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.

[1]  Igor Kononenko,et al.  ReliefF for estimation and discretization of attributes in classification, regression, and ILP probl , 1996 .

[2]  D. N. Tibarewala,et al.  Object-shape classification and reconstruction from tactile images using image gradient , 2012, 2012 Third International Conference on Emerging Applications of Information Technology.

[3]  Marko Robnik-Sikonja,et al.  Theoretical and Empirical Analysis of ReliefF and RReliefF , 2003, Machine Learning.

[4]  Olga Sourina,et al.  Real-Time EEG-Based Human Emotion Recognition and Visualization , 2010, 2010 International Conference on Cyberworlds.

[5]  Jinhai Cai,et al.  Hidden Markov Models with Spectral Features for 2D Shape Recognition , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  L. Jost.  Entropy and diversity , 2006 .

[7]  Amit Konar,et al.  Object-shape recognition from tactile images using a feed-forward neural network , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[8]  G. Pfurtscheller,et al.  Imagery of motor actions: differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG. , 2005, Brain research. Cognitive brain research.

[9]  Gert Pfurtscheller,et al.  Characterization of four-class motor imagery EEG data for the BCI-competition 2005 , 2005, Journal of neural engineering.

[10]  W Krause,et al.  Theta power in the EEG of humans during ongoing processing in a haptic object recognition task. , 2001, Brain research. Cognitive brain research.

[11]  B. Hjorth.  EEG analysis based on time domain properties. , 1970, Electroencephalography and clinical neurophysiology.

[12]  G. Pfurtscheller,et al.  Using adaptive autoregressive parameters for a brain-computer-interface experiment , 1997, Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136).

[13]  Amit Konar,et al.  Object shape recognition from tactile images using regional descriptors , 2012, 2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC).

[14]  Thomas Parisini,et al.  Shape from touch by a neural net , 1992, Proceedings 1992 IEEE International Conference on Robotics and Automation.

[15]  T. Ergenoğlu,et al.  Alpha rhythm of the EEG modulates visual detection performance in humans. , 2004, Brain research. Cognitive brain research.

[16]  S. Lemon,et al.  Classification and regression tree analysis in public health: Methodological review and comparison with logistic regression , 2003, Annals of behavioral medicine : a publication of the Society of Behavioral Medicine.