Enhancing Brain-Computer Interface Algorithm Performance via Neurophysics and Cognitive Neuroscience

This narrative review explores the enhancement of Brain-Computer Interface (BCI) algorithm performance through the integration of neurophysics and cognitive neuroscience principles. It examines the physical foundations of neural signal generation and propagation alongside cognitive factors such as attention, intention, and mental states that influence signal variability. The review discusses key theoretical frameworks including predictive coding and Bayesian brain models that inform adaptive and personalized decoding algorithms. Historical milestones and recent advancements in BCI technology are highlighted, emphasizing the shift from invasive to non-invasive techniques, the rise of deep learning, and the development of hybrid paradigms that combine multiple neural data sources. Critical challenges such as signal variability, non-stationarity, and ethical considerations are addressed alongside emerging solutions involving adaptive machine learning and user-centric designs. The review further presents real-world applications of enhanced BCIs in healthcare, education, and industry, showcasing their potential to improve neuroprosthetics, cognitive monitoring, and human-machine interaction. Despite significant progress, gaps remain in understanding higher-order cognitive modulation of brain signals and in developing algorithms resilient to inter-individual variability. Future research directions include deepening personalized neurophysiological markers, expanding cognitive state integration, and considering ethical frameworks to facilitate broader practical adoption. This comprehensive synthesis of interdisciplinary insights aims to guide further innovation toward robust, accurate, and user-responsive BCI systems with transformative societal impact.

Keywords:

adaptive algorithms , brain-computer interfaces , cognitive neuroscience , neurophysics , predictive coding , signal variability