This open educational resource (OER) curriculum module delivers a mathematically grounded and clinically aligned foundation in Neural Engineering. Spanning the interface between electrophysiology, neural decoding algorithms, bio-materials, and hardware engineering, the module examines technologies designed to record, decode, and modulate nervous system activity.
Core topics include neural signal acquisition across scales (EEG, ECoG, intracortical microelectrodes), digital filtering and spike-sorting pipelines, deep brain stimulation (DBS), neuroprosthetic limbs, cochlear implants, and bio-electronic foreign body reactions. Classical biophysical electrodynamics are bridged to contemporary 2026 computational paradigms, featuring:- Physics-Informed Neural Networks (PINNs) solving quasi-static Poisson volume conduction equations across anisotropic cortical tissue.- Fourier Neural Operators (FNOs) and continuous-time spiking models for trajectory decoding.- Sub-milliwatt neuromorphic edge architectures for real-time epileptic biomarker detection.- A multi-step analytical calculation evaluating microelectrode geometric area, Johnson-Nyquist thermal noise, charge injection density, and the Shannon electrochemical safety boundary.- A structured systems engineering trade-off matrix analyzing channel density versus vascular shear trauma, wireless telemetry bandwidth versus cortical thermal rise (strictly < 1.0 °C), and invasive versus non-invasive communication rates.- An interactive digital signal processing canvas simulating band-pass filtering and threshold-based spike extraction from noisy neural recordings.- Three tiers of self-assessment modules covering foundational neurotechnology concepts, scenario-based ethical and clinical inquiries, and quantitative electrophysiological calculations with complete step-by-step solutions.
Permanent web resource: https://prep4uni.online/stem/physical-technologies/biomedical-engineering/neural-engineering/
