Identification of task parameters from movement-related cortical potentials

作者：Gu Ying; do Nascimento Omar Feix; Lucas Marie Francoise; Farina Dario^*

来源：MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, 2009, 47(12): 1257-1264.

DOI：10.1007/s11517-009-0523-3

• Movement-related cortical potential
• Motor imagination
• Brain-computer interface
• Wavelet optimization
• Classification
• Support vector machine

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 (mean +/- A SD) 16 +/- A 9% and 26 +/- A 13% for discrimination of the two TTs under ballistic and moderate RTDs, respectively. RTDs could be discriminated with misclassification rates of 16 +/- A 11% and 19 +/- A 10% 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.

• 出版日期2009-12