Welcome to my academic space! I am Armin Abdollahi, a researcher dedicated to bridging the gap between Computational Neuroscience and Deep Learning. My work focuses on making Brain-Computer Interfaces (BCIs) more reliable and interpretable using Explainable AI (XAI).

🌐 Academic Website | 💼 LinkedIn | 🎓 Google Scholar

• Thesis (20/20 Score): Developing Transformer-based models for EEG signal decoding.
• Explainable AI (XAI): Uncovering the "Clever Hans" effect in BCI models using Grad-CAM and RSA.
• Robust Architectures: Engineering Convolutional Transformers (CvT) for artifact-resistant neural decoding.
• Open Science: Translating Alexander Guger’s BCI textbook into Persian to support the local scientific community.

Neuro-specific tools: MNE-Python | EEGLAB | Optuna | Hugging Face

• BCI & HCI: Motor Imagery, ERP, and SSVEP decoding.
• Explainability: Interpretable Deep Learning for Bio-signals.
• Multimodal Data: Integrating EEG with fNIRS for hybrid interfaces.
• Optimization: Hyperparameter Tuning & Automated ML (AutoML).