title: Audio representations of multi-channel EEG: a new tool for diagnosis of brain disorders.
abstract: OBJECTIVE
  The objective of this paper is to develop audio representations of electroencephalographic (EEG) multichannel signals, useful for medical practitioners and neuroscientists. The fundamental question explored in this paper is whether clinically valuable information contained in the EEG, not available from the conventional graphical EEG representation, might become apparent through audio representations.
  
  
  METHODS
  Music scores are generated from sparse time-frequency maps of EEG signals. Specifically, EEG signals of patients with mild cognitive impairment (MCI) and (healthy) control subjects are considered. Statistical differences in the audio representations of MCI patients and control subjects are assessed through mathematical complexity indexes as well as a perception test; in the latter, participants try to distinguish between audio sequences from MCI patients and control subjects.
  
  
  RESULTS
  Several characteristics of the audio sequences, including sample entropy, number of notes, and synchrony, are significantly different in MCI patients and control subjects (Mann-Whitney p < 0.01). Moreover, the participants of the perception test were able to accurately classify the audio sequences (89% correctly classified).
  
  
  CONCLUSIONS
  The proposed audio representation of multi-channel EEG signals helps to understand the complex structure of EEG. Promising results were obtained on a clinical EEG data set.
authors: Laboratoire Sigma, Ecole Supérieur de Physique et Chimie Industrielle de la ville de Paris (ESPCI ParisTech) 10 rue Vauquelin, 75231 Paris Cedex 05 ; Laboratory for Advanced Brain Signal Processing, RIKEN Brain Science Institute 2-1 Hirosawa, Saitama-Ken, Wako-Shi, 351-0198, Japan.
authors: François B
authors: FB
authors: Vialatte
authors: Justin
authors: J
authors: Dauwels
authors: Toshimitsu
authors: T
authors: Musha
authors: Andrzej
authors: A
authors: Cichocki
keywords: Alzheimer’s disease; EEG; Multichannel-EEG sonification; bump modeling; time-frequency transform
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