A pain assessment method based on decoding local field potential signals

Pain is a highly subjective sensation. To achieve more accurate assessment of pain signals in clinical settings, this study proposes a pain assessment method based on time-frequency-space features (PA-TFS). Through pain experiments on rats and recording their local field potential (LFP) signals, data from four rats were collected. The study involves steps such as filter bank analysis, time window decomposition, extraction of spatiotemporal features, and machine learning classification to decode pain signals. Experimental results demonstrate that the PA-TFS method exhibits the highest accuracy in decoding pain signals, showing significant advantages over other methods. Additionally, the study explores the impact of data collection time and trial quantity on decoding performance, revealing that the PA-TFS method maintains high accuracy under different time and trial quantities. This research provides a new method and perspective for the quantitative assessment of pain signals, with promising clinical applications.