Temporal-Spatial Prediction: Pre-Training on Diverse Datasets for EEG Classification

Electroencephalogram (EEG) classification tasks have received increasing attention because its high application value. Meanwhile, the great success of general pre-training models in language processing areas inspires us to excavate the potential of an EEG pre-trained model. This model is expected to adapt to diverse downstream tasks. However, current studies either ignore the temporal or spatial domain in EEG signals, or only use single datasets in pre-training. The proposed Temporal-Spatial Prediction (TSP) model effectively solve these issues. Specifically, the output of the TSP encoder serves as the input of two tasks: spatial prediction, i.e., masked autoencoder, and temporal prediction, i.e, contrastive predictive coding. In addition, in order to provide more diverse information and thus benefit the downstream fine-tuning, we pre-train TSP on six large EEG datasets with four different numbers of channels. Results on three public downstream datasets SEED, SEED-IV, TUEV demonstrate that TSP achieves the state-of-the-art performance on different EEG classification tasks. In addition, according to the ablation experiments, TSP performs better than the single-domain method, i.e. Temporal Prediction (TP) model and Spatial Prediction (SP) model.