QCBO‐WSVM: Quantum chaos butterfly optimization‐based weighted support vector machine for neuropathic pain detection from EEG signal

Abstract

AbstractChronic pain is a common problem among stroke patients, resulting from neurological damage to the central nervous system. This discomfort is primarily caused by the improper use of unaffected limbs or musculoskeletal issues. It can be challenging to differentiate neuropathic pain resulting from central nervous system damage. To address these challenges, researchers have developed a cutting‐edge technology called a Brain‐Computer Interface (BCI) based on electroencephalogram (EEG) data. In this paper, a novel BCI classifier has been developed using the Weighted Incremental‐Decremental Support Vector Machine (WIDSVM) classification method. The classifier has been trained using EEG‐based motor images from patients with central nervous system damage. The Quantum Chaos Butterfly Optimization Algorithm (QCBOA) has been used to enhance the performance of the WIDSVM classifier by creating a new dataset. The efficiency of the proposed model has been evaluated by comparing the results obtained from normal participants and those who developed chronic pain. The classification accuracy has been calculated for different regions, including the left hand, right hand, and feet, among the different participant groups. A total of 28 participants have been separated into three groups with pain in different regions, such as the lower abdomen and legs. The classifier has been tested using both 3‐channel bipolar montages and Common Spatial Patterns (CSPs). The results have shown that the proposed model offers higher classification accuracy and statistical significance in identifying the patient's risk of developing central neuropathic pain. However, it is important to note that further studies with larger sample sizes and different types of chronic pain are needed to validate the efficacy of the proposed model.