includes: Ten participants were selected for a high cognitive load induced experiment. The experiment used a simulated flight combat mission in which the participants controlled one aircraft and the enemy controlled three aircraft. The experiment was repeated cyclically, and the participants' electroencephalogram (EEG) data were collected.

3. The method as described in claim 1, characterized in that, The method establishes a cognitive load monitoring model, identifies high cognitive load states based on the cognitive load monitoring model, and generates model identification results, which also includes: Cognitive load comparison experiment and EEG data acquisition; The cognitive load level was classified by combining the subjective rating results after the task was collected with the task difficulty. EEG signal preprocessing and power spectral density index calculation were performed to obtain the training dataset; Utilize machine learning algorithms to train and test training data and models; Identification of high cognitive load states.

4. The method as described in claim 3, characterized in that, The electroencephalogram (EEG) signal preprocessing in the method further includes: Preprocessing of EEG signals includes rereference, filtering, segmentation, interpolation of bad leads, removal of bad segments, and artifact removal through independent component analysis.

5. The method as described in claim 3, characterized in that, The calculation of the EEG signal power spectral density index in the method also includes: Calculate the power spectral density at different frequency bands within the trial period; The power spectral density of the four frequency bands was averaged for all trials. All collected leads were divided into a preset number of brain regions according to brain region division rules; The power spectral density of the four frequency bands in all leads within each brain region is averaged to obtain the required power spectral density index. The extracted features are standardized or normalized using the Min-Max normalization method.

6. The method as described in claim 5, characterized in that, The method for calculating the power spectral density of different frequency bands within a trial also includes: Divide a signal of length N into L segments, then each segment has a length of M, i.e., N = LM. After adding a Hamming window w(n) to each data window, perform a Fourier transform and calculate its power spectrum, as shown in the following formula: Calculate the power spectral density (PSD) value for each data segment, and then average the calculated PSD values. Afterwards, the power spectral density of the signal was estimated for each epoch of the full-lead signal using the Welch method, and the average values ​​were taken for the four frequency bands (delta band 1-4Hz, theta band 4-8Hz, alpha band 8-13Hz, beta band 13-30Hz).

7. The method as described in claim 1, characterized in that, The method further includes regulating transcranial direct current stimulation of nerves based on the transcranial electrical stimulation control code: The brain region to be stimulated was the orbitofrontal region of the superior frontal gyrus. The stimulation electrodes were arranged with the anode at the center and the cathode (FP1) surrounding it. During stimulation, the current rose from 0 to 1.5 mA over a period of 15 seconds, was maintained at 1.5 mA for 15 minutes, and then gradually decreased to 0 over a period of 15 seconds.

8. A real-time cognitive load control device, characterized in that, The device includes: The EEG data acquisition module is used to conduct a preset high cognitive load induced experiment on a preset number of subjects and collect the EEG data of the subjects. The monitoring model building module is used to build a cognitive load monitoring model, identify high cognitive load states based on the cognitive load monitoring model, and generate model identification results. The recognition result output module is used to output the model recognition result to the transcranial electrical stimulation control code; The control code execution module is used to run transcranial electrical stimulation control codes after receiving high cognitive load identification tags. A nerve stimulation modulation module is used to modulate the transcranial direct current stimulation of nerves based on the transcranial electrical stimulation control code; The model result recognition module is used to indicate that the transcranial direct current stimulation modulation is effective if the cognitive load monitoring model outputs a low load state.

9. An electronic device, characterized in that, include Processor; and A memory storing computer-readable instructions that, when executed by the processor, implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the method according to any one of claims 1 to 7.

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