PhD Graduate in Automation Engineering EEG Signal Analysis | Graph Neural Networks | Deep Learning | Affective Computing

I work on EEG-based emotion recognition using graph neural networks, deep learning, and interpretable AI. My research focuses on modeling EEG functional connectivity, temporal dynamics, cross-subject variability, and cross-dataset transferability.

• EEG signal processing
• EEG-based emotion recognition
• Graph Neural Networks
• Deep learning and machine learning
• Affective computing
• Brain-computer interfaces
• Interpretable AI
• Mental health monitoring

Programming: Python, MATLAB, C++, LaTeX Frameworks: PyTorch, PyTorch Geometric, TensorFlow/Keras, scikit-learn Models: GCN, GAT, Graph Transformer, CNN, LSTM, Transformer EEG Analysis: filtering, segmentation, differential entropy, feature extraction, functional connectivity Tools: NumPy, Pandas, SciPy, Matplotlib, Overleaf, Origin

• H-GAT for EEG-based emotion recognition
• Multilayer-GTCN for physical and functional EEG connectivity
• Domain-adaptive EEG emotion recognition
• SA-DEEF for subject-adaptive EEG emotion recognition

• Email: atick.eee.uiu@gmail.com or atick@njust.edu.cn

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