Machine learning techniques for detecting motor imagery in upper limbs

Nowadays, the human machine interfaces have increased the applications for improving the quality of life in injured people. In spite of the progress in the field, new strategies are important to contribute to solve new problems. This proposal shows the employing of feature extraction in time and frequency domains. Three machine learning techniques as KNN, SVM and Random Forest were used to detect motor imagery from EEG signals. Comparison for feature extraction and the employed detection models were analyzed to find the best election in an application for close-open fist in hands. The results achieved more than 90% in accuracy for both approaches, showing as the frequency domain is preferable for feature extraction and the employment of the KNN classifier as best strategy for the present demand.