Novel EEG-Based Adaptive Color Palette Generation for Optimized Cognitive Task Performance in Spatial Reasoning

**Abstract:** This research introduces a novel framework for generating adaptive color palettes optimized for spatial reasoning tasks, leveraging real-time electroencephalography (EEG) data and Bayesian optimization. Unlike traditional approaches relying on static color combinations or limited user preference testing, our system dynamically adjusts color palettes based on an individual's cognitive state, measured through EEG signals, to maximize performance and minimize cognitive load during spatial navigation and manipulation tasks. The fundamental innovation lies in the integration of a recurrent neural network (RNN) for EEG signal denoising and feature extraction, coupled with a Bayesian optimization algorithm to iteratively refine color palettes influencing task completion time and accuracy. This promises significant improvements in user interfaces for spatial design, architectural planning, and augmented reality applications.