EEG-Infinity: A Mathematical Modeling-Inspired Architecture for Addressing Cross-Device Challenges in Motor Imagery

The distribution of electroencephalogram (EEG) data generally varies across datasets due to the huge difference between the physical structure of brain-computer interface devices, known as cross-device variability. Such variability poses great challenges in EEG decoding and hinders the standardized utilization of EEG datasets. In this study, we explore a new issue concerning the cross-device variability problem, pointing to the gap in the existing studies facing cross-device variability. To tackle this challenge, our paper is the first to model the cross-device variability problem through a “sequentially comprehensive formula” and a “spatial comprehensive formula”. Inspired by this modeling, a novel deep domain adaptation network named EEG-Infinity is proposed, incorporating replaceable EEG feature extraction backbones with a novel structure named “alignment head”. To show the effectiveness of the proposed EEG-Infinity, systematic experiments are conducted across four different EEG-based motor imagery datasets under 48 cases. The experimental results highlight the superior performance of the proposed EEG-Infinity over commonly used approaches with an average classification accuracy improvement of 1.51% across 34 cases, laying a foundation for research in large-scale EEG models.