EEG Data Set Evaluation Based on Fuzzy Clustering for Higher Precision Classification
| Autor: | Weiwei Deng, Guo-Liang Wang, Qiuxuan Wu, Zhong-Tao Xie, Bang-Hua Yang, Jian-Guo Wang |
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| Rok vydání: | 2018 |
| Předmět: |
Fuzzy clustering
Noise (signal processing) business.industry Computer science 0206 medical engineering Pattern recognition 02 engineering and technology 020601 biomedical engineering Fuzzy logic Data modeling 03 medical and health sciences ComputingMethodologies_PATTERNRECOGNITION 0302 clinical medicine Data pre-processing Artificial intelligence Cluster analysis business 030217 neurology & neurosurgery |
| Zdroj: | 2018 37th Chinese Control Conference (CCC). |
| DOI: | 10.23919/chicc.2018.8482816 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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