An adaptive joint CCA-ICA method for ocular artifact removal and its application to emotion classification

Xiaohui Gao; Shilai Zhang; Ke Liu; Ziqin Tan; Guanyi Zhao; Yumeng Han; Yue Cheng; Cunbo Li; Peiyang Li; Yin Tian; Fali Li

doi:10.1016/j.jneumeth.2023.109841

An adaptive joint CCA-ICA method for ocular artifact removal and its application to emotion classification

J Neurosci Methods. 2023 Apr 15:390:109841. doi: 10.1016/j.jneumeth.2023.109841. Epub 2023 Mar 21.

Authors

Xiaohui Gao¹, Shilai Zhang², Ke Liu³, Ziqin Tan¹, Guanyi Zhao¹, Yumeng Han¹, Yue Cheng⁴, Cunbo Li², Peiyang Li⁵, Yin Tian⁶, Fali Li⁷

Affiliations

¹ School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; Chongqing Institute for Brain and Intelligence, Guangyang Bay Laboratory, Chongqing 400064, China; Institute for Advanced Sciences, Chongqing University of Posts and Communications, China.
² Clinical Hospital of Chengdu Brain Science In-stitute, MOE Key Lab for Neuroinformation and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
³ Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Computational Intelligence Chongqing, 400065, China.
⁴ Key Laboratory of Intelligent Analysis and Decision on Complex Systems, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
⁵ School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; Chongqing Institute for Brain and Intelligence, Guangyang Bay Laboratory, Chongqing 400064, China; Institute for Advanced Sciences, Chongqing University of Posts and Communications, China. Electronic address: pyli@cqupt.edu.cn.
⁶ School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; Chongqing Institute for Brain and Intelligence, Guangyang Bay Laboratory, Chongqing 400064, China; Institute for Advanced Sciences, Chongqing University of Posts and Communications, China. Electronic address: tianyin@cqupt.edu.cn.
⁷ Clinical Hospital of Chengdu Brain Science In-stitute, MOE Key Lab for Neuroinformation and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China. Electronic address: fali.li@uestc.edu.cn.

PMID: 36948359
DOI: 10.1016/j.jneumeth.2023.109841

Abstract

Background: The quality of Electroencephalogram (EEG) signals is critical for revealing the neural mechanism of emotions. However, ocular artifacts decreased the signal to noise ratio (SNR) and covered the inherent cognitive component of EEGs, which pose a great challenge in neuroscience research.

New method: We proposed a novel unsupervised learning algorithm to adaptively remove the ocular artifacts by combining canonical correlation analysis (CCA), independent component analysis (ICA), higher-order statistics, empirical mode decomposition (EMD), and wavelet denoising techniques. Specifically, the combination of CCA and ICA aimed to improve the quality of source separation, while the higher-order statistics further located the source of ocular artifacts. Subsequently, these noised sources were further corrected by EMD and wavelet denoising to improve SNR of EEG signals.

Results: We evaluated the performance of our proposed method with simulation studies and real EEG applications. The results of simulation study showed our proposed method could significantly improve the quality of signals under almost all noise conditions compared to four state-of-art methods. Consistently, the experiments of real EEG applications showed that the proposed methods could efficiently restrict the components of ocular artifacts and preserve the inherent information of cognition processing to improve the reliability of related analysis such as power spectral density (PSD) and emotion recognition.

Comparison with existing methods: Our proposed model outperforms the comparative methods in EEG recovery, which further improve the application performance such as PSD analysis and emotion recognition.

Conclusions: The superior performance of our proposed method suggests that it is promising for removing ocular artifacts from EEG signals, which offers an efficient EEG preprocessing technology for the development of brain computer interface such as emotion recognition.

Keywords: Blind source separation; Electroencephalogram; Emotional classification; Ocular artifact removal.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Artifacts*
Canonical Correlation Analysis*
Electroencephalography / methods
Emotions
Reproducibility of Results
Signal Processing, Computer-Assisted