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• 1. Wei L, Hu H, Yuan K. Use of forehead bio-signals for controlling an intelligent wheelchair. IEEE International Conference on Robotics and Biomimetics, 2008 22; 108:113. doi:10.1109/ROBIO.2009.4912988 2. Wei L, Hu H, Lu T, Yuan K. Evaluating the performance of a face movement based wheelchair control interface in an indoor environment. IEEE International Conf. on Robotics and Biomimetics, 2010 14; 387:392. doi:10.1109/ROBIO.2010.5723358 3. Jeong JW, Yeo WH, Akhtar A, Norton JJ, Kwack YJ, Li S, et al. Materials and Optimized Designs for Brain‐Machine Interfaces Via Epidermal Electronics. Advanced Materials. 2013; 25: 6839-6846. 4. Paul GM, Cao F, Torah R, Yang K, Beeby S, Tudor J. A smart textile based facial EMG and EOG computer interface. IEEE Sensors Journal. 2014; 14: 393-400. 5.Wei L, Hu H. A hybrid brain-machine interface for hands-free control of an intelligent wheelchair. International Journal of Mechatronics and Automation. 2011; 1: 97-111. 6. Rechy-Ramirez EJ, Hu H. Bi-modal human machine interface for controlling an intelligent wheelchair. IEEE Fourth International Conference on Emerging Security Technologies, 2013 9; 66-70. doi:10.1109/EST.2013.19 7. Costa A, Hortal E, Ianez E, Azorin JM. A supplementary system for a brain–machine interface based on jaw artifacts for the bidimensional control of a robotic arm. PLoS One. 2014; 9:e112352. 8. Linden M, Habib T, Radojevic V. A controlled study of the effects of EEG biofeedback on cognition and behavior of children with attention deficit disorder and learning disabilities. Biofeedback and self-regulation. 1996; 21: 35-49. 9. Azami H, Sanei S, Mohammadi K. A novel signal segmentation method based on standard deviation and variable threshold. Journal of Computer Applications. 2011; 34: 27-34. 10. Specht DF. Probabilistic neural networks. Neural Networks. 1990; 3: 109-118.