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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

  • François B. Vialatte
  • Andrzej Cichocki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)

Abstract

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.

Keywords

Independent Component Analysis Independent Component Analysis Blind Source Separation Brain Computer Interface Sample Entropy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • François B. Vialatte
    • 1
  • Andrzej Cichocki
    • 1
  1. 1.BSI RIKEN ABSP Lab 2-1 HirosawaWako-ShiJapan

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