A Smart Space Focus Enhancement System Based on Grey Wolf Algorithm Positioning and Generative Adversarial Networks for Database Augmentation

In the age of technological advancement, brainwave monitoring and attention tracking are critical for individual productivity and organizational efficiency. However, distractions pose significant challenges, making an effective brainwave monitoring and attention system essential. Generative Adversarial Networks (GANs) enhance medical datasets by synthesizing diverse samples. This paper explores their application in improving datasets for indoor positioning and brainwave monitoring-based attention tracking. The goal is to develop an intelligent lighting system that adjusts settings based on users’ brainwave states and positions. GANs enhance brainwave monitoring and positioning datasets, with Principal Component Analysis (PCA) applied for dimensionality reduction. machine learning and deep learning models train on these augmented datasets, enabling dynamic lighting adjustments to optimize user experience. GANs undergo parameter fine-tuning to improve dataset quality. Various classification models, including neural networks (NN), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Long Short-Term Memory (LSTM), are used for brainwave monitoring, attention, and positioning. Fuzzy logic enhances system stability. The trained models are integrated with hardware components, such as the Raspberry Pi 4, to implement an “Indoor Positioning Deep Learning Brainwave Monitoring and Attention Monitoring System Based on the Grey Wolf Optimizer Algorithm”. Experimental results demonstrate a positioning accuracy of 15 cm and significant improvements in brainwave monitoring and attention tracking.