基于卷积神经网络的自适应样本加权脑机接口建模

针对脑机接口系统手动提取特征而产生的信息丢失与过拟合问题，建立了一个纯数据驱动的端到端的卷积神经网络模型.同时，为了解决卷积神经网络（convolutional neural network，CNN）需要大量数据而单人脑电数据量小的问题，建立了一套使用多人数据来建立目标用户模型的方法.通过分析其他人数据对目标个体模型的适应程度，清除那些对于目标模型贡献为负的样本.然后，在CNN网络的训练过程中，使用了一种元学习技术，赋予每一个训练数据一个权值.在训练CNN网络时，每一步网络参数更新之后，元学习器会根据训练集中数据样本对于最终模型的影响，自适应的调整每个样本数据的权值.实验结果表明，所提方法得到了比传统方法更好的分类精度，验证了所提方法的有效性.

Brain-computer Interface Model Based on Convolutional Neural Networks and Adaptive Sample Reweighting

To solve the problem of information loss and overfitting caused by feature extraction, we establish a pure data-driven end-to-end convolutional neural network (CNN) model for electroencephalography (EEG) signals in the motion-imagination-type brain-computer interface. At the same time, to solve the problem that CNN requires a large amount of training data but a small amount of single-subject EEG data, we establish a method of using multi-subject data. By analyzing if other subject's data can improve the target subject's model, we eliminate those samples that contribute negatively to the target model. Then, in the training process of the CNN, we use a meta-learning technique to give each training data a weight. When training the CNN, after each step of the network parameter updating, we analyze the effect of the sample data in the training set on the final model and adaptively adjust the weight of each sample data. The experimental results show that our CNN can achieve better accuracy than traditional methods when using multi-person data and performing data cleaning and adaptive sample weighting techniques.