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

Abstract

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.

Review of Studies on Emotion Recognition and Judgment Based on Physiological Signals

“…Priyasad et al [40] proposed a new method to recognize emotion through unprocessed EEG signals, and it was found that, with the new method, the classification accuracy for arousal, valence and dominance could be higher than 88%. Xie et al [41] presented an assessment approach based on EEG signals. They first determined the brain cognition laws by evaluating the EEG power topographic map under the effect of three types of automobile sound, i.e., the qualities of comfort, powerfulness, and acceleration, then classified the EEG features thus recognized as different automobile sounds, and finally employed the Kalman smoothing and minimal redundancy maximal relevance algorithm to modify the accuracy and reduce the amount of calculation.…”

Emotion recognition based on multi-modal physiological signals and transfer learning

“…In the aspect of emotion recognition, electroencephalogram (EEG) has been paid more attention by researchers among many physiological signals. The analysis of EEG signals in the field of emotion recognition depends on data preprocessing, feature extraction, and feature classification ( Xie et al, 2021 ). Many researchers use traditional machine learning or deep neural network to classify EEG signals by extracting the energy features of the delta, theta, alpha, beta, and gamma bands.…”