Single-channel electroencephalography decomposition by detector-atom network and its pre-trained model

Signal decomposition techniques utilizing multi-channel spatial features are critical for analyzing, denoising, and classifying electroencephalography (EEG) signals. To facilitate the decomposition of signals recorded with limited channels, this paper presents a novel single-channel decomposition approach that does not rely on multi-channel features. 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. •As consumer EEG devices become prevalent, single-channel analysis methods have been crucial but remain scarce.•We introduced a novel single-channel EEG decomposition method using a convolution model with a limited number of atoms.•We validated the method across various BCI and neuroscience scenarios, demonstrating robust and enhanced performance.•We developed a pre-trained decomposer enabling plug-and-play signal analysis, simplifying EEG measurement and analysis for consumer devices.