Deep learning with convolutional neural networks (ConvNets) has dramatically improved the learning capabilities of computer vision applications just through considering raw data without any prior feature extraction. Nowadays, there is a rising curiosity in interpreting and analyzing electroencephalography (EEG) dynamics with ConvNets. Our study focused on ConvNets of different structures, the efficiency of multiple machine learning algorithms with optimization on ConvNets, constructing for predicting imagined left and right movements on a subject-independent basis through raw EEG data. We adapted novel lower-upper triangularization based extreme learning machines (LuELM) to the ConvNet architecture. Results showed that recently advanced methods in machine learning field, i.e. adaptive moments and batch normalization together with dropout strategy, improved ConvNets predicting ability, outperforming that of conventional fully-connected neural networks with widely-used spectral features. The proposed prediction model achieved improvements in classification performances with the rates of 90.33%, 91.00%, and 89.67% for accuracy, recall, and specificity, respectively.

使用卷积神经网络（ConvNets）进行的深度学习仅考虑原始数据就可以大大提高计算机视觉应用程序的学习能力，而无需事先提取任何特征。如今，使用ConvNets解释和分析脑电图（EEG）动力学的兴趣日益浓厚。我们的研究重点是不同结构的ConvNet，在ConvNets上进行优化的多种机器学习算法的效率，以及通过原始EEG数据在独立于受试者的基础上预测想象的左右运动的构造。我们将新颖的基于上下三角关系的极限学习机（LuELM）改编为ConvNet架构。结果表明，机器学习领域的最新方法是自适应矩和批量归一化以及辍学策略，改进了ConvNets的预测能力，优于具有广泛使用的光谱特征的常规全连接神经网络。所提出的预测模型实现了分类性能的改进，其准确性，召回率和特异性分别达到90.33％，91.00％和89.67％。