Methods, systems, and apparatus implementing a generalizable self-calibrating protocol coupled with machine learning algorithms in an exemplary setting of classifying perceptual states as corresponding to the experience of perceptually opposite mental states (including pain or no pain) are disclosed. An embodiment presented represents inexpensive, commercially available, wearable EEG sensors providing sufficient data fidelity to robustly differentiate the two perceptually opposite states. Low-computational overhead machine learning algorithms that can be run on a mobile platform can be used to find the most efficient feature handles to classify perceptual states as self-calibrated by the user. The invention is generalizable to states beyond just pain and pave the way towards creating EEG NFB applications targeting arbitrary, self-calibrated perceptual states in at-home and wearable settings.
Methods, systems, and apparatus for self-calibrating EEG neurofeedback