Walking Imagery Evaluation in Brain Computer Interfaces via a Multi-View Multi-Level Deep Polynomial Network

Baiying Lei, Xiaolu Liu, Shuang Liang, Wenlong Hang, Qiong Wang, Kup Sze Choi, Jing Qin

Research output: Journal article publicationJournal articleAcademic researchpeer-review

19 Citations (Scopus)


Brain-computer interfaces based on motor imagery (MI) have been widely used to support the rehabilitation of motor functions of the upper limbs rather than lower limbs. This is probably because it is more difficult to detect the brain activities of lower limb MI. In order to reliably detect the brain activities of lower limbs to restore or improve the walking ability of the disabled, we propose a new paradigm of walking imagery (WI) in a virtual environment (VE), in order to elicit the reliable brain activities and achieve a significant training effect. First, we extract and fuse both the spatial and time-frequency features as a multi-view feature to represent the patterns in the brain activity. Second, we design a multi-view multi-level deep polynomial network (MMDPN) to explore the complementarity among the features so as to improve the detection of walking from an idle state. Our extensive experimental results show that the VE-based paradigm significantly performs better than the traditional text-based paradigm. In addition, the VE-based paradigm can effectively help users to modulate the brain activities and improve the quality of electroencephalography signals. We also observe that the MMDPN outperforms other deep learning methods in terms of classification performance.

Original languageEnglish
Article number8626466
Pages (from-to)497-506
Number of pages10
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Issue number3
Publication statusPublished - Mar 2019


  • brain-computer interface
  • multi-view feature
  • multi-view multi-level deep. polynomial network.
  • virtual environment
  • Walking imagery

ASJC Scopus subject areas

  • Internal Medicine
  • Neuroscience(all)
  • Biomedical Engineering


Dive into the research topics of 'Walking Imagery Evaluation in Brain Computer Interfaces via a Multi-View Multi-Level Deep Polynomial Network'. Together they form a unique fingerprint.

Cite this