Building indoor learning efficiency detection method and system
A technology of efficiency detection and construction, applied in medical science, psychological devices, diagnostic records/measurement, etc., can solve problems such as inability to accurately reflect the state of learners' attention, and achieve the effect of accurate classification
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Embodiment 1
[0093] Such as figure 1 and image 3 , figure 1 It is a flow block diagram of Embodiment 1 of the present invention, image 3 It is a system block diagram of Embodiment 1 of the present invention; a method for detecting learning efficiency in a building, comprising the following steps;
[0094] S1. Acquiring EEG signal data;
[0095] In this embodiment, the way to obtain the EEG signal data is to obtain the EEG signal acquisition experiment through the attention classification;
[0096] Concretely, described attention classification EEG signal collection experiment comprises the following steps;
[0097] S11. Acquiring the experimental online learning video;
[0098] The online learning video can be intercepted from 15 10-minute videos of Chinese University MOOC, ensuring that all students study in the same building room, and select 15 subjects respectively, and the selected content of each subject is relatively complete and interesting Not the same, this can better refl...
Embodiment 2
[0178] Such as Figure 4 , Figure 4 It is a schematic structural diagram of Embodiment 2 of the present invention; a detection system based on the detection method for indoor learning efficiency in a building, characterized in that it includes;
[0179] An acquisition module, configured to acquire EEG signal data;
[0180] A preprocessing module, configured to preprocess the collected EEG signal data;
[0181] The extraction module is used to extract the energy features under the different electrode channels of the EEG signal as the input features of the subsequent classifier through feature extraction;
[0182] The input module is used to use the energy feature under the corresponding electrode channel of the feature extraction as the input feature, and input it into the BP neural network to realize the classification of learning attention.
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