An Ensemble Method for EEG-based Texture Discrimination during Open Eyes Active Touch

Abstract

Touch sensation is a key modality that allows humans to understand and interact with their environment. More often than not, touch sensation depends on vision to accumulate and validate the received information. The ability to distinguish between materials and surfaces through active touch consists of a complex of neurophysiological operations. To unveil the functionality of these operations, neuroimaging and neurophysiological research tools are employed, with electroencephalography being the most used. In this paper, we attempt to distinguish between brain states when touching different natural textures (smooth, rough, and liquid). Recordings were obtained with a commercially available EEG wearable device. Time and frequency-based features were extracted, transformed with PCA decomposition, and an ensemble classifier combining Random Forest, Support Vector Machine, and Neural Network was utilized. High accuracy scores of 79.64% for the four-class problem and 89.34% for the three-class problem (Null-Rough-Water) were accordingly achieved. Thus, the methodology's robustness indicates its ability to classify different brain states under haptic stimuli.