CN112957049A - Attention state monitoring device and method based on brain-computer interface equipment technology - Google Patents
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Abstract
The invention discloses an attention state monitoring device and method based on a brain-computer interface device technology.A attention monitoring task module is used for a user to register an account, a plurality of tasks are selected from a task library according to the use condition of the user to the tasks to construct an attention monitoring task, and each task in the attention monitoring task is sequentially displayed for the user to complete; the brain-computer interface equipment is used for collecting EEG signals of the user in the process of completing each task in real time, filtering, amplifying and carrying out analog-to-digital conversion on the EEG signals to obtain EEG digital signals, and sending the EEG digital signals to the attention monitoring task module; the attention monitoring task module is used for extracting time-frequency domain features of the transmitted EEG digital signals, and inputting a one-dimensional feature matrix formed by the time-frequency domain features into the deep learning algorithm model for analysis so as to obtain an attention state evaluation result.
Description
Technical Field
The invention relates to the technical field of attention state monitoring, in particular to an attention state monitoring device and method based on a brain-computer interface device technology.
Background
Brain-computer interface equipment: a brain-computer interface device is a device that enables a direct connection path between the human or animal brain (or a culture of brain cells) and the outside world, including two parts, a collection device at the brain and an external device that reads, analyzes and presents signals externally.
EEG signals: electroencephalography is a general reflection of electrophysiological activity of brain neurons on the surface of the cerebral cortex or scalp. The electroencephalogram signals contain a large amount of physiological and disease information, such as sleep states, attention concentration and the like. In the context of sleep, EEG is an important tool for assessing sleep quality and studying sleep processes.
Attention state: the attention state is the directing and concentrating condition of five information channels of vision, hearing, touch, smell and taste to objective things. Attention has two basic features, one being directional, meaning that mental activities selectively reflect some phenomena away from the rest of the subject. Second, concentration, refers to the intensity or tension of mental activities staying on the selected object.
Existing means and devices for evaluating attentional status:
(1) the activity recorder evaluates the degree of attention concentration, i.e., 2D and 3D features obtained using the Kinect One sensor, which characterize the facial and body characteristics of the subject, including expression and body posture, to estimate the level and variation of the subject's attentional state.
(2) The attention is evaluated based on a drowsiness warning system, namely the pupil condition and the head activity are scanned by a radar, and the fatigue state of the driver is judged through data analysis.
(3) The attention assessment method based on the SSVEP has the principle that the vision of a subject is stimulated by displaying a test image, the brain waves of the subject are collected, and the SSVEP in the brain waves is extracted, so that the purpose of analyzing the attention state is achieved.
The disadvantage of the above-mentioned actigraph-based attention state monitoring is that it is not possible to make a decision about the attention state on the basis of comprehensive information, but it is only possible to infer the attention state of a person indirectly on the basis of signs and external behavioral changes.
The attention evaluating system aiming at the drowsiness early warning system has the defect that the monitoring function is limited in space. When the head part leaves the radar scanning, or when the detected person wears an external article to cause an obstacle to the radar scanning, the radar scanning cannot pay attention to the detected person.
The attention assessment method based on the SSVEP has the defect that the feature extraction and analysis of data acquired by electroencephalogram signals are not comprehensive enough, so that information of other frequencies cannot be combined in the analysis process.
Disclosure of Invention
Aiming at the problems and the defects in the prior art, the invention provides a novel attention state monitoring device and method based on a brain-computer interface device technology.
