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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...
This study investigates the use of the Magnitude — Squared Coherence (MSC) to extract features from three events: spontaneous electroencephalogram (EEG), EEG-based motor task, and EEG-based motor imagination. We extracted such characteristics by using the Delta band (0.1–2 Hz), Alpha band (8–13 Hz) and Beta band (14–30 Hz). Tasks were classified by using Hidden Markov Models (HMM) and Multilayer Perceptron...
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