Electroencephalography Decoding with Conditional Identification Generator.

Abstract

Decoding Electroencephalography (EEG) signals are extremely useful for advancing and understanding human-artificial intelligence (AI) interaction systems. Recent advancements in deep neural networks (DNNs) have demonstrated significant promise in this respect due to their ability to model complex nonlinear relationships. However, DNNs face persistent challenges in addressing the inter-person variability inherent in EEG signals, which limits their generalizability. To tackle this limitation, we propose a novel framework that integrates conditional identification information, leveraging the interaction between EEG signals and individual traits to enhance the model's internal representation and improve decoding accuracy. Building on this foundation, we further introduce a privacy-preserving conditional information generator - a generative model that derives embedding knowledge directly from raw EEG signals. This approach eliminates the need for personal identification via individual tests, ensuring both efficiency and privacy. Experimental evaluations conducted on WithMe dataset confirm that this framework outperforms baseline network architectures. Notably, our approach achieves substantial improvements in decoding accuracy for both familiar and unseen subjects, paving the way for efficient, robust, and privacy-conscious human-computer interface systems.