The invention solves the technical problems through the following technical scheme:
the invention provides an attention state monitoring device based on brain-computer interface equipment technology, which is characterized by comprising brain-computer interface equipment and attention monitoring task equipment, wherein the brain-computer interface equipment is worn on the head of a user;
the attention monitoring task module is used for a user to register an account, a plurality of tasks are selected from a task library according to the use condition of the user to the tasks to construct an attention monitoring task, and each task in the attention monitoring task is displayed in sequence for the user to complete;
the brain-computer interface equipment is used for collecting EEG signals of the user in the process of completing each task in real time, carrying out filtering processing, signal amplification and analog-to-digital conversion on the EEG signals to obtain EEG digital signals, and sending the EEG digital signals to the attention monitoring task module;
the attention monitoring task module is used for extracting time-frequency domain features of transmitted EEG digital signals, inputting a one-dimensional feature matrix formed by the time-frequency domain features into a deep learning algorithm model for analysis to obtain an attention state evaluation result, wherein the attention state evaluation result is divided into 0-10 levels and represents the attention state degrees of 0%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% and 100%, 0% is unconsciousness, and 100% is over-concentration.
Preferably, the attention monitoring task module is configured to issue an early warning to the user when, during the monitoring process, the change in the acquired EEG signal indicates that the degree of attention is lower than the degree required by the task to be performed.
Preferably, the deep learning algorithm model adopts a convolutional neural network model.
The invention also provides an attention state monitoring method based on the brain-computer interface device technology, which is characterized by comprising the following steps of:
s1, the attention monitoring task module is used for a user to register an account, a plurality of tasks are selected from the task library according to the use condition of the user to the tasks to construct the attention monitoring tasks, and each task in the attention monitoring tasks is displayed in sequence for the user to complete;
s2, the brain-computer interface equipment collects EEG signals of the user in the process of completing each task in real time, and the EEG signals are subjected to filtering processing, signal amplification and analog-to-digital conversion to obtain EEG digital signals, and the EEG digital signals are sent to the attention monitoring task module;
s3, the attention monitoring task module extracts time-frequency domain features of the transmitted EEG digital signals, a one-dimensional feature matrix formed by the time-frequency domain features is input into a deep learning algorithm model to be analyzed to obtain an attention state evaluation result, the attention state evaluation result is divided into 0-10 levels and respectively represents the attention state degrees of 0%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% and 100%, wherein 0% is unconscious, and 100% is over-concentration.
Preferably, the attention monitoring task module is configured to issue an early warning to the user when, during the monitoring process, the change in the acquired EEG signal indicates that the degree of attention is lower than the degree required by the task to be performed.
Preferably, the deep learning algorithm model adopts a convolutional neural network model.
On the basis of the common knowledge in the field, the above preferred conditions can be combined randomly to obtain the preferred embodiments of the invention.
The positive progress effects of the invention are as follows:
the invention directly extracts information from the thinking of human brain based on EEG signals collected by a specially-made brain-computer interface device, and utilizes a deep learning algorithm to evaluate the attention state; the method only needs to wear the brain-computer interface equipment to carry out resting EEG acquisition, can finish evaluation without active feedback of a tested person, is light and efficient, and is easy for large-scale popularization.
Drawings
Fig. 1 is a front view of a brain-computer interface device in accordance with a preferred embodiment of the present invention.
Fig. 2 is a top view of a brain-computer interface device in accordance with a preferred embodiment of the present invention.
FIG. 3 is a software interface diagram of the attention monitoring task module according to the preferred embodiment of the invention.
Fig. 4 is a flowchart of a method for monitoring attention status based on brain-computer interface device technology according to a preferred embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
As shown in fig. 1-2, the present embodiment provides an attention state monitoring apparatus based on brain-computer interface device technology, which includes a brain-computer interface device for being worn on the head of a user and an attention monitoring task device, where the attention monitoring task device includes a task library including various tasks and an attention monitoring task module.
Wherein, brain-computer interface device 1: the electrodes are symmetrically distributed on the forehead leaf or the frontal lobe, and the electrode points are symmetrically distributed left and right; the device mainly comprises a biosensor, a front-end circuit device and the like. The point locations are prefrontal lobe Fp3, Fp1, Fpz, Fp2, Fp4, frontal lobe AF2, AF3, AF4, AF5, earlobe A1 and A2 according to the international 10-20 system. Wherein the ground point is Fpz; the reference points are A1, the average value of A2, and take the form of ear clips. Specifically, referring to fig. 1 and 2, 11 shows a headband, 12 shows a frontal lobe measuring electrode, 13 shows a frontal lobe measuring electrode, 14 shows an electrode fixing assembly, 15 shows a hook and loop fastener, 16 shows a reference electrode ear clip, 17 shows a circuit box, and 18 shows a switch indicator.
