CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 先进机器人
Convolutional LSTM: A Deep Learning Method for Motion Intention Recognition based on Spatiotemporal EEG Data
Zhijie Fang1,2; Weiqun Wang1,2; Zeng-Guang Hou1,2,3
2019
Conference Namethe 26th International Conference on Neural Information Processing (ICONIP)
Conference Date2019-12-12
Conference PlaceSydney, Australia
Abstract

Brain-Computer Interface (BCI) is a powerful technology that allows human beings to communicate with computers or to control devices. Owing to their convenient collection, non-invasive Electroencephalography (EEG) signals play an important role in BCI systems. Design of high-performance motion intention recognition algorithm based on EEG data under cross-subject and multi-category circumstances is a crucial challenge. Towards this purpose, a convolutional recurrent neural network is proposed. The raw EEG streaming is transformed into image sequence according to its location of the primary sensorimotor area to preserve its spatiotemporal features. A Convolutional Long ShortTerm Memory (ConvLSTM) network is used to encode spatiotemporal information and generate a better representation from the obtained image sequence. The spatial features are then extracted from the output of ConvLSTM network by convolutional layer. The convolutional layer along with ConvLSTM network is capable of capturing the spatiotemporal features which enables the recognition of motion intention from the raw EEG signals. Experiments are carried out on the PhysioNet EEG motor imagery dataset to test the performance of the proposed method. It is shown that the proposed method can achieve high accuracy of 95.15%, which outperforms previous methods. Meanwhile, the proposed method can be used to design high-performance BCI systems, such as mind-controlled exoskeletons, prosthetic hands and rehabilitation robotics.
 

Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/26192
Collection复杂系统管理与控制国家重点实验室_先进机器人
Corresponding AuthorWeiqun Wang
Affiliation1.University of Chinese Academy of Sciences
2.Institute of Automation, Chinese Academy of Sciences
3.The CAS Center for Excellence in Brain Science and Intelligence Technology
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhijie Fang,Weiqun Wang,Zeng-Guang Hou. Convolutional LSTM: A Deep Learning Method for Motion Intention Recognition based on Spatiotemporal EEG Data[C],2019.
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