Just as music has the power to evoke a rainbow of emotions within us, our brain waves hold the key to decoding and understanding this musical language. In this review, we explore the fascinating field of music-emotion recognition and analysis based on EEG signals. Think of it like using a special decoder ring to decipher the secret emotional messages hidden within our brainwaves while we listen to different songs. By studying these neurophysiological signals, researchers have made strides in developing methods for analyzing music emotions, ranging from data processing techniques to building emotion models and extracting key features. As we navigate the exciting world of artificial intelligence, deep learning-based approaches are emerging as the go-to method for recognizing and interpreting the emotional landscapes of music. While EEG technology allows us to tap into the brain’s electrical activity non-invasively, thus making it a valuable tool for exploring emotions, there are still challenges to overcome and new frontiers to explore in the field of EEG-based music emotion recognition. Are you ready to uncover the science behind the music that moves us? Dive into the research to learn more!
Music plays an essential role in human life and can act as an expression to evoke human emotions. The diversity of music makes the listener’s experience of music appear diverse. Different music can induce various emotions, and the same theme can also generate other feelings related to the listener’s current psychological state. Music emotion recognition (MER) has recently attracted widespread attention in academics and industry. With the development of brain science, MER has been widely used in different fields, e.g., recommendation systems, automatic music composing, psychotherapy, and music visualization. Especially with the rapid development of artificial intelligence, deep learning-based music emotion recognition is gradually becoming mainstream. Besides, electroencephalography (EEG) enables external devices to sense neurophysiological signals in the brain without surgery. This non-invasive brain-computer signal has been used to explore emotions. This paper surveys EEG music emotional analysis, involving the analysis process focused on the music emotion analysis method, e.g., data processing, emotion model, and feature extraction. Then, challenging problems and development trends of EEG-based music emotion recognition is proposed. Finally, the whole paper is summarized.
Dr. David Lowemann, M.Sc, Ph.D., is a co-founder of the Institute for the Future of Human Potential, where he leads the charge in pioneering Self-Enhancement Science for the Success of Society. With a keen interest in exploring the untapped potential of the human mind, Dr. Lowemann has dedicated his career to pushing the boundaries of human capabilities and understanding.
Armed with a Master of Science degree and a Ph.D. in his field, Dr. Lowemann has consistently been at the forefront of research and innovation, delving into ways to optimize human performance, cognition, and overall well-being. His work at the Institute revolves around a profound commitment to harnessing cutting-edge science and technology to help individuals lead more fulfilling and intelligent lives.
Dr. Lowemann’s influence extends to the educational platform BetterSmarter.me, where he shares his insights, findings, and personal development strategies with a broader audience. His ongoing mission is shaping the way we perceive and leverage the vast capacities of the human mind, offering invaluable contributions to society’s overall success and collective well-being.