论文标题
使用集合课程学习和协作培训的运动图像解码
Motor Imagery Decoding Using Ensemble Curriculum Learning and Collaborative Training
论文作者
论文摘要
在这项工作中，我们研究了从脑电图（EEG）数据中解码的跨受试者运动图像（MI）的问题。由于各个个体间的差异（例如，脑解剖学，人格和认知概况），多种受试者的EEG数据集呈现出几种域移动。这些域转移使多受试者训练一项具有挑战性的任务，并阻碍了强大的跨主题概括。受域泛化技术对解决此类问题的重要性的启发，我们提出了一个具有多个功能提取器（第一阶段）和共享分类器（第二阶段）（第二阶段）构建的两阶段模型集合体系结构，我们以两个新颖的损失术语来训练端到端。第一个损失应用了课程学习，迫使每个功能提取器专门研究培训主题的一部分并促进特征多样性。第二个损失是一个内组合的蒸馏目标，它允许合奏模型之间的知识协作交流。我们将我们的方法与几种最新技术进行了比较，该技术在两个大型MI数据集上进行了主题无关的实验，即Physionet和OpenBMI。我们的算法在5倍的交叉验证和保留一个受试者的评估设置中均优于所有方法，使用了大量的可训练参数。我们证明，我们的模型结合了课程学习和协作培训的力量，可以提高学习能力和稳健的表现。我们的工作解决了多主体EEG数据集中域变化的问题，为无校准的大脑计算机接口铺平了道路。我们将代码公开提供：https：//github.com/gzoumpourlis/ensemble-mi
In this work, we study the problem of cross-subject motor imagery (MI) decoding from electroencephalography (EEG) data. Multi-subject EEG datasets present several kinds of domain shifts due to various inter-individual differences (e.g. brain anatomy, personality and cognitive profile). These domain shifts render multi-subject training a challenging task and also impede robust cross-subject generalization. Inspired by the importance of domain generalization techniques for tackling such issues, we propose a two-stage model ensemble architecture built with multiple feature extractors (first stage) and a shared classifier (second stage), which we train end-to-end with two novel loss terms. The first loss applies curriculum learning, forcing each feature extractor to specialize to a subset of the training subjects and promoting feature diversity. The second loss is an intra-ensemble distillation objective that allows collaborative exchange of knowledge between the models of the ensemble. We compare our method against several state-of-the-art techniques, conducting subject-independent experiments on two large MI datasets, namely PhysioNet and OpenBMI. Our algorithm outperforms all of the methods in both 5-fold cross-validation and leave-one-subject-out evaluation settings, using a substantially lower number of trainable parameters. We demonstrate that our model ensembling approach combining the powers of curriculum learning and collaborative training, leads to high learning capacity and robust performance. Our work addresses the issue of domain shifts in multi-subject EEG datasets, paving the way for calibration-free brain-computer interfaces. We make our code publicly available at: https://github.com/gzoumpourlis/Ensemble-MI
