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- W2184993078 abstract "In this work EEG signals decomposition, reconstruction and classification is achieved for the brain computer interface.EEG signals are most widely used in the medical field to analyse the patient condition, because it contains much information about human task. From several studies it has been suggested that EEG can be used to detect the severity of several diseases such as CJD, Alzheimer's, dementia and schizophrenia, in addition, specific events (known patterns in electrical stimulation) can indicate epileptic seizures. In general, EEG data analysis has been performed to either predict or classify signals. For the decomposition of EEG signals Discrete wavelet transform is observed and for the classification purpose KNN classifier, SVM classifier and LDA are observed. SVM classifier reaches to 100% efficiency. Index Terms—Electroencephalogram (EEG), Feature extraction, KNN classifier, LDA, SVM classifier, Wavelet decomposition, 10-20 electrode system —————————— ——————————" @default.
- W2184993078 created "2016-06-24" @default.
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- W2184993078 date "2013-01-01" @default.
- W2184993078 modified "2023-09-27" @default.
- W2184993078 title "Multi-Class EEG Classification for Brain Computer Interface" @default.
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