EEGNet is a compact convolutional neural network (CNN) designed for EEG-based brain-computer interfaces (BCIs). It uses depthwise and separable convolutions to efficiently extract EEG features while minimizing the number of model parameters. The model was evaluated across four BCI paradigms: P300 visual-evoked potentials, error-related negativity (ERN), movement-related cortical potentials (MRCP), and sensory motor rhythms (SMR). EEGNet demonstrated strong generalization across these paradigms, achieving comparable performance to existing models while being significantly more compact. It also showed effective performance on both event-related potential (ERP) and oscillatory-based BCIs. The model's features were visualized and analyzed to ensure interpretability, revealing neurophysiologically meaningful patterns. EEGNet's compact design and interpretability make it a promising solution for BCI applications, particularly when limited training data is available. The model's performance was validated across various datasets and classification tasks, showing robustness and effectiveness in different BCI paradigms. The results suggest that EEGNet can learn interpretable features across a range of BCI tasks, indicating that its performance is driven by meaningful neural signals rather than noise or artifacts. The model's architecture and performance have been documented and are available for further research and application.EEGNet is a compact convolutional neural network (CNN) designed for EEG-based brain-computer interfaces (BCIs). It uses depthwise and separable convolutions to efficiently extract EEG features while minimizing the number of model parameters. The model was evaluated across four BCI paradigms: P300 visual-evoked potentials, error-related negativity (ERN), movement-related cortical potentials (MRCP), and sensory motor rhythms (SMR). EEGNet demonstrated strong generalization across these paradigms, achieving comparable performance to existing models while being significantly more compact. It also showed effective performance on both event-related potential (ERP) and oscillatory-based BCIs. The model's features were visualized and analyzed to ensure interpretability, revealing neurophysiologically meaningful patterns. EEGNet's compact design and interpretability make it a promising solution for BCI applications, particularly when limited training data is available. The model's performance was validated across various datasets and classification tasks, showing robustness and effectiveness in different BCI paradigms. The results suggest that EEGNet can learn interpretable features across a range of BCI tasks, indicating that its performance is driven by meaningful neural signals rather than noise or artifacts. The model's architecture and performance have been documented and are available for further research and application.