Sparse Dynamical Features generation, application to Parkinson's Disease diagnosis. (arXiv:2210.11624v1 [eess.SY])

In this study we focus on the diagnosis of Parkinson's Disease (PD) based on
electroencephalogram (EEG) signals. We propose a new approach inspired by the
functioning of the brain that uses the dynamics, frequency and temporal content
of EEGs to extract new demarcating features of the disease. The method was
evaluated on a publicly available dataset containing EEG signals recorded
during a 3-oddball auditory task involving N = 50 subjects, of whom 25 suffer
from PD. By extracting two features, …

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