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EEG Data Set Evaluation Based on Fuzzy Clustering for Higher Precision Classification

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.