Textural feature of EEG signals as a new biomarker of reward processing in Parkinson’s disease detection

Abstract

BackgroundParkinson’s disease (PD) detection holds great potential for providing effective treatments, slowing the disease process, and improving the quality of patient’s life, but the development of a clinical accurate, generalized, robust and cost-effective method is a challenge. MethodIn this paper, a novel PD detection method based on textural features of clinical electroencephalogram (EEG) signals has been proposed. In contrast to most existing methods, which do not consider reward positivity (RP)-relevant features for automatic PD detection, this method has focused on providing a novel EEG marker of RP using an enhanced time–frequency representation, texture descriptors based on Gray Level Co-occurrence Matrix, local binary pattern, and sparse coding classifier. ResultsThe proposed method has been evaluated using EEG signals recorded during a reinforcement-learning task from 28 patients with PD and 28 sex- and age matched healthy controls. Results have demonstrated that the proposed architecture reaches a high detection with an average accuracy rate of 100%, presenting better performance and outperforming previous techniques. Conclusionsit can provide a new solution to detect RP changes in PD and can offer obvious stability advantages on several clinical and technical variables (medication states, type of textural descriptors, reduced channels), suggesting a generalizable detection system.