An Offline Affective Brain-Computer Interface Paradigm with CycleGAN Data Augmentation and Meta-Learning Update Mode

Data acquisition and system update are key parts of affective brain-computer interface (aBCI). Facing the issue of data scarcity, supplementing data from real samples of other subjects is a feasible solution. However, only data from subjects with emotional patterns (EP) similar to those of the user can be used for initializing and updating the aBCI. Therefore, we proposed an inter-subject data supplementation solution based on Cycle-Consistent Generative Adversarial Network (CycleGAN). With the introduction of EP metrics, information reconstruction and data augmentation networks can mitigate individual emotional pattern differences (IEPD). Based on these networks, a one-to-one inter-subject data sharing community was established. Even with data showing significant emotional pattern differences, it can still be transformed into data compatible with the user. Given that the system update mode for aBCI remains undefined, we proposed a meta-learning system update mode to learn the user's EP knowledge. Recursive meta-learning is employed to learn emotional information from a multi-task dataset. A temporary meta-learning paradigm for emotion recognition is designed to enable the system to adapt to unknown session tasks. After multi-session updates, an EP record system is used to summarize user-specific emotion-related feature patterns accumulated across sessions. The proposed paradigm is validated with a multi-session sequential learning process, yielding performances of 93.33 ± 7.00% on SEED, 77.08 ± 9.07% on SEED-FRA, and 71.87 ± 7.77% on SEED-GER. This research highlights the importance of offline assistive technologies, providing a novel perspective and an initial framework for the offline operational mechanisms of aBCI.