State Classification in Free Sniffing Behavior of Beagle Dogs Based on Functional Connectivity Metrics

Dogs play a vital role in fields such as border control, drug detection, disaster rescue, and disease detection, owing to their exceptional olfactory system. However, traditional training methods are reliant on behavioral observation, which is inefficient and demands high skill from trainers. This study proposes a neural signal analysis approach based on brain-computer interface (BCI) technology to enhance training efficiency by analyzing the neural mechanisms underlying dogs' sniffing behavior. The Phase Locking Value (PLV) is used to quantify the phase characteristics of Electrocorticography (ECoG) in dogs under free-moving conditions. A scent classification model based on Bayesian optimization and AdaBoost ensemble learning is constructed. Experimental data demonstrate that this method effectively mitigates motion artifact interference and successfully classifies neural signals corresponding to two different scents. This research provides a theoretical foundation and technical approach for the development of a novel canine olfactory BCI system, offering significant practical value in reducing training costs for working dogs and expanding the application scenarios of olfactory detection.