Study of Auditory Brain Cognition Laws-Based Recognition Method of Automobile Sound Quality

The research shows that subjective feelings of people, such as emotions and fatigue, can be objectively reflected by electroencephalography (EEG) physiological signals Thus, an evaluation method based on EEG, which is used to explore auditory brain cognition laws, is introduced in this study. The br...

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Bibliographic Details
Published inFrontiers in human neuroscience Vol. 15; p. 663049
Main Authors Xie, Liping, Lu, Chihua, Liu, Zhien, Yan, Lirong, Xu, Tao
Format Journal Article
LanguageEnglish
Published Lausanne Frontiers Research Foundation 08.10.2021
Frontiers Media S.A
Subjects
Algorithms
Asymmetry
automobile sound quality
Automobiles
Brain
brain cognition laws
Brain research
Cognition & reasoning
Cognitive ability
Computer applications
EEG
Electrodes
Emotions
Experiments
Human Neuroscience
Implants
Kalman smoothing
Learning algorithms
Methods
mRMR
Neural networks
Physiology
Principal components analysis
Semantics
Sound
Online AccessGet full text
ISSN1662-5161
1662-5161
DOI10.3389/fnhum.2021.663049

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Summary:The research shows that subjective feelings of people, such as emotions and fatigue, can be objectively reflected by electroencephalography (EEG) physiological signals Thus, an evaluation method based on EEG, which is used to explore auditory brain cognition laws, is introduced in this study. The brain cognition laws are summarized by analyzing the EEG power topographic map under the stimulation of three kinds of automobile sound, namely, quality of comfort, powerfulness, and acceleration. Then, the EEG features of the subjects are classified through a machine learning algorithm, by which the recognition of diversified automobile sound is realized. In addition, the Kalman smoothing and minimal redundancy maximal relevance (mRMR) algorithm is used to improve the recognition accuracy. The results show that there are differences in the neural characteristics of diversified automobile sound quality, with a positive correlation between EEG energy and sound intensity. Furthermore, by using the Kalman smoothing and mRMR algorithm, recognition accuracy is improved, and the amount of calculation is reduced. The novel idea and method to explore the cognitive laws of automobile sound quality from the field of brain-computer interface technology are provided in this study.
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Reviewed by: Bradley Jay Edelman, Max Planck Institute of Neurobiology (MPIN), Germany; Seong-Eun Kim, Seoul National University of Science and Technology, South Korea
This article was submitted to Brain-Computer Interfaces, a section of the journal Frontiers in Human Neuroscience
Edited by: Jose Luis Contreras-Vidal, University of Houston, United States
ISSN:1662-5161
1662-5161
DOI:10.3389/fnhum.2021.663049