Decoding Emotions from Missing-Channel EEG Signals via Uncertainty-Aware Modeling with Neural Processes

Yan-Kai Liu, Xuan-Hao Liu, Yi-Dong Zhao, Bao-Liang Lu, Wei-Long Zheng

Abstract: Electroencephalography (EEG) signals have been widely utilized in affective brain-computer interface (BCI) applications. However, EEG signals are inherently susceptible to both external environmental noise and internal subject-specific variability, resulting in significant uncertainty. Moreover, in real-world scenarios, excessive external interference may severely degrade the quality of EEG recordings, leading to missing or corrupted channels. Therefore, developing robust emotion decoding methods that can handle incomplete EEG data is critical for enhancing the generalizability and reliability of affective BCIs. In this paper, we propose Fewer-Channel EEG Neural Processes (FC-EEGNP), a novel neural process-based framework designed to learn the uncertain relationships between EEG signals and brain regions, enabling emotion recognition under missing-channel conditions. FC-EEGNP reconstructs high spatial resolution EEG representations from a limited subset of available channels, preserving critical emotional information despite data sparsity. We conduct extensive experiments on three publicly available EEG datasets to evaluate the performance of FC-EEGNP. Results demonstrate that our model consistently achieves state-of-the-art performance across multiple emotion recognition settings, including subject-dependent and cross-subject tasks.