Attention monitoring task device: the system mainly comprises attention monitoring task module task software and a task library. The carrier and display of the attention monitoring task module can be a personal computer or a mobile phone, and the tasks of the task library include but are not limited to the following: video, text reading, calculation, mini-games, and simulated driving.
The functions of the components are described in detail below:
the attention monitoring task module is used for a user to register an account, a plurality of tasks are selected from the task library according to the use condition of the user to the tasks to construct attention monitoring tasks, and each task in the attention monitoring tasks is displayed in sequence to be completed by the user.
The brain-computer interface equipment is used for collecting EEG signals of the user in the process of completing each task in real time, filtering, amplifying and carrying out analog-to-digital conversion on the EEG signals to obtain EEG digital signals, and sending the EEG digital signals to the attention monitoring task module.
The attention monitoring task module is used for extracting time-frequency domain features of transmitted EEG digital signals, inputting a one-dimensional feature matrix formed by the time-frequency domain features into a deep learning algorithm model (a convolutional neural network model) for analysis to obtain an attention state evaluation result, wherein the attention state evaluation result is divided into 0-10 levels and respectively represents the attention state degrees of 0%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% and 100%, wherein 0% is unconsciousness, and 100% is over concentration.
In addition, as shown in fig. 3, the attention monitoring task module is configured to issue an early warning to the user when the change of the acquired EEG signal indicates that the degree of attention is lower than the degree required by the task to be performed.
As shown in fig. 4, the present invention further provides an attention state monitoring method based on brain-computer interface device technology, which includes the following steps:
1. the user wears the brain-computer interface equipment to ensure that the electrode position corresponds to the correct scalp point position.
2. The attention monitoring task module is used for a user to register an account, a plurality of tasks are selected from the task library according to the use condition of the user to the tasks to construct attention monitoring tasks, and each task in the attention monitoring tasks is displayed in sequence to be completed by the user.
3. The brain-computer interface equipment collects EEG signals of the user in the process of completing each task in real time, and carries out filtering processing, signal amplification and analog-to-digital conversion on the EEG signals to obtain EEG digital signals, and sends the EEG digital signals to the attention monitoring task module.
4. The attention monitoring task module extracts time-frequency domain features of the transmitted EEG digital signals, a one-dimensional feature matrix formed by the time-frequency domain features is input into a deep learning algorithm model to be analyzed to obtain an attention state evaluation result, the attention state evaluation result is divided into 0-10 levels and represents the attention state degrees of 0%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% and 100%, wherein 0% is unconsciousness, and 100% is over concentration.
5. And the attention monitoring task module sends out early warning to the user when the change of the acquired EEG signal shows that the attention degree is lower than the degree required by the performed task in the monitoring process.
The embodiment realizes the feature collection of the attention state during the task, timely sends out early warning when the attention is reduced, and has the functions of automatically drawing and generating the graphic report after the monitoring is finished, so that a user can more intuitively and easily understand the analysis report.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that these are by way of example only, and that the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the spirit and scope of the invention, and these changes and modifications are within the scope of the invention.
