Zhang Bingxue, Shi Yang, Hou Longfeng, Yin Zhong, Chai Chengliang
Department of Optical-Electrical & Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Department of Energy & Power Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Brain Sci. 2021 Oct 24;11(11):1397. doi: 10.3390/brainsci11111397.
Educational theory claims that integrating learning style into learning-related activities can improve academic performance. Traditional methods to recognize learning styles are mostly based on questionnaires and online behavior analyses. These methods are highly subjective and inaccurate in terms of recognition. Electroencephalography (EEG) signals have significant potential for use in the measurement of learning style. This study uses EEG signals to design a deep-learning-based model of recognition to recognize people's learning styles with EEG features by using a non-overlapping sliding window, one-dimensional spatio-temporal convolutions, multi-scale feature extraction, global average pooling, and the group voting mechanism; this model is named the TSMG model (Temporal-Spatial-Multiscale-Global model). It solves the problem of processing EEG data of variable length, and improves the accuracy of recognition of the learning style by nearly 5% compared with prevalent methods, while reducing the cost of calculation by 41.93%. The proposed TSMG model can also recognize variable-length data in other fields. The authors also formulated a dataset of EEG signals (called the LSEEG dataset) containing features of the learning style processing dimension that can be used to test and compare models of recognition. This dataset is also conducive to the application and further development of EEG technology to recognize people's learning styles.
教育理论认为，将学习风格融入与学习相关的活动中可以提高学业成绩。传统的识别学习风格的方法大多基于问卷调查和在线行为分析。这些方法在识别方面具有高度主观性和不准确性。脑电图（EEG）信号在学习风格测量方面具有巨大潜力。本研究利用EEG信号设计了一种基于深度学习的识别模型，通过使用非重叠滑动窗口、一维时空卷积、多尺度特征提取、全局平均池化和群体投票机制，利用EEG特征识别人们的学习风格；该模型被命名为TSMG模型（时空多尺度全局模型）。它解决了处理可变长度EEG数据的问题，与现有方法相比，学习风格识别准确率提高了近5%，同时计算成本降低了41.93%。所提出的TSMG模型还可以识别其他领域的可变长度数据。作者还制定了一个EEG信号数据集（称为LSEEG数据集），其中包含学习风格处理维度的特征，可用于测试和比较识别模型。该数据集也有利于EEG技术在识别人们学习风格方面的应用和进一步发展。
