报告题目：Learning Joint Space-Time-Frequency Features for EEG Decoding on Small Labeled Data
报告内容摘要：Brain-computer interfaces (BCIs), which control external equipment using cerebral activity, have received considerable attention recently. Translating brain activities measured by electroencephalography (EEG) into correct control commands is a critical problem in this field. Most existing EEG decoding methods separate feature extraction from classification and thus are not robust across different BCI users. In this paper, we propose to learn subject-specific features jointly with the classification rule. We develop a deep convolutional network (ConvNet) to decode EEG signals end-to-end by stacking time-frequency transformation, spatial filtering, and classification together. Our proposed ConvNet implements a joint space-time-frequency feature extraction scheme for EEG decoding. Morlet wavelet-like kernels used in our network significantly reduce the number of parameters compared with classical convolutional kernels and endow the features learned at the corresponding layer with a clear interpretation, i. e. spectral amplitude. We further utilize subject-to-subject weight transfer, which uses parameters of the networks trained for existing subjects to initialize the network for a new subject, to solve the dilemma between a large number of demanded data for training deep ConvNets and small labeled data collected in BCIs. The proposed approach is evaluated on three public datasets, obtaining superior classification performance compared with the state-of-the-art methods.
报告人简介：唐凤珍，2009年获得东北大学学士学位，2011年获得东北大学硕士学位，2015年获得英国伯明翰大学博士学位，现为中国科学院沈阳自动化研究所副研究员，是机器人学国家重点实验室神经计算课题组组长，中国科学院率先行动“百人计划”青年俊才（C 类）。在机器学习领域重要期刊Neural Networks， Neurocomputing、Neural Computation等 和人工智能领域重要国际学术会议IJCAI， KDD等以及机器人学重要会议ICRA等发表学术论文15篇。