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2021 | OriginalPaper | Chapter

13. Resting-State EEG Sex Classification Using Selected Brain Connectivity Representation

Authors : Jean Li, Jeremiah D. Deng, Divya Adhia, Dirk De Ridder

Published in: Advances in Artificial Intelligence, Computation, and Data Science

Publisher: Springer International Publishing

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Abstract

Electroencephalography (EEG) is a widely used non-invasive technique to measure multi-channel potentials that reflect the electrical activity of the brain. Over the last few decades, EEG analysis has been an intensively explored research topic due to its potentials in being applied to the diagnosis of neurological diseases, such as epilepsy, brain tumors, head injury, sleep disorders, and dementia [19]. Despite many advances made in recent years, EEG signal analysis remains a challenging task. In addition to being non-stationary, EEG signals often have high noise-to-information ratios, and they can be significantly affected by various artifacts, demonstrating characteristics that differ from signals generated by activities in the brain [21]. Common artifacts include eye movements, jaw tension, and muscle contractions. To make effective signal analysis even more challenging, EEG signals are highly individual-specific, and cross-subject pattern identification can be elusive.

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Metadata
Title
Resting-State EEG Sex Classification Using Selected Brain Connectivity Representation
Authors
Jean Li
Jeremiah D. Deng
Divya Adhia
Dirk De Ridder
Copyright Year
2021
DOI
https://doi.org/10.1007/978-3-030-69951-2_13

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