Identification of task parameters from movement-related cortical potentials
The study investigates the accuracy in discriminating rate of torque development (RTD) and target torque (TT) (task parameters) from electroencephalography (EEG) signals generated during imaginary motor tasks. Signals were acquired from nine healthy subjects during four imaginary isometric plantar-flexions of the right foot involving two RTDs (ballistic and moderate) and two TTs (30 and 60% of the maximal voluntary contraction torque), each repeated 60 times in random order. The single-trial EEG traces were classified with a pattern recognition approach based on wavelet coefficients as features and support vector machine (SVM) as classifier. Average misclassification rates were (meanSD) 169% and 2613% for discrimination of the two TTs under ballistic and moderate RTDs, respectively. RTDs could be discriminated with misclassification rates of 1611% and 1910% under high and low TT, respectively. These results indicate that differences in both TT and RTD can be detected from single-trial EEG traces during imaginary tasks.
movement-related cortical potential support vector machine motor imagination braincomputer interface wavelet optimization classification