Importance of Reliable EEG Data in Motor Imagery Classification: Attention Level-based Approach

Seho Lee, Tak Kim, Seung Ouk Hwang, Hakseung Kim, Dong Joo Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

Brain-computer interface (BCI) has been widely used to predict the intention of users in motor imagery-based (MI-based) task. Although the overall MI classification accuracy has been largely enhanced from previous efforts, applying MI-BCI to the so-called BCI-illiterate subjects remains as an unsolved problem. This study proposed a physiological approach for improving MI-BCI performance, by measuring the baseline attention level estimated by coefficient F from the electroencephalogram (EEG) band-activities. In this endeavor, a total of 9 MI-EEG recordings were retrieved from an open BCI dataset. A measure of attention level was calculated for each trial to select high attention trials. High attention trial-based machine learning model showed higher MI classification performance (median accuracy = 62.50% (interquartile range (IQR) = 55.21-82.29%)) than the conventional approach (median accuracy = 57.64% (IQR = 54.17-62.50%)) with statistical significance (Wilcoxon rank sum test, p = 0.037). This study found that machine learning models trained from high attention trials yield improved classification accuracy to the models derived from total trial regardless of both BCI illiterate and literate.

Original languageEnglish
Title of host publication8th International Winter Conference on Brain-Computer Interface, BCI 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728147079
DOIs
Publication statusPublished - 2020 Feb
Event8th International Winter Conference on Brain-Computer Interface, BCI 2020 - Gangwon, Korea, Republic of
Duration: 2020 Feb 262020 Feb 28

Publication series

Name8th International Winter Conference on Brain-Computer Interface, BCI 2020

Conference

Conference8th International Winter Conference on Brain-Computer Interface, BCI 2020
Country/TerritoryKorea, Republic of
CityGangwon
Period20/2/2620/2/28

Bibliographical note

Funding Information:
through the National Research Foundation of Korea(NRF) funded by of Science and ICT (NRF-2019M3C1B8077477). *Corresponding author

Funding Information:
This work was supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (No. 2017-0-00432, Development of non-invasive integrated BCI SW platform to control home appliances and external devices by user's thought via AR/VR interface); Convergent Technology R&D Program for Human Augmentation

Publisher Copyright:
© 2020 IEEE.

Keywords

  • BCI illiteracy
  • attention
  • brain computer interface
  • electroencephalography
  • motor imagery task

ASJC Scopus subject areas

  • Behavioral Neuroscience
  • Cognitive Neuroscience
  • Artificial Intelligence
  • Human-Computer Interaction

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