  This paper presents a novel single-channel decomposition approach to
facilitate the decomposition of electroencephalography (EEG) signals recorded
with limited channels. Our model posits that an EEG signal comprises short,
shift-invariant waves, referred to as atoms. We design a decomposer as an
artificial neural network aimed at estimating these atoms and detecting their
time shifts and amplitude modulations within the input signal. The efficacy of
our method was validated across various scenarios in brain--computer interfaces
and neuroscience, demonstrating enhanced performance. Additionally,
cross-dataset validation indicates the feasibility of a pre-trained model,
enabling a plug-and-play signal decomposition module.
