Author : Fardin Shiraghae

Introduction and Objectives: The human brain has always been regarded as a complex system, and various scholars pay attention it from different aspects. Neuroscientists are seeking to discover the mechanisms of the nerves and find a model that can be used to explain the structure and function, such as perception and emotions related to the nervous system. Therefore, the present study was conducted with the aim of processing ECoG brain signals in order to predict fingers movement using adaptive neuro-fuzzy inference systems.
Materials and methods: The data used in this study consisted of the fourth data collection of the series BCI2008 tournament. In the case of using neuro-fuzzy inference system average classification accuracy of moving first, second, third, fourth and fifth fingers 89.8% obtained. In this process, because it requires signal percent to higher noise, the ECoG brain signals have been used. So, classification of brain signals at a frequency of 100-200 Hz and the characteristics of DCT, and using classification of networks of adaptive neuro fuzzy inference system were obtained.
Results: in the implementation, the best results was for classification of brain signals at a frequency of 100-200 Hz and the characteristics of DCT, and using classification of networks of adaptive neuro-fuzzy inference system were obtained.
Conclusion: as a result of improving the classification, a combination of genetic and fuzzy algorithms and PSO to improve the fuzzy model parameters used.

Keywords: Brain computer interface (BCI), brain signals ECoG, Adaptive Neuro Fuzzy Inference System (ANFIS)

Article published in International Journal of Current Engineering and Technology, Vol.7, No.5 (Sept/Oct 2017)