Topological data analysis of single-trial electroencephalographic signals
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/631519
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AbstractEpilepsy is a neurological disorder marked by sudden recurrent episodes of sensory disturbance, loss of consciousness, or convulsions, associated with abnormal electrical activity in the brain. Statistical analysis of neuro-physiological recordings, such as electroencephalography (EEG), facilitates the understanding of epileptic seizures. Standard statistical methods typically analyze amplitude and frequency information in EEG signals. In the current study, we propose a topological data analysis (TDA) framework to analyze single-trial EEG signals. The framework denoises signals with a weighted Fourier series (WFS), and tests for differences between the topological features—persistence landscapes (PLs) of denoised signals through resampling in the frequency domain. Simulation studies show that the test is robust for topologically similar signals while bearing sensitivity to topological tearing in signals. In an application to single-trial epileptic EEG signals, EEG signals in the diagnosed seizure origin and its symmetric site are found to have similar PLs before and during a seizure attack, in contrast to signals at other sites showing significant statistical difference in the PLs of the two phases.
CitationWang Y, Ombao H, Chung MK (2018) Topological data analysis of single-trial electroencephalographic signals. The Annals of Applied Statistics 12: 1506–1534. Available: http://dx.doi.org/10.1214/17-AOAS1119.
SponsorsSupported in part by NIH Brain Initiative Grant EB022856 Supported in part by NSF DMS, NSF SES and the KAUST Baseline Research Fund.
PublisherInstitute of Mathematical Statistics
JournalThe Annals of Applied Statistics