  In recent years, with the development of deep learning, electroencephalogram
(EEG) classification networks have achieved certain progress. Transformer-based
models can perform well in capturing long-term dependencies in EEG signals.
However, their quadratic computational complexity leads to significant
computational overhead. Moreover, most EEG classification models are only
suitable for single tasks, showing poor generalization capabilities across
different tasks and further unable to handle EEG data from various tasks
simultaneously due to variations in signal length and the number of channels.
In this paper, we introduce a universal EEG classification network named
EEGMamba, which seamlessly integrates the Spatio-Temporal-Adaptive
(ST-Adaptive) module, Bidirectional Mamba, and Mixture of Experts (MoE) into a
unified framework for multiple tasks. The proposed ST-Adaptive module performs
unified feature extraction on EEG signals of different lengths and channel
counts through spatio-adaptive convolution and incorporates a class token to
achieve temporal-adaptability. Moreover, we design a bidirectional Mamba
particularly suitable for EEG signals for further feature extraction, balancing
high accuracy and fast inference speed in processing long EEG signals. In order
to better process EEG data for different tasks, we introduce Task-aware MoE
with a universal expert, achieving the capture of both differences and
commonalities between EEG data from different tasks. We test our model on eight
publicly available EEG datasets, and experimental results demonstrate its
superior performance in four types of tasks: seizure detection, emotion
recognition, sleep stage classification, and motor imagery. The code is set to
be released soon.
