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- W2024656057 abstract "This work investigates the Movement Imagination training using Principal Component Analysis (PCA) and Magnitude-Squared Coherence (MSC) as features extractor. The characteristics were extracted by using the Delta band (0.1-2 Hz), Alpha band (8-13 Hz) and Beta band (14-30 Hz) and the classifier was Multilayer Perceptron (MLP). Thus., the electroencephalogram (EEG) from five healthy subjects was recorded in the derivations C1, C2, C3, C4, C5, C6 and Cz (10-10 International System). The average hit rate in classification were 63.92 0/0, 71.31 0/0, 73.86 0/0, 83.31 0/0, 81.09 % and 93.43 % to 1st, 2nd, 3rd, 4th, 5th and 6th stages of training, respectively. Therefore., the results show the training increased the classifier hit rate using PCA and MSC as feature extractor." @default.
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- W2024656057 date "2013-02-01" @default.
- W2024656057 modified "2023-09-26" @default.
- W2024656057 title "Evaluation of the movement imagination training using the principal component analysis and magnitude-squared coherence as extractors of features" @default.
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- W2024656057 doi "https://doi.org/10.1109/brc.2013.6487525" @default.
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