Electroencephalogram (EEG) waveforms represent complex electrical fields generated by synchronized neural populations inside the cerebral cortex. Because biological systems exhibit inherently fuzzy borders, sharp, discrete boundaries are ill-suited for describing cognitive shifts. For example, standard medical protocols define the Alpha band strictly from 8 to 12 Hz. However, a biological oscillation at 7.9 Hz in a relaxed subject behaves structurally like an Alpha wave, even though it formally falls into the Theta domain.
To address these ambiguous biological boundaries, this interactive simulator implements a Mamdani-style Fuzzy Inference System (FIS). The inputs to the fuzzy engine are the synthetic EEG signal's Dominant Frequency and Root-Mean-Square Amplitude.
The frequency input spectrum is mapped using overlapping triangular ($\mu_{\text{tri}}$) and trapezoidal ($\mu_{\text{trap}}$) membership functions corresponding to standard neurological profiles:
The controller applies fuzzy logical conjunctions (AND) represented by the minimum operator over raw input memberships to evaluate human mental states:
The winning crisp state is dynamically highlighted on the dashboard interface based on the maximum product of frequency activation and corresponding signal power constraints.
An optional, integrated sonification tool utilizes standard Web Audio components to map biological frequency directly into acoustic patterns. By splitting stereo signals, a 150 Hz carrier wave is delivered to the left speaker, and a carrier offset by the exact EEG target frequency is delivered to the right channel. This produces a "Binaural Beat" within the brain stem, physically demonstrating the entrainment mechanism utilized in modern neurofeedback training.