A hybrid quorum sensing model for neurodynamic feature optimization in EEG-based Parkinson's disease detection.

Parkinson's disease (PD) detection using electroencephalography (EEG) and deep learning(DL) has garnered significant attention due to the disease's complex nature and lack of definitive biomarkers. While imaging and invasive methods are expensive and unsafe, EEG allows for safe, low-cost, non-invasive, and portable data collection, and DL helps improve its ability to find subtle brain activity patterns. The extraction of novel features using DL yields high-dimensional data, which leads to added computational demands. This research proposes a method for feature selection that includes a novel multi-stage hybrid quorum sensing optimization (HQSO) algorithm. In the first stage, a coarse set of relevant features is selected. Quorum sensing is adopted as a performance-driven decision-making framework, where solutions adapt their behavior based on population-level fitness. A multilayer perceptron (MLP) is used to evaluate the fitness. In the second stage, a hybrid ranking mechanism statistically refines this set by prioritizing the most discriminative features, which are further reduced by a correlation-based pruning method to reduce feature redundancy. The proposed multi-stage hybrid feature selection method, integrating metaheuristic-based exploration with statistical refinement and correlation pruning, significantly improves PD detection performance. On the San Diego dataset, it achieves 98.09% accuracy, outperforming recent models like Rizvi et al. (97.90%) and matching top-tier models such as Khare et al. (100%). On the University of New Mexico dataset, it attains 94.96% accuracy, closely competing with the best-reported 99.9% by Shirisha et al. The reduction of features by almost 60% highlights its practicality for real-time, resource-constrained applications like wearable EEG-based monitoring systems.