Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Fruit Fly Optimization based EEG Channel Selection Method for BCI

BCI 시스템을 위한 Fruit Fly Optimization 알고리즘 기반 최적의 EEG 채널 선택 기법

  • Yu, Xin-Yang (The School of Electrical Electronics Engineering, Chung-Ang University) ;
  • Yu, Je-Hun (The School of Electrical Electronics Engineering, Chung-Ang University) ;
  • Sim, Kwee-Bo (The School of Electrical Electronics Engineering, Chung-Ang University)
  • ;
  • 유제훈 (중앙대학교 공과대학 전자전기공학부) ;
  • 심귀보 (중앙대학교 공과대학 전자전기공학부)
  • Received : 2014.06.30
  • Accepted : 2015.12.28
  • Published : 2016.03.01
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Abstract

A brain-computer interface or BCI provides an alternative method for acting on the world. Brain signals can be recorded from the electrical activity along the scalp using an electrode cap. By analyzing the EEG, it is possible to determine whether a person is thinking about his/her hand or foot movement and this information can be transferred to a machine and then translated into commands. However, we do not know which information relates to motor imagery and which channel is good for extracting features. A general approach is to use all electronic channels to analyze the EEG signals, but this causes many problems, such as overfitting and problems removing noisy and artificial signals. To overcome these problems, in this paper we used a new optimization method called the Fruit Fly optimization algorithm (FOA) to select the best channels and then combine them with CSP method to extract features to improve the classification accuracy by linear discriminant analysis. We also used particle swarm optimization (PSO) and a genetic algorithm (GA) to select the optimal EEG channel and compared the performance with that of the FOA algorithm. The results show that for some subjects, the FOA algorithm is a better method for selecting the optimal EEG channel in a short time.

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References

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