Performance enhancement of object shape classification by coupling tactile sensing with EEG

In this work we establish the fact that using Electroencephalogram (EEG) with tactile signal during dynamic exploration accomplishes object shape recognition better than using the either alone. Adaptive auto-regressive coefficients and Hjorth parameters are used as features which are classified using linear Support Vector Machine, Naïve Bayes, k-nearest neighbor and tree classifiers. Following this, the space complexity to store the high-dimensional tactile features is identified. ReliefF algorithm is used as a dimension reduction technique. A polynomial of order 6 is used to fit an EEG feature to a corresponding tactile feature. These pre-fitted polynomials are used to predict the EEG features in situation where EEG measuring device is not present. Finally we note that using these predicted features along with the tactile features yields enhanced classification accuracies.