Deep learning of multifractal attributes from motor imagery induced EEG

Junhua Li, Andrzej Cichocki

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


Electroencephalogram (EEG) is an effective metric to monitor or measure human brain activities. Another advantage for EEG utilization is non-invasive, and is not harmful to subjects. However, this leads to two drawbacks: low signal-to-noise ratio and non-stationarity. In order to extract useful features contained in the EEG, multifractal attributes were explored in this paper. A few attributes were utilized to analyze the EEG recorded during motor imagery tasks. Then, we built a deep learning model based on denoising autoencoder to recognize different motor imagery tasks. From the results, we can find that 1) Motor imagery induced EEG is of multifractal attributes, 2) multifractal spectrum D(h) and the statistics cp based on cumulants can reflect difference between different motor imagery tasks, so they can be adopted as features for classification. 3) A deep network with initialization by denoising autoencoder is suitable to learn multifractal attributes extracted from EEG. The classification accuracies demonstrated that the proposed method is feasible.

Original languageEnglish
Pages (from-to)503-510
Number of pages8
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publication statusPublished - 2014
Externally publishedYes


  • Brain computer interface
  • Deep learning
  • Denoising autoencoder
  • Multifractal attribute
  • Wavelet leader


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