**7. Conclusion**

We have proposed a BCI system embedding neuro-fuzzy prediction in feature extraction in this work. The results demonstrate the potential for the use of neuro-fuzzy prediction together with support vector machine in MI classification. It also shows that the proposed system is robust for the inter-subject use under careful parameter training, which is important for BCI applications. Compared with other well-known approaches, neuro-fuzzy prediction together with SVM achieves better results in BCI applications. In future works, more effective prediction/features and powerful classifiers will be used to further improve classification results.

Neuro-Fuzzy Prediction for Brain-Computer Interface Applications 311

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The author would like to express his sincerely appreciation for grant under shared facilities supported by the Program of Top 100 Universities Advancement, Ministry of Education, Taiwan.

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The author would like to express his sincerely appreciation for grant under shared facilities supported by the Program of Top 100 Universities Advancement, Ministry of Education,

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**Section 5** 

**Application to Power System** 

**Engineering Problems** 

