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International Journal of Information Technology & Computer Science ( IJITCS )

Abstract :

 Recently, there is an increasing interest in and research on human engineering and emotion engineering. As a basic  research on biofeedback interface technology, the development of a system for processing and modeling complex biomedical signals is very important, and these technologies will eventually offer a pleasant life environment, so the human-centered system based on biomedical signal analysis is the keyword of the future technology.  In this study, a biofeedback interface was designed to analyze biomedical signals (EEG, ECG) to recognize the user concentration and emotion state as well as effectively assessing the user intention. Compared with the existing interface technique using single biomedical signals, the proposed technology can analyze complex biomedical signals to make it easy to assess the  user state and intention and enhance the utilization thereof.

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References :

  1. Andre Ferreira, Wanderley C Celeste, Fernando A Cheein, Teodiano F  Bastos-Filho, Mario Sarcinelli-Filho, Ricardo Carelli “Human-machine interfaces based on EMG and EEG applied to robotic systems,” Journal of NeuroEngineering and Rehabilitation 2008.
  2. Axel Thielscher, Luiz Pessoa “Neural Correlates of Perceptual Choice and Decision Making during Fear–Disgust Discrimination,” The Journal of Neuroscience, pp.2908 –2917, 2007.
  3. H. Adeli, Z. Zhou, and N. Dadmehr, “Analysis of EEG records in an epileptic patient using wavelet transform,” J. Neurosci. Meth., vo.123, no.1, pp.69–87, 2003.
  4. N. Acir and C. Guzelis, “Automatic spike detection in EEG by a two stage procedure based on support vector machines,” Comput. Biol. Med., vo.34,  pp.561–575, 2004.
  5. R. Khosrowabadi, et al., “EEG-based Emotion Recognition Using Self-  Organizing Map for Boundary Detection,” Pattern Recognition (ICPR), 20th International Conference, pp.4242-4245, 2010.
  6. R.Acharya U, “Classification of cardiac abnormalities using heart rate  signals,” Medical & Biological Engineering & Computing, vo.42, pp.288-  293, 2004.
  7. Ting W, Guo-Zheng Y, Bang-Hua Y and Hong S, “EEG Feature Extraction Based on Wavelet Packet Decomposition for Brain Computer  Interface,” Transactions of the Institute of Measurement & Control, vo.41, pp. 618-625, 2008.
  8. Islam M, Fraz M.R, Zahid Z, Arif M, “Optimizing Common Spatial Pattern and feature extraction algorithm for Brain Computer Interface,” Emerging Technologies (ICET), pp.1-6, 2011.
  9. Axel Thielscher, Luiz Pessoa, “Neural Correlates of Perceptual Choice and Decision Making during Fear–Disgust Discrimination,” The Journal  of Neuroscience, pp.2908 –2917, 2007.
  10. Yong Zhao, Wenxue Hong, Yonghong Xu, Tao Zhang, “Multichannel Epileptic EEG Classification Using Quaternions and Neural Network,” Pervasive Computing Signal Processing and Applications (PCSPA), pp.568-571, 2010.
  11. Ito S.-I, Mitsukura Y, Jianting Cao, Fukumi M, “A design of the EEG feature detection and condition classification,” Annual Conference,   pp.2798-2803, 2007.

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