Feature learning from incomplete EEG with denoising autoencoder

Junhua Li, Zbigniew Struzik, Liqing Zhang, Andrzej Cichocki

Research output: Contribution to journalArticlepeer-review

100 Citations (Scopus)


An alternative pathway for the human brain to communicate with the outside world is by means of a brain computer interface (BCI). A BCI can decode electroencephalogram (EEG) signals of brain activities, and then send a command or an intent to an external interactive device, such as a wheelchair. The effectiveness of the BCI depends on the performance in decoding the EEG. Usually, the EEG is contaminated by different kinds of artefacts (e.g., electromyogram (EMG), background activity), which leads to a low decoding performance. A number of filtering methods can be utilized to remove or weaken the effects of artefacts, but they generally fail when the EEG contains extreme artefacts. In such cases, the most common approach is to discard the whole data segment containing extreme artefacts. This causes the fatal drawback that the BCI cannot output decoding results during that time. In order to solve this problem, we employ the Lomb-Scargle periodogram to estimate the spectral power from incomplete EEG (after removing only parts contaminated by artefacts), and Denoising Autoencoder (DAE) for learning. The proposed method is evaluated with motor imagery EEG data. The results show that our method can successfully decode incomplete EEG to good effect.

Original languageEnglish
Pages (from-to)23-31
Number of pages9
Publication statusPublished - 1 Oct 2015
Externally publishedYes


  • Brain computer interface
  • Denoising autoencoder
  • Incomplete EEG
  • Motor imagery
  • Spectral power estimation


Dive into the research topics of 'Feature learning from incomplete EEG with denoising autoencoder'. Together they form a unique fingerprint.

Cite this