Biomedical Engineering MSc student exploring brain signals through computation, modeling, and biomedical data analysis.
- I’m especially interested in EEG signal processing, computational neuroscience, and how engineering methods can make brain activity more interpretable
- My work sits across biomedical data analysis, brain-computer interfaces, physiological modeling, and medical imaging
- I’m drawn to projects that connect signals, systems, and biological questions in a way that is both technical and readable
- EEG preprocessing and time-frequency analysis
- Brain-computer interface workflows and neural decoding
- Computational physiology and reduced-order modeling
- Machine learning methods for biomedical signals and medical imaging
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EEG-Emotion-Recognition
Research-oriented EEG emotion recognition with convolutional models, DEAP and SEED benchmarks, and Optuna-based hyperparameter search. -
EEG-Processing
An EEG analysis pipeline centered on preprocessing, ICA artifact removal, ERP structure, and time-frequency characterization. -
multi-scale-neural-dynamics
Multi-scale neural dynamics in Python, from single-neuron models to neural mass systems and associative networks. -
physiological-systems-modeling
A curated MATLAB portfolio of circulation, respiratory, gas-exchange, and feedback-control models in computational physiology. -
Medical-Imaging-Analysis
Medical imaging work in multimodal registration, level-set segmentation, and 3D reconstruction from MRI and DICOM data.
Languages & Core Tools
Scientific Python
ML / Deep Learning
Research Areas
GitHub: Parichehr13
LinkedIn: parichehr-moradi-82b0121a9