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Topological Inference on Electroencephalography

In: Research Papers in Statistical Inference for Time Series and Related Models

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  • Yuan Wang

    (University of South Carolina)

Abstract

Statistical inference of electroencephalography (EEG) from diverse clinical groups often requires considerable technicality and computational power. Motivated by topological data analysis, we now take a new analytical angle on EEG signals by characterizing their shape with persistent homology (PH). This paper reviews our recent studies where novel statistical inference procedures are developed for PH features of EEG signals to address clinical questions in brain disorders.

Suggested Citation

  • Yuan Wang, 2023. "Topological Inference on Electroencephalography," Springer Books, in: Yan Liu & Junichi Hirukawa & Yoshihide Kakizawa (ed.), Research Papers in Statistical Inference for Time Series and Related Models, chapter 0, pages 539-553, Springer.
    • Handle: RePEc:spr:sprchp:978-981-99-0803-5_24
    DOI: 10.1007/978-981-99-0803-5_24
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