Claims (6)
1. An attention state monitoring device based on brain-computer interface equipment technology is characterized by comprising brain-computer interface equipment and attention monitoring task equipment, wherein the brain-computer interface equipment is worn on the head of a user, and the attention monitoring task equipment comprises a task library containing various tasks and an attention monitoring task module;
the attention monitoring task module is used for a user to register an account, a plurality of tasks are selected from a task library according to the use condition of the user to the tasks to construct an attention monitoring task, and each task in the attention monitoring task is displayed in sequence for the user to complete;
the brain-computer interface equipment is used for collecting EEG signals of the user in the process of completing each task in real time, carrying out filtering processing, signal amplification and analog-to-digital conversion on the EEG signals to obtain EEG digital signals, and sending the EEG digital signals to the attention monitoring task module;
the attention monitoring task module is used for extracting time-frequency domain features of transmitted EEG digital signals, inputting a one-dimensional feature matrix formed by the time-frequency domain features into a deep learning algorithm model for analysis to obtain an attention state evaluation result, wherein the attention state evaluation result is divided into 0-10 levels and represents the attention state degrees of 0%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% and 100%, 0% is unconsciousness, and 100% is over-concentration.
2. The brain-computer interface device technology-based attention state monitoring apparatus of claim 1, wherein said attention monitoring task module is configured to issue an early warning to the user when the change of the collected EEG signals indicates that the degree of attention is lower than required for the task being performed during the monitoring process.
3. The brain-computer interface device technology-based attention state monitoring apparatus of claim 1, wherein the deep learning algorithm model employs a convolutional neural network model.
4. An attention state monitoring method based on brain-computer interface device technology is characterized by comprising the following steps:
s1, the attention monitoring task module is used for a user to register an account, a plurality of tasks are selected from the task library according to the use condition of the user to the tasks to construct the attention monitoring tasks, and each task in the attention monitoring tasks is displayed in sequence for the user to complete;
s2, the brain-computer interface equipment collects EEG signals of the user in the process of completing each task in real time, and the EEG signals are subjected to filtering processing, signal amplification and analog-to-digital conversion to obtain EEG digital signals, and the EEG digital signals are sent to the attention monitoring task module;
s3, the attention monitoring task module extracts time-frequency domain features of the transmitted EEG digital signals, a one-dimensional feature matrix formed by the time-frequency domain features is input into a deep learning algorithm model to be analyzed to obtain an attention state evaluation result, the attention state evaluation result is divided into 0-10 levels and respectively represents the attention state degrees of 0%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% and 100%, wherein 0% is unconscious, and 100% is over-concentration.
5. The method of claim 4, wherein the attention monitoring task module is configured to issue an early warning to the user when the change in the collected EEG signals indicates that the degree of attention is lower than required for the task being performed during the monitoring process.
6. The brain-computer interface device technology-based attention state monitoring method of claim 4, wherein the deep learning algorithm model employs a convolutional neural network model.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114366125A (en) * | 2022-01-27 | 2022-04-19 | 中国人民解放军陆军特色医学中心 | Wearable electroencephalogram system for mammals |
| CN115576464A (en) * | 2022-12-08 | 2023-01-06 | 深圳市心流科技有限公司 | User evaluation method, device, equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108670276A (en) * | 2018-05-29 | 2018-10-19 | 南京邮电大学 | Study attention evaluation system based on EEG signals |
| CN109009171A (en) * | 2018-08-01 | 2018-12-18 | 深圳市心流科技有限公司 | Attention assessment method, system and computer readable storage medium |
| CN111184511A (en) * | 2020-02-04 | 2020-05-22 | 西安交通大学 | Electroencephalogram signal classification method based on attention mechanism and convolutional neural network |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108670276A (en) * | 2018-05-29 | 2018-10-19 | 南京邮电大学 | Study attention evaluation system based on EEG signals |
| CN109009171A (en) * | 2018-08-01 | 2018-12-18 | 深圳市心流科技有限公司 | Attention assessment method, system and computer readable storage medium |
| CN111184511A (en) * | 2020-02-04 | 2020-05-22 | 西安交通大学 | Electroencephalogram signal classification method based on attention mechanism and convolutional neural network |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114366125A (en) * | 2022-01-27 | 2022-04-19 | 中国人民解放军陆军特色医学中心 | Wearable electroencephalogram system for mammals |
| CN115576464A (en) * | 2022-12-08 | 2023-01-06 | 深圳市心流科技有限公司 | User evaluation method, device, equipment and storage medium |
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Application publication date: 20210615 |