Hybrid Vision Transformer and MLP-Mixer for Epileptic Seizure Detection in Intracranial EEG

Accurate and timely seizure detection is essential for effective epilepsy management, and automated systems can play a valuable role in supporting clinical practice. In this study, we introduce a hybrid approach that uses time-frequency representations of Intracranial electroencephalography (iEEG) signals filtered at High-Frequency Oscillations (HFOs) bands as input to different convolutional neural network (CNN) backbones for feature extraction, followed by classification with either a Vision Transformer (ViT) or MLP-Mixer. This work establishes a systematic, comparative framework for benchmarking hybrid CNN-ViT against CNN-MLP-Mixer, providing a critical new reference for automated epileptic seizure detection within HFOs filtered iEEG signals. Extensive evaluation demonstrates that the ViT consistently achieves superior performance, with an EfficientNetB0-ViT model attaining remarkable accuracy (97.85%) and specificity (98.92%). Crucially, the MLP-Mixer emerges as a highly competitive alternative, exhibiting strong recall capabilities that make it suitable for applications where missing a seizure is not an option. Overall, our findings suggest that self-attention mechanisms in ViTs provide a distinct advantage for capturing complex seizure dynamics, yet MLP-based models present a powerful, efficient option.