Decoding Music in the Human Brain Using EEG Data

Abstract

Semantic vectors, or language embeddings, are used in computational linguistics to represent language for a variety of machine related tasks including translation, speech to text, and natural language understanding. These semantic vectors have also been extensively studied in correlation with human brain data, showing evidence that the representation of language in the human brain can be modeled through these vectors with high correlation. Further, various attempts have been made to study how the human brain represents and understands music. For example, it has been shown that EEG data of subjects listening to music can be used for tempo detection and singer gender recognition. We propose studying the relationship between the EEG data of subjects listening to audio and the audio feature vectors modeled after the semantic vectors in computational linguistics. This could provide new insight into how the brain processes and understands music.