Analysis of the correlation between the human voice and brain activity

Abstract

In this paper, we investigated the coupling among the alterations of brain activity and the rhythmic pattern of voice. We benefited from complexity and information concepts and ran the analysis on EEG and voice (audio) signals using sample entropy and Shannon entropy. To change brain activity, we applied four different odors with different complexities on ten subjects (5 M, 5 F). Accordingly, subjects’ voice was changed, and therefore, we evaluated the changes in EEG versus voice signals by calculating their Shannon entropy and sample entropy. The obtained results showed that the variations of complexity (r = 0.8659) and the information content (r = 0.9423) of voice and EEG signals are strongly correlated. This method can be utilized to evaluate the coupling of other biosignals versus brain activity.

KEYWORDS:

• Brain
• voice
• EEG signals
• odors
• information
• complexity
• sample entropy
• Shannon entropy

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported in part by the project (2021/2204), Grant Agency of Excellence, University of Hradec Kralove, Faculty of Informatics and Management, Czech Republic.