04 / Skills
Technical toolkit.
Multidisciplinary experience across neural engineering, biomedical signal processing, machine learning, and deep learning.
Biomedical Signal Processing
9+ years
Analysis of EEG (electroencephalography), ECG (electrocardiography), EOG (electrooculography), and EMG (electromyography) signals, including filtering, feature extraction, time-frequency analysis, and artifact removal.
Machine Learning
9+ years
Support vector machines, LDA (linear discriminant analysis), PCA (principal component analysis), ICA (independent component analysis), Kalman filters, reinforcement learning, and interpretable modeling.
Embedded Systems & IoT
11+ years
IoT (Internet of Things) development using Arduino, Raspberry Pi, OpenBCI hardware for BCI (brain-computer interface) applications, ROS (Robot Operating System), sensor integration, prototyping, and connected systems.
Python
10+ years
NumPy, SciPy, scikit-learn, MNE-Python (minimum-norm estimation in Python), data engineering, visualization, and PyQt application development.
MATLAB
10+ years
MATLAB (Matrix Laboratory) for signal processing, experimental paradigm scripting, visualization, EEGLAB, Brainstorm, and specialized engineering toolboxes.
PyTorch
4+ years
Convolutional and recurrent neural networks, LSTM (long short-term memory) models, attention mechanisms, training, and evaluation.
TensorFlow / Keras
4+ years
Deep-learning model development, training workflows, evaluation, and deployment.
Other Tools
11+ years
Bash (Bourne Again Shell), Git and GitHub, LaTeX, C/C++, Java, LabVIEW (Laboratory Virtual Instrument Engineering Workbench), high-performance computing, and Shapr3D CAD (computer-aided design).