Fig. 1
From: A hybrid local-global neural network for visual classification using raw EEG signals
The overall architecture of our model for EEG classification. First, the input EEG signals are downsampled and epoched into trials. Baseline correction is an option step and no further preprocessing steps are applied. Next, raw EEG trials are encoded by Temporal and Spatial Convolutional Block. Then, the transformer block further extracts global-temporal features. Ultimately, EEG features are aggregated and classified into labels.
