Attention characteristic identification method, and application

Abstract

The invention discloses an attention characteristic identification method, and is provided with an attention normal form model. The invention is characterized in that the attention normal form model comprises attention focusing periods and interval rest periods, wherein the attention focusing periods and the interval rest periods are alternately arranged; and each attention focusing period is thata user is under an attention paying state so as to train the attention of the user, and each interval rest period is that the user is under a rest state. Compared with the prior art, the attention characteristic identification method has the following beneficial technical effects of: firstly, due to the staggered arrangement of the attention paying states and the rest states constructed by the attention normal form model, the structure can provide break time for the user and also can assist in training to improve the ability of the user for entering the attention paying state from a noisy state and the rest state; and in addition, the system can distinguish data on a time axis in virtue of a circulation structure so as to be convenient in implementing a data comparison mechanism and discovering data under a non-attention-paying state as much as possible so as to improve judgment for the attention paying state.

Description

technical field [0001] The invention belongs to the technical field of human-computer interaction, and in particular relates to an attention feature recognition method. That is to use the method of picking up the user's EEG signal to express the user's attention level, and use the expressed user's attention level to implement control and management, such as monitoring the user's attention training process and displaying feedback to improve the training effect and training. Management effects, or control of control behaviors such as oculoelectricity, etc. Electroencephalogram (Electroencephalograph, EEG) accompanies us all the time in our lives. It is the overall response of the spontaneous and rhythmic electrical activity of brain cell groups in the cerebral cortex and scalp, which can be detected by electrodes placed on the scalp. EEG can be divided into four rhythmic waves of δ, θ, α, and β according to different frequencies. Many foreign scholars and experts have found th...

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Problems solved by technology

Further research shows that the test method defined in the above patent has the following problems: First, its main function is to test and feedback EEG signals, but it does not further disclose the EEG signal sampling steps and the relationship between sampling system equipment and improving training capabilities What kind of internal correlation and logic exists, but think that the training ability can be improved through external information feedback; second, the signal sampling process will be interfered by a lot of noise, so the sampling data may not be completely accurate, although the existing technology The noise filtering mechanism is used but the data screening mechanism is lacking; third, during the entire training process, the data obtained by online detection is presented continuously on the time axis, which includes the data of the highly concentrated stage and the attention The data in the stage of concentration is not particularly concentrated or even in the stage of rest. For this reason, only the noise filtering mechanism lacks a screening mechanism and obviously cannot truly reflect the relevant data related to the training of attention concentration, and it cannot be used to improve attention or use Attention level information provides objective and accurate data

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