SessionNet: Feature Similarity-Based Weighted Ensemble Learning for Motor Imagery Classification

Byeong Hoo Lee, Ji Hoon Jeong, Seong Whan Lee

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


A brain-computer interface (BCI) provides a direct communication pathway between user and external devices. Motor imagery (MI) paradigm is widely used in non-invasive BCI to control external devices by decoding user intentions. The traditional MI-BCI problem is to obtain enough EEG data samples for adopting deep learning techniques, as electroencephalography (EEG) data have intricate and non-stationary properties that can cause a discrepancy between different sessions of data. Because of the discrepancy, the recorded EEG data with different sessions cannot be treated as the same. In this study, we recorded a large intuitive EEG dataset that contained nine types of movements of a single-arm across 12 subjects. We proposed a SessionNet that learns generality with EEG data recorded over multiple sessions using feature similarity to improve classification performance. Additionally, the SessionNet adopts the principle of a hierarchical convolutional neural network that shows robust classification performance regardless of the number of classes. The SessionNet outperforms conventional methods on 3-class, 5-class, and two types of 7-class and 9-class of a single-arm task. Hence, our approach could demonstrate the possibility of using feature similarity based on a novel ensemble learning method to train generality from multiple session data for better MI classification performance.

Original languageEnglish
Article number9146526
Pages (from-to)134524-134535
Number of pages12
JournalIEEE Access
Publication statusPublished - 2020

Bibliographical note

Publisher Copyright:
© 2013 IEEE.


  • Brain-computer interface (BCI)
  • convolutional neural network (CNN)
  • electroencephalogram (EEG)
  • motor imagery (MI)
  • weighted ensemble learning

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering


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