Error Correction Regression Framework for Enhancing the Decoding Accuracies of Ear-EEG Brain-Computer Interfaces

No Sang Kwak; Seong Whan Lee

doi:10.1109/TCYB.2019.2924237

Error Correction Regression Framework for Enhancing the Decoding Accuracies of Ear-EEG Brain-Computer Interfaces

No Sang Kwak, Seong Whan Lee^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

44 Citations (Scopus)

Abstract

Ear-electroencephalography (EEG) is a promising tool for practical brain-computer interface (BCI) applications because it is more unobtrusive, comfortable, and mobile than a typical scalp-EEG system. However, an ear-EEG has a natural constraint of electrode location (e.g., limited in or around the ear) for acquiring informative brain signals sufficiently. Achieving reliable performance of ear-EEG in specific BCI paradigms that do not utilize brain signals on the temporal lobe around the ear is difficult. For example, steady-state visual evoked potentials (SSVEPs), which are mainly generated in the occipital area, have a significantly attenuated and distorted amplitude in ear-EEG. Therefore, preserving the high level of decoding accuracy is challenging and essential for SSVEP BCI based on ear-EEG. In this paper, we first investigate linear and nonlinear regression methods to increase the decoding accuracy of ear-EEG regarding SSVEP paradigm by utilizing the estimated target EEG signals on the occipital area. Then, we investigate an ensemble method to consider the prediction variability of the regression methods. Finally, we propose an error correction regression (ECR) framework to reduce the prediction errors by adding an additional nonlinear regression process (i.e., kernel ridge regression). We evaluate the ECR framework in terms of single session, session-to-session transfer, and subject-transfer decoding. We also validate the online decoding ability of the proposed framework with a short-time window size. The average accuracies are observed to be 91.11±9.14%, 90.52±8.67%, 86.96±12.13%, and 78.79±12.59%. This paper demonstrates that SSVEP BCI based on ear-EEG can achieve reliable performance with the proposed ECR framework.

Original language	English
Article number	8758838
Pages (from-to)	3654-3667
Number of pages	14
Journal	IEEE Transactions on Cybernetics
Volume	50
Issue number	8
DOIs	https://doi.org/10.1109/TCYB.2019.2924237
Publication status	Published - 2020 Aug

Bibliographical note

Funding Information:
Manuscript received November 20, 2018; revised March 21, 2019 and May 26, 2019; accepted June 14, 2019. Date of publication July 10, 2019; date of current version July 10, 2020. This work was supported in part by the Samsung Research Funding Center of Samsung Electronics under Project SRFC-TC1603-02, and in part by the Institute for Information & Communications Technology Planning & Evaluation grant funded by the Korea Government (Development of BCI-Based Brain and Cognitive Computing Technology for Recognizing User’s Intentions Using Deep Learning) under Grant 2017-0-00451. This paper was recommended by Associate Editor C.-T. Lin. (Corresponding author: Seong-Whan Lee.) N.-S. Kwak is with the Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, South Korea (e-mail: [email protected]).

Publisher Copyright:
© 2013 IEEE.

Keywords

Brain-computer interface (BCI)
ear-electroencephalography (EEG)
nonlinear regression
steady-state visual evoked potential (SSVEP)

ASJC Scopus subject areas

Software
Control and Systems Engineering
Information Systems
Human-Computer Interaction
Computer Science Applications
Electrical and Electronic Engineering

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10.1109/TCYB.2019.2924237

Cite this

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title = "Error Correction Regression Framework for Enhancing the Decoding Accuracies of Ear-EEG Brain-Computer Interfaces",

abstract = "Ear-electroencephalography (EEG) is a promising tool for practical brain-computer interface (BCI) applications because it is more unobtrusive, comfortable, and mobile than a typical scalp-EEG system. However, an ear-EEG has a natural constraint of electrode location (e.g., limited in or around the ear) for acquiring informative brain signals sufficiently. Achieving reliable performance of ear-EEG in specific BCI paradigms that do not utilize brain signals on the temporal lobe around the ear is difficult. For example, steady-state visual evoked potentials (SSVEPs), which are mainly generated in the occipital area, have a significantly attenuated and distorted amplitude in ear-EEG. Therefore, preserving the high level of decoding accuracy is challenging and essential for SSVEP BCI based on ear-EEG. In this paper, we first investigate linear and nonlinear regression methods to increase the decoding accuracy of ear-EEG regarding SSVEP paradigm by utilizing the estimated target EEG signals on the occipital area. Then, we investigate an ensemble method to consider the prediction variability of the regression methods. Finally, we propose an error correction regression (ECR) framework to reduce the prediction errors by adding an additional nonlinear regression process (i.e., kernel ridge regression). We evaluate the ECR framework in terms of single session, session-to-session transfer, and subject-transfer decoding. We also validate the online decoding ability of the proposed framework with a short-time window size. The average accuracies are observed to be 91.11±9.14\%, 90.52±8.67\%, 86.96±12.13\%, and 78.79±12.59\%. This paper demonstrates that SSVEP BCI based on ear-EEG can achieve reliable performance with the proposed ECR framework. ",

keywords = "Brain-computer interface (BCI), ear-electroencephalography (EEG), nonlinear regression, steady-state visual evoked potential (SSVEP)",

author = "Kwak, \{No Sang\} and Lee, \{Seong Whan\}",

note = "Funding Information: Manuscript received November 20, 2018; revised March 21, 2019 and May 26, 2019; accepted June 14, 2019. Date of publication July 10, 2019; date of current version July 10, 2020. This work was supported in part by the Samsung Research Funding Center of Samsung Electronics under Project SRFC-TC1603-02, and in part by the Institute for Information \& Communications Technology Planning \& Evaluation grant funded by the Korea Government (Development of BCI-Based Brain and Cognitive Computing Technology for Recognizing User{\textquoteright}s Intentions Using Deep Learning) under Grant 2017-0-00451. This paper was recommended by Associate Editor C.-T. Lin. (Corresponding author: Seong-Whan Lee.) N.-S. Kwak is with the Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, South Korea (e-mail: [email protected]). Publisher Copyright: {\textcopyright} 2013 IEEE.",

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Error Correction Regression Framework for Enhancing the Decoding Accuracies of Ear-EEG Brain-Computer Interfaces

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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