Biologically Inspired Image Processing Techniques for Improved Resolution in FMRI Data

Functional Magnetic Resonance Imaging (fMRI) is a popular neuro-imaging technology that detects brain activity by monitoring blood oxygenation levels. However, fMRI suffers from limitations in temporal resolution due to the slow hemodynamic response, leading to challenges in accurately capturing rapid neural processes. To address this issue, this work proposed the use of Particle Swarm Optimization (PSO) method to enhance temporal resolution in fMRI data analysis. PSO is a nature-inspired optimization algorithm that efficiently explores high-dimensional search spaces to find optimal solutions. This approach refines the temporal resolution by identifying optimal time points and reducing noise, which enhances the interpretability of neural dynamics. Experimental results demonstrate that PSO-based temporal resolution enhancement improves the detection of cognitive and motor-related brain activity compared to conventional methods. This approach provides a significant step forward in fMRI analysis, offering better insights into fast neural events and opening avenues for more accurate brain-computer interfaces and clinical diagnostics.