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.