Computational Neural Projects
A curated collection of neural data analysis tools, BCI implementations, and computational models built for scientific rigor.
Wavelet-based feature extraction with K-means clustering optimized for low-latency GPU processing.
EEGNet architecture utilizing temporal and spatial convolutions for robust signal classification.
Collaborate on neural research?
Let's discuss how these computational models and neural pipelines can support your research goals.
A rigorous framework for computational precision.
Every project follows a structured, scientific progression designed to ensure code reliability, algorithmic efficiency, and reproducible research outcomes.
Neural Data Audit
Rigorous assessment of raw electrophysiology datasets and signal-to-noise ratios to define the computational scope.
Key Deliverables
- Signal Quality Analysis
- Data Pipeline Assessment
- Model Feasibility Report
Algorithm Architecture
Designing custom neural network topologies and signal processing chains optimized for low-latency performance.
Key Deliverables
- Neural Topology Design
- Latency Optimization Plan
- Computational Model Specs
Implementation & Testing
Developing modular codebases with strict unit testing against public neuroimaging benchmarks and standards.
Key Deliverables
- Modular Codebase Build
- Benchmark Validation Suite
- Automated Test Coverage
Deployment & Monitoring
Containerized deployment with continuous telemetry tracking to ensure stability and scientific accuracy.
Key Deliverables
- Containerized Deployment
- Telemetry Dashboard Setup
- Performance Monitoring
Open to research collaborations and technical consulting engagements.