High-resolution EEG acquisition and state mapping for measurable cognitive systems
Our EEG projects focus on high-fidelity neural signal acquisition using the Emotiv Flex 2.0 research-grade EEG system and EmotivPro platform. In parallel, we are developing a proprietary signal analysis and modelling environment that enables deeper feature extraction, adaptive state modelling, and closed-loop experimentation under full architectural control.
We build baseline-anchored pipelines that transform raw multi-channel EEG into reproducible features for state classification, stability tracking, and closed-loop system design.
Current work includes modelling how neural features shift across attention, affect, and cognitive load. Where relevant, we study behavioural and habit-loop dynamics as measurable state transitions—framed as signal and control problems, not narrative interpretation.
EEG is not just collection; it is instrumentation. We treat the dataset as a controllable system substrate: define a baseline, apply structured task conditions, capture state transitions, and quantify feature stability over time.
This work supports downstream programmes in neuromodulation and acoustic entrainment by providing a rigorous measurement layer. The objective is practical: reliable state detection, interpretable feature behaviour, and repeatable experimental design.
As these models mature, they form a signal architecture for human–machine interface research: detection, classification, and feedback under constraint.
We don’t just read the signal.
We engineer with it.
