Evaluation of the movement imagination training using the principal component analysis and magnitude-squared coherence as extractors of features

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 1
^st
, 2
^nd
, 3
^rd
, 4
^th
, 5
^th
and 6
^th
stages of training, respectively. Therefore., the results show the training increased the classifier hit rate using PCA and MSC as feature extractor.