Fig. 1
Model Architecture. This figure illustrates the architecture of the EEG classification model built using a customized transformer encoder. (a) Overview of the classification pipeline. Resting-state EEG data recorded with the eyes closed were preprocessed by applying bandpass and notch filters to remove low-frequency drifts and power line noise. Short-time Fourier transform (STFT) was subsequently applied to extract the time-frequency features. The resulting spectral features were input into the model, which outputted the class probabilities corresponding to the cognitive status (i.e., healthy or patients). (b) The internal structure of the transformer encoder. The model employs three parallel transformer encoder blocks, each consisting of a multi-head attention mechanism followed by a convolutional feedforward network using 1D convolutional layers. Dropout was applied at multiple stages to prevent overfitting, and layer normalization was applied to stabilize the training. ‘N’ denotes the batch size.
