Decoding Three-Dimensional Trajectory of Executed and Imagined Arm Movements From Electroencephalogram Signals

• Published in: IEEE transactions on neural systems and rehabilitation engineering 2015-09, Vol.23 (5), p.867-876
• Main Authors: Kim, Jeong-Hun, Biessmann, Felix, Lee, Seong-Whan
• Format: Article
• Language: English
• Subjects: Adult; Algorithms; Arm - physiology; Arm movement trajectory; Brain Mapping - methods; Brain-Computer Interfaces; brain-computer interfaces (BCI); Contamination; Decoding; Elbow; Electroencephalography; electroencephalography (EEG); Electroencephalography - methods; Electrooculography; Evoked Potentials, Motor - physiology; Humans; Imagination - physiology; kernel ridge regression; Male; Motor Cortex - physiology; Movement - physiology; Pattern Recognition, Automated - methods; Reproducibility of Results; Robots; Sensitivity and Specificity; Trajectory; upper limb rehabilitation
• ISSN: 1534-4320; 1558-0210
• DOI: 10.1109/TNSRE.2014.2375879

Decoding motor commands from noninvasively measured neural signals has become important in brain-computer interface (BCI) research. Applications of BCI include neurorehabilitation after stroke and control of limb prostheses. Until now, most studies have tested simple movement trajectories in two dimensions by using constant velocity profiles. However, most real-world scenarios require much more complex movement trajectories and velocity profiles. In this study, we decoded motor commands in three dimensions from electroencephalography (EEG) recordings while the subjects either executed or observed/imagined complex upper limb movement trajectories. We compared the accuracy of simple linear methods and nonlinear methods. In line with previous studies our results showed that linear decoders are an efficient and robust method for decoding motor commands. However, while we took the same precautions as previous studies to suppress eye-movement related EEG contamination, we found that subtracting residual electro-oculogram activity from the EEG data resulted in substantially lower motor decoding accuracy for linear decoders. This effect severely limits the transfer of previous results to practical applications in which neural activation is targeted. We observed that nonlinear methods showed no such drop in decoding performance. Our results demonstrate that eye-movement related contamination of brain signals constitutes a severe problem for decoding motor signals from EEG data. These results are important for developing accurate decoders of motor signal from neural signals for use with BCI-based neural prostheses and neurorehabilitation in real-world scenarios.