Research Portfolio
🎓 Master’s Thesis & Extracted Journal Papers
Developing Transformer-based Deep Learning model for Signal Decoding in BCI
Thesis Overview: Pioneered the development of neurophysiologically robust architectures to decode motor intent from EEG signals. This research exposed the "Clever Hans" effect in SOTA models and led to two major journal publications (Under Review at Biomedical Signal Processing and Control - BSPC):
- Paper 1: The Illusion of Accuracy in Rehabilitation BCI. An Explainable AI (XAI) investigation using Grad-CAM, Saliency Maps, and RSA to prove that high-performance CNNs often learn high-amplitude ocular artifacts (EOG) rather than true neural intent.
- Paper 2: A Robust Benchmark for Rehabilitation BCI. Validating Convolutional Transformers (CvT) on neurophysiologically isolated EEG signals to ensure safe and reliable intent decoding for clinical use.
🔬 Independent & Applied Research
Speech BCI: Imagined Speech Decoding
Independent Project: Developing an end-to-end Spectro-Temporal Transformer architecture to classify phonemes and words directly from EEG signals using the KARA ONE dataset. This work focuses on building robust silent communication interfaces independent of my thesis research.
Bone Age Estimation (Medical Computer Vision)
Implemented a Vision Transformer (ViT) for regression analysis on pediatric hand X-ray images. This project achieved a lower Mean Absolute Error (MAE) compared to traditional CNNs and was presented at the AIMS and IOAS conferences (Tehran & Germany).