Exploring EEG Spectral Dynamics of Emotional Responses in Music Appreciation
Date Issued
2011
Date
2011
Author(s)
Lin, Yuan-Pin
Abstract
Ongoing electroencephalogram (EEG) provides noninvasive measurement of brain activity with temporal resolution in milliseconds. The spectral dynamics of EEG has largely been used to investigate the neural activity engaged in brain functions, and also to characterize/detect function-related EEG patterns to construct brain-computer interfaces in different fields. Nowadays, since EEG might provide more insights into the processes and responses of perception and emotional experience during multimedia appreciation, the EEG-inspired multimedia research has been a growing research topic. The main concept is to assess multimedia content from users’ brain signal through interpreting the induced content-related responses. Toward this end, how to characterize user’s perception and emotional experience in response to multimedia from EEG is very crucial. However, the correspondence of EEG spectral dynamics and emotional responses and its feasibility used for emotion recognition have not been extensively studied yet.
This dissertation mainly focuses on exploring the EEG spectral dynamics of emotional responses in music appreciation. The overall objective of this dissertation is threefold for gaining more insight to the field of EEG-based emotion recognition. Firstly, this dissertation aims to construct an optimal machine learning approach to differentiate EEG patterns into distinct emotion states during music appreciation. Next, this dissertation is to adopt the independent component analysis (ICA), popularly used to analyze brain activity in neuroscience nowadays, to assess the emotional responses. Lastly, in order to provide new insights into the link between the changes in musical structures and the emotional perception, this dissertation is further to utilize ICA to investigate the underlying neural mechanisms which engage in musical mode and tempo perception.
Several findings significantly contribute to the field of EEG-based emotion recognition. First of all, this dissertation has systematically conducted certain of EEG feature extraction and classification methods trying to solve the emotion classification problem. The results showed that combining a feature type of spectral power asymmetry across multiple frequency bands with a classifier of support vector machine (SVM) was an optimal way for characterizing four emotional states (joy, anger, sadness and pleasure) with an average subject-dependent accuracy of 82.29% ± 3.06% across 26 subjects. A group of features extracted from the frontal and parietal lobes have been identified to provide discriminative information associated with emotion processing, which were insensitive to subject-variability and largely consistent with previous literature. Next, in contrast to feature-based classification approach, ICA was used to separate independent spectral changes of the EEG in response to music-induced emotional processes. An independent brain process with equivalent dipole located in the fronto-central region exhibited distinct delta-band and theta-band power changes associated with self-reported emotional states, which were less interfered by the activities from other brain processes complement previous EEG studies of emotion perception to music. Lastly, by applying ICA to multi-channel scalp EEG data, this dissertation further explored temporally independent brain sources that contribute to the perception of musical mode and/or tempo during natural music listening. Six brain processes with equivalent dipoles located at or near the medial frontal, right/left sensorimotor, superior parietal, medial parietal, and lateral occipital areas exhibited statistically significant spectral differences in response to changes in musical tempo and/or mode. These areas were consistent with those previously reported brain regions, obtained by other neuroimaging modalities, associated with changes in musical structures. More significantly, the delta-band and theta-band activities projected from the medial frontal region were also found associated to emotional valence and arousal processes.
In summary, this dissertation has successfully constructed an optimal EEG-based emotion recognition scheme based on feature-based classification approaches, but has evidently explored the EEG spectral dynamics associated to the emotional responses and musical structures (mode and tempo) based on neurocomputation approaches. All findings may facilitate the understating, improvement and optimization of the EEG-based emotion recognition research, but may get more fundamentals to implement a real-time emotion recognition system for multimedia applications in the near future.
Subjects
EEG
emotion recognition
independent component analysis
emotional responses
music appreciation
musical structure
Type
thesis
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