Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/84710
Title: Audio representations of multi-channel EEG: a new tool for diagnosis of brain disorders
Authors: Vialatte, François B.
Dauwels, Justin
Musha, Toshimitsu
Cichocki, Andrzej
Keywords: Multichannel-EEG sonification
Time-frequency transform
Issue Date: 2012
Source: Vialatte, F. B., Dauwels, J., Musha, T., & Cichocki, A. (2016). Audio representations of multi-channel EEG: a new tool for diagnosis of brain disorders. American Journal of Neurodegenerative Disease, 1(3), 292-304.
Series/Report no.: American Journal of Neurodegenerative Disease
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 and Materials: 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.
URI: https://hdl.handle.net/10356/84710
http://hdl.handle.net/10220/41923
ISSN: 2165-591X
Rights: © 2012 AJND (published by e-Century Publishing Corporation). This paper was published in American Journal of Neurodegenerative Disease and is made available as an electronic reprint (preprint) with permission of e-Century Publishing Corporation. The published version is available at: [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3560465/]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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