Clusterized KNN for EEG Channel Selection and Prototyping of Lower Limb Joint Torques

In this paper, a method for automatic channel selection of EEG signals acquired during the execution of lower limb movements is presented; for this method the hip and knee joint torques are measured. The method is based on maximizing both the percentage of prototypes extracted and the relative dispersion of its respective torques using a genetic algorithm. The prototyping is made with clusterized KNN, a proposed modification of the K-nearest neighbors algorithm, and the dispersion is computed as the ratio of interquartile ranges (IQR) between original and resulting torques. Results show that frequent channels are consistent with those known to be activated during motor tasks and that additional channels, needed for extracting relevant information from the data, vary from subject to subject. Extracted data can be used as new inputs for later regression tasks and for further analysis in order to characterize neural processes.