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Computational Intelligence and Neuroscience
Volume 2015, Article ID 858015, 8 pages
http://dx.doi.org/10.1155/2015/858015
Research Article

Comparison of Features for Movement Prediction from Single-Trial Movement-Related Cortical Potentials in Healthy Subjects and Stroke Patients

1Department of Health Science and Technology, Aalborg University, 9220 Aalborg, Denmark
2Center for Chiropractic Research, New Zealand College of Chiropractic, Auckland 1060, New Zealand
3Faculty of Health & Environmental Sciences, Health & Rehabilitation Research Institute, AUT University, Auckland 1060, New Zealand

Received 23 March 2015; Revised 29 May 2015; Accepted 1 June 2015

Academic Editor: Thomas DeMarse

Copyright © 2015 Ernest Nlandu Kamavuako et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Detection of movement intention from the movement-related cortical potential (MRCP) derived from the electroencephalogram (EEG) signals has shown to be important in combination with assistive devices for effective neurofeedback in rehabilitation. In this study, we compare time and frequency domain features to detect movement intention from EEG signals prior to movement execution. Data were recoded from 24 able-bodied subjects, 12 performing real movements, and 12 performing imaginary movements. Furthermore, six stroke patients with lower limb paresis were included. Temporal and spectral features were investigated in combination with linear discriminant analysis and compared with template matching. The results showed that spectral features were best suited for differentiating between movement intention and noise across different tasks. The ensemble average across tasks when using spectral features was (error = 3.4 ± 0.8%, sensitivity = 97.2 ± 0.9%, and specificity = 97 ± 1%) significantly better () than temporal features (error = 15 ± 1.4%, sensitivity: 85 ± 1.3%, and specificity: 84 ± 2%). The proposed approach also (error = 3.4 ± 0.8%) outperformed template matching (error = 26.9 ± 2.3%) significantly (). Results imply that frequency information is important for detecting movement intention, which is promising for the application of this approach to provide patient-driven real-time neurofeedback.