Randomized trial demonstrates reconstruction of imagined melodies through relative pitch decoding in epilepsy patients, suggesting new pathways for understanding music imagery.
Music imagery involves internally generated auditory experiences, yet decoding imagined melodic content from neural activity remains challenging. Here, we examined whether imagined melodies can be decoded from human electrocorticography (ECoG) recordings using a relative pitch framework. Ten epilepsy patients performed a music imagery task involving familiar melodies presented in multiple tonalities (3 males and 7 females; mean age, 27.3 ± 4.69 years). Neural features from imagery-responsive cortical sites were used to train intra-subject models to decode relative pitch classes at the single-note level. Although single-note decoding accuracy was modest, it reliably exceeded chance. Sequential integration of decoded pitch classes enabled reconstruction of melodic contours that preserved the relative structure of imagined melodies. These results provide a proof-of-concept that imagined melodic structure can be accessed from intracranial neural recordings when framed in terms of relative pitch, underscoring the importance of sequence-level approaches for decoding internally generated auditory content. Significance Statement Imagining music is a rich internal experience, yet decoding the content of imagined melodies from brain activity remains a major challenge. Most previous studies have focused on detecting music imagery or decoding coarse musical features, rather than reconstructing melodic structure itself. In this study, we demonstrate that imagined melodies can be decoded from human intracranial brain recordings by representing melody in terms of relative pitch, a fundamental principle of musical perception. Although decoding individual imagined notes is difficult, combining note-level predictions over time allows recovery of melodic contours that preserve the structure of imagined songs. These results show that structured internal musical representations can be accessed from neural signals and suggest a principled pathway toward decoding complex imagined auditory content.
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Kwon et al. (2026) studied this question.
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