Earticle

초록

영어

Electroencephalogram (EEG) recording provides a new way to support human-machine communication. It gives us an opportunity to analyze the neuro-dynamics of human cognition. Machine learning is a powerful for the EEG classification. In addition, machine learning can compensate for high variability of EEG when analyzing data in real time. However, the optimal EEG electrode location must be prioritized in order to extract the most relevant features from brain wave data. In this paper, we propose an intelligent system model for the extraction of EEG data by training the optimal electrode location of EEG in a specific problem. The proposed system is basically a fuzzy system and uses a neural network structurally. The fuzzy clustering method is used to determine the optimal number of fuzzy rules using the features extracted from the EEG data. The parameters and weight values found in the process of determining the number of rules determined here must be tuned for optimization in the learning process. Genetic algorithms are used to obtain optimized parameters. We present useful results by using optimal rule numbers and non - symmetric membership function using EEG data for four movements with the right arm through various experiments.

목차

Abstract
1. Introduction
2. Background of Study
2.1. EEG (Electroencephalogram) Signal
2.2. Artificial Neural Network as a System Structure
2.3. Fuzzy Theory for a Rule-based Technology
2.4. Genetic Algorithm for Optimizing Parameters
2.5. Wavelets to be used for Feature Extraction
3. Design of an Intelligent Neural fuzzy System for EEG Classification
3.1. Preparing EEG Data Set for User’s Intention Recognition
3.2. The Proposed System Modeling using Neural Fuzzy Approach
3.3. EEG Signal Analysis for Feature Extraction
3.4. Membership Function
3.5. Fitness Function and Chromosome Expression
4. Experimental Results and Analysis for the User’s Intension Classification
4.1. Analysis of EEG Data
4.2. Determine of the Number of Fuzzy Rules by using Optimal Cluster Evaluation
4.3. Experimental Results according to Electrode Location
4.4. Comparison of Training Results with and without Wavelet Transform
4.5. Comparison of Training Results According to the Type of Membership Function
4.6. Comparison of Training Results according to Algorithm
4.7. The Hyper Parameters of Asymmetric Gaussian Membership Function after Training and Experiment Results
5. Conclusion
Acknowledgments
References

키워드

저자

• Chang Young Lee [ Major in Computer Engineering, Chonnam National University, Yeosu ]
• Ibrahim Aliyu [ Major in Computer Engineering, Chonnam National University, Yeosu ]
• Chang Gyoon Lim [ Major in Computer Engineering, Chonnam National University, Yeosu ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  통합자연과학논문집(구 조선자연과학논문집) [Journal of Integrative Natural Science]

• 간기

  계간

• pISSN

  2005-1042

• 수록기간

  2008~2025

• 등재여부

  KCI 등재

• 십진분류

  KDC 405 DDC 505