EEG Data Set Evaluation Based on Fuzzy Clustering for Higher Precision Classification
Zhong-Tao Xie, Jianguo Wang, Weiwei Deng, Qiuxuan Wu, Banghua Yang, Guo‐Liang Wang · 2018
One significant part of Electroencephalography (EEG) signal classification is data preprocessing. The traditional methods are hard to remove the noise and restore the original signal. In this paper, a novel method based on fuzzy c-means clustering is proposed for EEG data preprocessing. This novel method can make up for the lack of traditional methods and can evaluate whether a certain stage of data meets the requirements. After excluding the data that does not meet the requirements, the model classification effect has been significantly improved. The proposed method has achieved a good performance across the data from the BCI competition IV dataset I.