Biologically inspired mechanisms for enhancing robustness in EEG signal modeling: Challenges, opportunities, and perspectives.

The human brain maintains functional stability under changing conditions through interacting processes that include synaptic plasticity, homeostatic regulation, adaptive connectivity, and oscillatory dynamics. By contrast, electroencephalographic (EEG) recordings are highly non-stationary across sessions, individuals, and recording conditions. The resulting distribution shifts can impair model generalization and undermine the long-term reliability of brain-computer interface (BCI) systems. Machine learning (ML) and transfer learning (TL) approaches have improved cross-session and cross-subject decoding, but many depend on data-driven adaptation, often require recalibration, and do not explicitly model biological processes associated with neural adaptation and stability. This perspective-driven review examines how bio-inspired mechanisms, including synaptic plasticity, homeostatic regulation, neural oscillations, and spiking representations, could inform EEG models that are more robust to non-stationarity. This review synthesizes recent advances, critically compares bio-inspired methods with conventional ML and TL paradigms, and considers hybrid designs in which biologically grounded mechanisms complement artificial neural networks. To support clearer evaluation, the paper introduces operational definitions of bio-inspired, bio-plausible, and bio-realistic modeling; maps biological mechanisms to mathematical descriptions and computational modules; identifies evidence gaps and mechanism-specific limitations; and proposes a minimum specification for continual EEG benchmarks. Because direct EEG evidence remains limited for many proposed mechanisms, the review distinguishes empirically supported findings from hypotheses and future research directions. Together, this framework provides a testable roadmap for developing and evaluating adaptive EEG learning systems under realistic non-stationary conditions.