Improved BCI calibration in multimodal emotion recognition using heterogeneous adversarial transfer learning.

ABSTRACT: The use of brain-computer interface (BCI) technology to identify emotional states has gained significant interest, especially with the rise of virtual reality (VR) applications. However, the extensive calibration required for precise emotion recognition models presents a significant challenge, particularly for sensitive groups such as children, elderly, and patients. This study presents a novel approach that utilizes heterogeneous adversarial transfer learning (HATL) to synthesize electroencephalography (EEG) data from various other signal modalities, reducing the need for lengthy calibration phases. We benchmark the efficacy of three generative adversarial network (GAN) architectures, such as conditional GAN (CGAN), conditional Wasserstein GAN (CWGAN), and CWGAN with gradient penalty (CWG

SUBMITTER: Sarikaya MA 

PROVIDER: S-EPMC11784743 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature