The Removal of EOG Artifacts From EEG Signals Using Independent Component Analysis and Multivariate Empirical Mode Decomposition

Wang, Gang; Teng, Chaolin; Li, Kuo; Zhang, Zhonglin; Yan, Xiao‐Hai

doi:10.1109/jbhi.2015.2450196

Cited by 123 publications

(62 citation statements)

References 33 publications

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“…The results show that the EEMD-ICA algorithm has the best performance. Similar manner to EEMD-ICA, the EEMD-CCA [92,93,94,95], remove contaminated IMFs and then using unmixing matrix W, determined by CCA algorithm, to reconstruct source signals [96]. Another method utilized interchannel dependence information seen in few channel situation by combining multivariate empirical mode decomposition (MEMD) and CCA was discussed in [78].…”

Section: Hybrid Methodsmentioning

confidence: 99%

Removal of Artifacts from EEG Signals: A Review

Jiang

Bian

Tian

2019

Sensors

557

329

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Electroencephalogram (EEG) plays an important role in identifying brain activity and behavior. However, the recorded electrical activity always be contaminated with artifacts and then affect the analysis of EEG signal. Hence, it is essential to develop methods to effectively detect and extract the clean EEG data during encephalogram recordings. Several methods have been proposed to remove artifacts, but the research on artifact removal continues to be an open problem. This paper tends to review the current artifact removal of various contaminations. We first discuss the characteristics of EEG data and the types of different artifacts. Then, a general overview of the state-of-the-art methods and their detail analysis are presented. Lastly, a comparative analysis is provided for choosing a suitable methods according to particular application.

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Section: Hybrid Methodsmentioning

confidence: 99%

Removal of Artifacts from EEG Signals: A Review

Jiang

Bian

Tian

2019

Sensors

557

329

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“…X indicates the filtered EEG recorded with 3 electrodes (O1, O2, Oz) on the visual cortex [30]. The EEG was divided into several frequency bands EEG by wavelet packet decomposition [31]- [34]. The powers of divided EEG are calculated as the following (1).…”

Section: ) Detection Of Eyes Closed and Openmentioning

confidence: 99%

Quadcopter Control System Using a Hybrid BCI Based on Off-Line Optimization and Enhanced Human-Machine Interaction

et al. 2020

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Quadcopter is an important way for the human to explore the physical world. The brain-computer interface (BCI) technology is used to control the quadcopter flight in order to help disabled persons communicate with the external world freely. In this study, a quadcopter control system using a hybrid BCI based on off-line optimization and enhanced human-machine interaction was designed to control the quadcopter flight in 3D physical space. The proposed system implemented the control of quadcopter moving up/down, forward/backward, left/right by six different SSVEP, and turning left/right by left-hand and right-hand motor imagery. Meanwhile, the optimization of the control system and the human-machine interaction enhancement improved practicability in real-time use. Five subjects participated in an on-line experiment to control the quadcopter flight in real-time. The average classification accuracy of EEG-based commands in the on-line experiment was 87.09±2.82% and information transfer rate (ITR) was 0.857±0.085 bits/min. The results demonstrated the feasibility of multidirectional control of quadcopter flight in 3D space by using hybrid BCI technology and revealed the practicality and operability of the hybrid BCI control system based on off-line optimization and human-machine interaction enhancement. INDEX TERMS Quadcopter control system, motor imagery, steady-state visual evoked potential, off-line optimization, human-machine interaction.

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“…EMG signals from 10 muscle locations described in Section 1 will be mathematically modeled, measured and compared to assess the quality of their contextual information. In this work, Empirical Mode Decomposition (EMD) [25] will be used to model all signals while Total Hilbert-Huang Spectral Entropy [26][27] and Kullback-Leibler Divergence ( ) [28] will be used for measuring the quality of information from these signals. The mathematical background of these techniques will be explained in Section 3.…”

Section: Emg Modelling Using Empirical Mode Decomposition (Emd)mentioning

confidence: 99%

Quality assessment on muscle locations for speech representation

Safie

Rahim

2020

IJEECS

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<span lang="EN-US">There are more than 68 muscles, which are activated either simultaneously or sequentially during speech production. To monitor the signals from all these muscles at once, involve a lot of sensors and such system is very expensive. In the Quran therapeutic treatment applications, the use of specific muscles is very important, for the production of correct Arabic pronunciation. The proper pronunciation will improve the reader's understanding of what is being read, thus assisting the effectiveness of the therapy process. The objective of this study is to identify the most optimal muscle location, which is suitable for monitoring the quality of a recitation during the Quran’s therapeutic process, based on the information content embedded in their Electromyogram (EMG) signals. Empirical Mode Decomposition (EMD) technique was used in this study to extract features of the EMG while the combination of Hilbert Huang Spectral Entropy (HHSE) and Kullback Leibler Divergence (KLD) techniques were used to quantify the information content in each feature. Combination of these techniques managed to rank ten widely used speech muscles in the literature based upon their information content. Four muscle locations have been suggested, which is believed to be sufficient in developing a low-cost self-assessment system for monitoring Quran recitation.</span>

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The Removal of EOG Artifacts From EEG Signals Using Independent Component Analysis and Multivariate Empirical Mode Decomposition

Cited by 123 publications

References 33 publications

Removal of Artifacts from EEG Signals: A Review

Removal of Artifacts from EEG Signals: A Review

Quadcopter Control System Using a Hybrid BCI Based on Off-Line Optimization and Enhanced Human-Machine Interaction

Quality assessment on muscle locations for speech representation

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