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- W4236049173 abstract "The detection of ALS disease is very critical due to control of nervous system. The nervous system cannot recognize all signal of physical behaviors of human body. For the monitoring of unusual physical behavior of human body identify with biomedical engineering process. Motor imagery EEG classification is the way to detection ALS disease and some other critical disease such as brain stroke and epilepsy. The electropherogram (EEG) is electric recoded signal stored in computer and converted into digital signal with A/D converter. The whole process of signal recoding proceeds by brain computer interface. The complex structure of EEG signal faced a problem of prediction of critical illness. In this paper proposed ensemble-based classifier for the prediction of critical disease. The extraction of features of EEG signals is also challenging task, for the extraction of features used wavelet transform function. The extracted features with transform are very-high dimension and noises. For the optimization of features applied BEE scout algorithm. The BEE scout algorithms reduce the unwanted component of features and provide better component of features for the classifier. The proposed algorithm simulated in MATLAB software and tested with BCI competition IV dataset." @default.
- W4236049173 created "2022-05-12" @default.
- W4236049173 creator A5008417967 @default.
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- W4236049173 date "2021-01-08" @default.
- W4236049173 modified "2023-09-26" @default.
- W4236049173 title "Feature Optimization of Motor Imagery EEG Classification using BEE Scout Algorithms and Machine Learning for ALS Disease Predication" @default.
- W4236049173 doi "https://doi.org/10.1109/icatme50232.2021.9732719" @default.
- W4236049173 hasPublicationYear "2021" @default.
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