A bio-engineering sandbox demonstrating real-time fuzzy logic inference. Adjust cortical band amplitudes to simulate EEG signals and watch the inference engine classify signal quality dynamically.
Electroencephalogram (EEG) instrumentation records human neuro-electric activity across multiple frequency bands. However, clean clinical data is challenging to maintain due to constant interference from muscle tension, cardiac rhythms, eye blinks, and ambient electromagnetic line noise.
Traditional signal processors struggle to categorize overall signal quality using strict numerical cut-offs, because physiological waves inherently vary across populations. This application replaces hard-coded parameters with a professional-grade Fuzzy Logic Inference Engine.
The signal evaluator monitors three metrics transformed via custom trapezoidal membership functions:
An inference engine evaluates nine clinical rules using min-max composition. It calculates the centroid defuzzification score across 101 discrete points, providing an Interaction-to-Next-Paint (INP) response time of under 100ms.
The development roadmap includes the following capabilities:
Access curated open-source datasets, signal libraries, and academic foundations to deepen your understanding of neuroscience systems and fuzzy control architecture.
Access official datasets, diagnostic software directories, biological models, and system engineering modules.
An open-source MATLAB software suite for biological dataset analysis, filtering, and event-related potential processing.
Advanced community-driven Python suite used to process, visualize, and classify multi-channel EEG, MEG, and clinical signal data.
Official portal for the Engineering in Medicine and Biology Society. Standard guidelines for biosignal processing.