Artifact Removal from EEG Signals Recorded Using Low Resolution Emotiv Device

Sinha, Aniruddha; Chatterjee, Debatri; Das, Rajat; Datta, Shreyasi; Gavas, Rahul; Saha, Sudipta

doi:10.1109/smc.2015.256

Cited by 9 publications

(3 citation statements)

References 17 publications

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“…Electrooculargram (EOG) artifacts: These are mainly caused due to eye blinks or eye movements which adds up as a high amplitude signal upon the EEG signal. This artifact mainly affects the frontal channels due to their vicinity from the eyes (Sinha et al, 2015a). Most of these artifacts falls below 4-5 Hz range (Gavas et al, 2020).…”

Section: Power Line Interference: Strong Signals Resulting From A/cmentioning

confidence: 99%

Brain-Computer Interfaces for Perception, Learning, and Motor Control

2021

Frontiers Research Topics

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About FrontiersFrontiers is more than just an open-access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers Journal SeriesThe Frontiers Journal Series is a multi-tier and interdisciplinary set of open-access, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers Journal Series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to QualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation.

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Section: Power Line Interference: Strong Signals Resulting From A/cmentioning

confidence: 99%

Brain-Computer Interfaces for Perception, Learning, and Motor Control

2021

Frontiers Research Topics

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“…Electrooculargram (EOG) artifacts: These are mainly caused due to eye blinks or eye movements which adds up as a high amplitude signal upon the EEG signal. This artifact mainly affects the frontal channels due to their vicinity from the eyes (Sinha et al, 2015a ). Most of these artifacts falls below 4–5 Hz range (Gavas et al, 2020 ).…”

Section: Formulation Of Optimization Problems In Bcimentioning

confidence: 99%

Formulation of the Challenges in Brain-Computer Interfaces as Optimization Problems—A Review

Fathima¹,

Kore

2021

Front. Neurosci.

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Electroencephalogram (EEG) is one of the common modalities of monitoring the mental activities. Owing to the non-invasive availability of this system, its applicability has seen remarkable developments beyond medical use-cases. One such use case is brain-computer interfaces (BCI). Such systems require the usage of high resolution-based multi-channel EEG devices so that the data collection spans multiple locations of the brain like the occipital, frontal, temporal, and so on. This results in huge data (with high sampling rates) and with multiple EEG channels with inherent artifacts. Several challenges exist in analyzing data of this nature, for instance, selecting the optimal number of EEG channels or deciding what best features to rely on for achieving better performance. The selection of these variables is complicated and requires a lot of domain knowledge and non-invasive EEG monitoring, which is not feasible always. Hence, optimization serves to be an easy to access tool in deriving such parameters. Considerable efforts in formulating these issues as an optimization problem have been laid. As a result, various multi-objective and constrained optimization functions have been developed in BCI that has achieved reliable outcomes in device control like neuro-prosthetic arms, application control, gaming, and so on. This paper makes an attempt to study the usage of optimization techniques in formulating the issues in BCI. The outcomes, challenges, and major observations of these approaches are discussed in detail.

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“…The advancements and convenience in non-invasive EEG systems have made their applications possible beyond medical applications in domains like gaming [1], motor imagery [2], learning [3], security applications [4], brain computer interfaces and so on. However, the real time application of EEG is very challenging due to the noise or artifacts [5] which are undesired components arising because of muscle movements, line noise, breathing, heart rate, eye blinks and so on; they mislead the actual cerebral activity of the recorded EEG data leading to complexity in analyzing the EEG data and often result in drifting the baseline of the EEG [6], [7]. Baseline drifts in EEG are slow varying waves and are transient patterns that are sharply countered.…”

mentioning

confidence: 99%

Hierarchical-Variational Mode Decomposition for Baseline Correction in Electroencephalogram Signals

Fathima,

Ahmed

2023

IEEE Open J. Instrum. Meas.

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Electroencephalogram (EEG) signals being time resolving signals, suffer very often from baseline drift caused by eye movements, breathing, variations in differential electrode impedances, movement of the subject and so on. This leads to misinterpretation of the EEG data under test. Hence, the absence of techniques for effectively removing the baseline drift from the signal can degrade the overall performance of the EEG-based systems. To address this issue, this paper deals with developing a novel scheme of hierarchically decomposing a signal using variational mode decomposition (VMD) in a tree-based model for a given level of the tree for accurate and effective analysis of the EEG signal and research in Brain Computer Interface (BCI). The proposed hierarchical extension to the conventional VMD, i.e. H-VMD is evaluated for performing baseline drift removal from the EEG signals. The method is tested using both synthetically generated and real EEG dataset. With the availability of groundtruth information in synthetically generated data, metrics like percentage root mean squared difference (PRD) and correlation coefficient are used as evaluation metrics. It is seen that the proposed method performs better in estimating the underlying baseline signal and closely resembles the groundtruth with higher values of correlation and lowest value of PRD when compared to the closely related state of the art methods.

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Artifact Removal from EEG Signals Recorded Using Low Resolution Emotiv Device

Cited by 9 publications

References 17 publications

Brain-Computer Interfaces for Perception, Learning, and Motor Control

Brain-Computer Interfaces for Perception, Learning, and Motor Control

Formulation of the Challenges in Brain-Computer Interfaces as Optimization Problems—A Review

Hierarchical-Variational Mode Decomposition for Baseline Correction in Electroencephalogram Signals

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