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

TL;DR

This paper introduces a neural network-based method for decomposing single-channel EEG signals into shift-invariant wave components, improving performance and enabling pre-trained models for versatile brain signal analysis.

Contribution

The paper proposes a novel neural network approach for EEG decomposition that models signals as shift-invariant waves and introduces a pre-trained model for broad applicability.

Findings

Enhanced EEG decomposition performance across scenarios

Successful validation in brain-computer interface applications

Feasibility of a pre-trained, plug-and-play model

Abstract

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.