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Vialatte, F. B., Dauwels, J., Musha, T., & Cichocki, A. (2012). Audio representations of multi-channel EEG: A new tool for diagnosis of brain disorders. American Journal of Neurodegenerative Disease, 1(3), 292–304. 
Added by: sirfragalot (01/11/2018 01:01:42 PM)   Last edited by: sirfragalot (01/11/2018 01:12:32 PM)
Resource type: Journal Article
Peer reviewed
ID no. (ISBN etc.): 2165-591X
BibTeX citation key: Vialatte2012
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Categories: General, Sound Design
Keywords: Electroencephalograhy, Presence, Psychophysiology, Sonification
Creators: Cichocki, Dauwels, Musha, Vialatte
Collection: American Journal of Neurodegenerative Disease
Views: 3/118
"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."
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