  Affective brain-computer interfaces (aBCIs) are increasingly recognized for
their potential in monitoring and interpreting emotional states through
electroencephalography (EEG) signals. Current EEG-based emotion recognition
methods perform well with short segments of EEG data. However, these methods
encounter significant challenges in real-life scenarios where emotional states
evolve over extended periods. To address this issue, we propose a Dual
Attentive (DuA) transformer framework for long-term continuous EEG emotion
analysis. Unlike segment-based approaches, the DuA transformer processes an
entire EEG trial as a whole, identifying emotions at the trial level, referred
to as trial-based emotion analysis. This framework is designed to adapt to
varying signal lengths, providing a substantial advantage over traditional
methods. The DuA transformer incorporates three key modules: the
spatial-spectral network module, the temporal network module, and the transfer
learning module. The spatial-spectral network module simultaneously captures
spatial and spectral information from EEG signals, while the temporal network
module detects temporal dependencies within long-term EEG data. The transfer
learning module enhances the model's adaptability across different subjects and
conditions. We extensively evaluate the DuA transformer using a
self-constructed long-term EEG emotion database, along with two benchmark EEG
emotion databases. On the basis of the trial-based leave-one-subject-out
cross-subject cross-validation protocol, our experimental results demonstrate
that the proposed DuA transformer significantly outperforms existing methods in
long-term continuous EEG emotion analysis, with an average enhancement of
5.28%.
