In this work an experimental methodology was developed using OpenBCI for the acquisition of EEG signals from 24 volunteer subjects. The volunteers are work and research colleagues from ESPOL University.
EEG signal preprocessing techniques are used to eliminate noise components. In addition, feature extraction algorithms were used to extract features from each EEG electrode. Next, some classifiers were trained and those with the highest accuracy were selected. Finally, the best classifiers were used to classify motor tasks in real-time and control two active prostheses.
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