2 papers
q-bio.NC2025
SSDLabeler: Realistic semi-synthetic data generation for multi-label artifact classification in EEG
Taketo Akama, Akima Connelly, Shun Minamikawa +1
EEG recordings are inherently contaminated by artifacts such as ocular, muscular, and environmental noise, which obscure neural activity and complicate preprocessing. Artifact clas…
cs.SD2025
Naturalistic Music Decoding from EEG Data via Latent Diffusion Models
Emilian Postolache, Natalia Polouliakh, Hiroaki Kitano +4
In this article, we explore the potential of using latent diffusion models, a family of powerful generative models, for the task of reconstructing naturalistic music from electroen…