Self-Concentration Detection Based on Doubled Amplitude/Phase Processing in Node PDE Modular Models

Reliable classification of brain sequence cases is a challenging problem due to signal ambiguity and noise. Personal concentration is primarily determined by the base frequency of electroencephalogram (EEG) waves, i.e., the task rests on appropriate modelling and recognition of patterns in the corresponding human (in)activity (e.g., reading, relaxation, solving maths problems, etc.). Five underlying types of frequency (alpha, beta, gamma, delta, and theta) were considered as secondary input wave parameters in complex-valued node extensions to the prime amplitude in processing signals. Self-optimisable Artificial Intelligence (AI) methods can process, statistically analyse, and model the series-specific character and time behaviour to recognise untrained session assigned labels. This procedure involves signal pre-processing (transformation) and feature extraction to enhance the representation in time variability, eliminate uncertain cases, and reduce the unacceptable large raw format of data in detailed frequency band recording. This study focuses on improving AI modelling through brain-inspired doubled amplitude/frequency signal processing. This extended concept is based on an analogy with neural activity that generates dynamic frequency pulses as the main information holder in response to time excitations. The model is obtained in partial differential equation (PDE) solutions of evolutionary tree structure nodes—self-computational terms, using the optimal sine/cosine or rational expression. It enables the representation of periodic patterns in their intrinsic form related to primary wave characteristics. Two different machine learning methodologies were compared: the first evolutionary PDE transform and deep learning-based recurrent processing applied to all session records in bloc, assessing only one final class assessment, achieving predictive accuracy above 90% on untrained 1/3 data. The second group of regular modelling techniques evaluates each data row separately to compute its bound-label output in a time-lagged frame, reaching accuracy above 70%. An executable parametric software with a link to the public EEG data repository is available.