Research Scientist (Neurotech) | Real-Time ML on Biosignals (EEG/EMG/ECG)
Research scientist specializing in brain-computer interfaces, real-time machine learning on biosignals, and neural interface technology. Currently Research Associate at the University of Glasgow.
- 🔬 Building BCI systems that enhance human capabilities
- 🤖 Developing time-series ML for EEG/EMG/ECG analysis
- ⚡ Creating low-latency, closed-loop neural interfaces
- 📊 Combining control engineering with computational neuroscience
AI-driven audio-neural interface that transforms speech into memorable formats using real-time EEG feedback. 40%+ memory improvement in clinical studies.
Establishing ethical timing thresholds for BCIs to preserve human sense of agency, autonomy, and legal responsibility in ultra-fast neurotechnology systems.
Closed-loop system integrating EEG, ECG, and EMG for sensorimotor decision-making with latency-optimized architecture.
Machine Learning & AI
- Time-series classification/regression, event detection, segmentation
- PyTorch, scikit-learn, TensorFlow
- Causal & streaming inference
Biosignal Processing
- EEG/EMG/ECG filtering & feature extraction
- Real-time signal processing (LSL integration)
- Artifact handling & source separation
Control Engineering
- Linear & nonlinear control systems
- System modeling & identification
- Hardware-in-the-loop (HIL/SIL) validation
Programming & Tools
- Python (NumPy, SciPy, pandas)
- MATLAB
- Git, Docker
- 🔬 Research Associate - University of Glasgow (2024 - Present)
- 🧠 MSc, Brain and Cognition - Pompeu Fabra University (2022-2023)
- ⚙️ PhD, Control Engineering - AUT, Iran (2009-2013)
- 🌍 Visiting Researcher - University of Groningen (2017-2018)
- 🌐 Website: hamedghanes.github.io
- 📧 Email: [email protected]
- 💼 LinkedIn: linkedin.com/in/hamedghane
- 🎓 Academic Profile: University of Glasgow