CEMOAE: A Dynamic Autoencoder with Masked Channel Modeling for Robust EEG-Based Emotion Recognition

Emotion recognition through electroencephalography (EEG) has been an area of active research, but the inherent sensitivity of EEG signals to noise and artifacts poses significant challenges, especially in real-world settings. These complications often necessitate the removal of corrupted channels, making it crucial to develop robust models capable of maintaining performance even when few channels are available. To address this, we propose the Corrupted EMOtion AutoEncoder (CEMOAE), an innovative approach that leverages masked channel modeling to maintain robust performance, achieved through three components: masked autoencoder pretraining for robust representation learning, random masked auxiliary task for implicit modeling of channel corruption, and masked auto-repair to explicitly narrow the data distribution gap between high-quality and corrupted EEG signals. Specifically, we first pretrain a masked autoencoder with the dynamic masking strategy for feature extractor initialization and channel recovery. During the finetuning stage, we mask EEG data using the auxiliary task to mimic real-world EEG corruption. We then employ the pretrained autoencoder to repair these signals and finetune the feature extractor for emotion recognition. Experiments on the SEED dataset demonstrate that CEMOAE achieves SOTA performance for emotion recognition under the random channel corruption simulation, validating the effectiveness of the proposed techniques.