EEG Signal Analysis Technology And Neural Mechanism Research About Music Emotion

The brain is the most complex organ in the human body.The study of the brain's neural mechanisms is the basis of artificial intelligence and an important way to realize artificial intelligence.Electroencephalogram(EEG)has been an important means for people to explore the brain since its discovery.Emotion is a kind of integrated state of a person.One of the signs that computers possess intelligence is the ability to recognize emotions.Analyzing and identifying emotions has become an important cross-disciplinary research topic that spans multiple fields.In this topic,the subject is asked to evoke the corresponding emotions through long-term music appreciation,and the EEG signals are recorded during the evoked course.Then we analyzed the EEG signals to analyze the brain recognition mechanism in the process of human music appreciation and used in emotion recognition to achieve the purpose of improving the recognition rate of emotion recognition.The work done in this paper mainly includes:Designed a brain-cognitive experiment based on long-term music to collect corresponding EEG data.Traditional brain-cognitive experiments are based on short-term sound signals,lacking EEG signals based on long-term music-induced emotions.Therefore,we designed the brain cognitive experiments by ourselves,to collected the corresponding EEG signals,and preprocessed the data,and constructed an EEG signal data set based on long-term music-induced emotions.It is difficult to analyze the EEG signals evoked by long-term music,we propose the concept of music event point so that event-related potential technology can be used to analyze the EEG signals evoked by long-term music.After statistics and cognitive science verification,we are convinced that we extract ERP components from EEG signals evoked by long-term music.Subsequently,we used this method to analyze the differences in ERP waveforms under different emotional states.In the process of music appreciation for a long period of time,the N1 and P2 components of ERP differ significantly in different emotional states.This shows that at the early stage of auditory emotions,the brain activities corresponding to different emotions are significantly different.Analyzed the time-frequency characteristics of EEG signals to explore the cognitive mechanisms of the brain during music appreciation and used it in actual emotion classification.We found that the main brain regions related to emotion inducing are the central area and the frontal area.The main emotion-related EEG frequency bands are delta band,theta band,alpha band,and gamma band.And feature extraction and optimization are performed according to relevant brain regions and frequency bands.Under the two classifications,the recognition rate of 71.2%.This is the highest recognition rate.Under the three classification problems,the recognition rate is also higher.Propose the concept of dynamic brain networks to analyze the EEG signals evoked by long-term music.The traditional brain network research neglected the real-time and dynamics of the brain.In the process of long-term music appreciation,the connectivity of the brain changes constantly.Therefore,the characteristics of the corresponding brain network will continue to change.We use mutual information to construct a dynamic brain network o and observe changes in the brain network over time.We found that through the dynamic brain network,emotions can be effectively divided.We use the brain network to classify emotions.Under the four categories,the emotion recognition rate reached 67.3%,exceeding the current highest recognition rate.Summarized the whole topic.This topic proposes music event and dynamic brain networks to analyze the EEG signals.Explores the law of brain cognition and applies the law of brain cognition to practical classification problems to achieve the effect of improving the recognition rate.After our verification,this research idea is indeed feasible.Increasing the computer's recognition rate through the brain cognitive mechanism is an important means of realizing artificial intelligence.