An EEG and Deep Learning-based Detection Method for Learning Concentration

Concentration has a significant impact on learning effectiveness, making it crucial to study attention. However, there is little research on the quantitative measurement of learning attention. Electroencephalography (EEG) signals can reflect the brain’s attention during learning; therefore, this paper proposes a learning concentration detection method based on EEG. Firstly, a portable, wearable single-channel EEG acquisition device is used to collect the brain’s EEG signals during the learning process. Secondly, the single-channel EEG signals are converted into images to evaluate learning concentration, thereby transforming the concentration detection problem into an image recognition task. Thirdly, convolutional neural network models—AlexNet, ResNet, and the Visual Geometry Group (VGG) network—are applied to detect the converted images. Finally, an experiment was conducted, and the results show that the detection accuracy rate reaches 93.23%, which proves that the proposed method can effectively evaluate learning concentration.