Decoding Event-related Potential from Ear-EEG Signals based on Ensemble Convolutional Neural Networks in Ambulatory Environment

Young Eun Lee, Seong Whan Lee

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

3 Citations (Scopus)

Abstract

Recently, practical brain-computer interface is actively carried out, especially, in an ambulatory environment. However, the electroencephalography (EEG) signals are distorted by movement artifacts and electromyography signals when users are moving, which make hard to recognize human intention. In addition, as hardware issues are also challenging, ear-EEG has been developed for practical brain-computer interface and has been widely used. In this paper, we proposed ensemble-based convolutional neural networks in ambulatory environment and analyzed the visual event-related potential responses in scalp-and ear-EEG in terms of statistical analysis and brain-computer interface performance. The brain-computer interface performance deteriorated as 3-14% when walking fast at 1.6 m/s. The proposed methods showed 0.728 in average of the area under the curve. The proposed method shows robust to the ambulatory environment and imbalanced data as well.

Original languageEnglish
Title of host publication9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728184852
DOIs
Publication statusPublished - 2021 Feb 22
Event9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021 - Gangwon, Korea, Republic of
Duration: 2021 Feb 222021 Feb 24

Publication series

Name9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021

Conference

Conference9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021
Country/TerritoryKorea, Republic of
CityGangwon
Period21/2/2221/2/24

Bibliographical note

Funding Information:
This work was partly supported by Institute for Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00451, Development of BCI based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning), (No. 2015-0-00185, Development of Intelligent Pattern Recognition Softwares for Ambulatory Brain Computer Interface), and (No. 2019-0-00079, Artificial Intelligence Graduate School Program (Korea University)).

Publisher Copyright:
© 2021 IEEE.

Keywords

  • ambulatory environment
  • brain-computer interface
  • ear-EEG
  • ensemble CNN
  • event-related potential

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Signal Processing

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