Sparse Bump Sonification: A New Tool for Multichannel EEG Diagnosis of Mental Disorders; Application to the Detection of the Early Stage of Alzheimer's Disease

摘要

This paper investigates the use of sound and music as a means of representing and analyzing multichannel EEG recordings. Specific focus is given to applications in early detection and diagnosis of early stage of Alzheimer's disease. We propose here a novel approach based on multi channel sonification, with a time-frequency representation and sparsification process using bump modeling. The fundamental question explored in this paper is whether clinically valuable information, not available from the conventional graphical EEG representation, might become apparent through an audio representation. Preliminary evaluation of the obtained music score - by sample entropy, number of notes, and synchronous activity - incurs promising results.