Learning joint space–time–frequency features for EEG decoding on small labeled data
文献类型：期刊论文
| 作者 | Feng XS(封锡盛)2,3 ; Si BL(斯白露)2,3; Tang FZ(唐凤珍)2,3; Zhao DY(赵冬晔)1,2,3
|
| 刊名 | Neural Networks
 |
| 出版日期 | 2019 |
| 卷号 | 114页码:67-77 |
| 关键词 | Brain-computer interfaces Convolutional neural network Joint space–time–frequency feature learning Subject-to-subject weight transfer Small labeled data |
| ISSN号 | 0893-6080 |
| 产权排序 | 1 |
| 英文摘要 | 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 data sets, obtaining superior classification performance compared with the state-of-the-art methods. |
| 语种 | 英语 |
| 资助机构 | National Key Research and Development Program of China [grant number 2016YFC0801808] ; Frontier Science research project of the Chinese Academy of Sciences [grant number QYZDY-SSW-JSC005] ; CAS Pioneer Hundred Talents Program, China [grant number Y8F1160101] ; State Key Laboratory of Robotics, China [grant number Y7C120E101] |
| 源URL | [http://ir.sia.cn/handle/173321/24471]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 通讯作者 | Tang FZ(唐凤珍) |
| 作者单位 | 1.University of Chinese Academy of Sciences, Beijing 100049, China 2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China 3.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China |
| 推荐引用方式 GB/T 7714 | Feng XS,Si BL,Tang FZ,et al. Learning joint space–time–frequency features for EEG decoding on small labeled data[J]. Neural Networks,2019,114:67-77. |
| APA | Feng XS,Si BL,Tang FZ,&Zhao DY.(2019).Learning joint space–time–frequency features for EEG decoding on small labeled data.Neural Networks,114,67-77. |
| MLA | Feng XS,et al."Learning joint space–time–frequency features for EEG decoding on small labeled data".Neural Networks 114(2019):67-77. |
入库方式： OAI收割
来源：沈阳自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。