A comparative study of the performance of different spectral estimation methods for classification of mental tasks.
In this paper we compare three different spectral estimation techniques for the classification of mental tasks. These techniques are the standard periodogram, the Welch periodogram and the Burg method, applied to electroencephalographic (EEG) signals. For each one of these methods we compute two parameters: the mean power and the root mean square (RMS), in various frequency bands. The classification of the mental tasks was conducted with a linear discriminate analysis. The Welch periodogram and the Burg method performed better than the standard periodogram. The use of the RMS allows better classification accuracy than the obtained with the power of EEG signals.
Diez
Pablo F
PF
Departamento de Electrónica y Automática, Facultad de Ingeniería, Universidad Nacional de San Juan, Argentina. pdiez@gateme.unsj.edu.ar
Laciar
Eric
E
Mut
Vicente
V
Avila
Enrique
E
Torres
Abel
A
D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D003071:Cognition / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013647:Task Performance and Analysis; D014584:User-Computer Interface