Study of Algorithms Classifiers for an Offline BMI Based on Motor Imagery of Pedaling
The paper compares different signal processing algorithms and classifiers to evaluate the accuracy of a BMI based on lower-limb motor imagery. The methods were based on the analysis of the peaks of the different processing epochs for the alpha, beta and gamma EEG bands through the Marginal Hilbert Spectrum, Power Spectral Density and Fourier harmonic components. Data were classified and analyzed by three classifiers: Support Vector Machine, Self-Organizing Maps and Linear Discriminator analysis. Results show accuracy is dependent on the subject, but there is not dependency between the subjects and the methods, and classifiers. Best accuracy results were achieved by using the value of the peak of the Hilbert Marginal Spectrum, obtaining the analytical signal with the Stockwell transform. Regarding the classifiers SOM presented lower accuracy values than SVM and LDA.
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