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Current Medical Imaging

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ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

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Review Article

Progress and Challenges in Physiological Artifacts’ Detection in Electroencephalographic Readings

Author(s): Amandeep Bisht, Preeti Singh*, Chamandeep Kaur, Sunil Agarwal and Manisha Ajmani

Volume 18, Issue 5, 2022

Published on: 10 January, 2022

Article ID: e080921196274 Pages: 23

DOI: 10.2174/1573405617666210908124704

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Abstract

Background: Electroencephalographic (EEG) recordings are used to trace neural activity within the cortex to study brain functioning over time.

Introduction: During data acquisition, the unequivocal way to reduce artifact is to avoid artifact stimulating events. Though there are certain artifacts that make this task challenging due to their association with the internal human mechanism, in the human-computer interface, these physiological artifacts are of great assistance and act as a command signal for controlling a device or an application (communication). That is why pre-processing of electroencephalographic readings has been a progressive area of exploration, as none of the published work can be viewed as a benchmark for constructive artifact handling.

Methods: This review offers a comprehensive insight into state of the art physiological artifact removal techniques listed so far. The study commences from the single-stage traditional techniques to the multistage techniques, examining the pros and cons of each discussed technique. Also, this review paper gives a general idea of various datasets available and briefs the topical trend in EEG signal processing.

Results: Comparing the state of the art techniques with hybrid ones on the basis of performance and computational complexity, it has been observed that the single-channel techniques save computational time but lack in effective artifact removal especially physiological artifacts. On the other hand, hybrid techniques merge the essential characteristics resulting in increased performance, but time consumption and complexity remain an issue.

Conclusion: Considering the high probability of the presence of multiple artifacts in EEG channels, a trade-off between performance, time and computational complexity is the only key for effective processing of artifacts in the time ahead. This paper is anticipated to facilitate upcoming researchers in enriching the contemporary artifact handling techniques to mitigate the expert’s burden.

Keywords: EEG, physiological artifacts, artifact removal, EOG, EMG, signal processing.

Graphical Abstract

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