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

Young Eun Lee; Seong Whan Lee

doi:10.1109/BCI51272.2021.9385313

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 proceeding › Conference contribution

5 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 language	English
Title of host publication	9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728184852
DOIs	https://doi.org/10.1109/BCI51272.2021.9385313
Publication status	Published - 2021 Feb 22
Event	9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021 - Gangwon, Korea, Republic of Duration: 2021 Feb 22 → 2021 Feb 24

Publication series

Name	9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021

Conference

Conference	9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021
Country/Territory	Korea, Republic of
City	Gangwon
Period	21/2/22 → 21/2/24

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

Access to Document

10.1109/BCI51272.2021.9385313

Cite this

Lee, Y. E., & Lee, S. W. (2021). Decoding Event-related Potential from Ear-EEG Signals based on Ensemble Convolutional Neural Networks in Ambulatory Environment. In 9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021 Article 9385313 (9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BCI51272.2021.9385313

Decoding Event-related Potential from Ear-EEG Signals based on Ensemble Convolutional Neural Networks in Ambulatory Environment. / Lee, Young Eun; Lee, Seong Whan.
9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021. Institute of Electrical and Electronics Engineers Inc., 2021. 9385313 (9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lee, YE & Lee, SW 2021, Decoding Event-related Potential from Ear-EEG Signals based on Ensemble Convolutional Neural Networks in Ambulatory Environment. in 9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021., 9385313, 9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021, Institute of Electrical and Electronics Engineers Inc., 9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021, Gangwon, Korea, Republic of, 21/2/22. https://doi.org/10.1109/BCI51272.2021.9385313

Lee YE, Lee SW. Decoding Event-related Potential from Ear-EEG Signals based on Ensemble Convolutional Neural Networks in Ambulatory Environment. In 9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021. Institute of Electrical and Electronics Engineers Inc. 2021. 9385313. (9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021). doi: 10.1109/BCI51272.2021.9385313

Lee, Young Eun ; Lee, Seong Whan. / Decoding Event-related Potential from Ear-EEG Signals based on Ensemble Convolutional Neural Networks in Ambulatory Environment. 9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021).

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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.",

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

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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