Comparison of Features for Movement Prediction from Single-Trial Movement-Related Cortical Potentials in Healthy Subjects and Stroke Patients
Joint Authors
Kamavuako, Ernest Nlandu
Jochumsen, Mads
Niazi, Imran Khan
Dremstrup, Kim
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-06-16
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
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 (P<0.01) 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 (P>0.001).
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.
American Psychological Association (APA)
Kamavuako, Ernest Nlandu& Jochumsen, Mads& Niazi, Imran Khan& Dremstrup, Kim. 2015. Comparison of Features for Movement Prediction from Single-Trial Movement-Related Cortical Potentials in Healthy Subjects and Stroke Patients. Computational Intelligence and Neuroscience،Vol. 2015, no. 2015, pp.1-8.
https://search.emarefa.net/detail/BIM-1057770
Modern Language Association (MLA)
Kamavuako, Ernest Nlandu…[et al.]. Comparison of Features for Movement Prediction from Single-Trial Movement-Related Cortical Potentials in Healthy Subjects and Stroke Patients. Computational Intelligence and Neuroscience No. 2015 (2015), pp.1-8.
https://search.emarefa.net/detail/BIM-1057770
American Medical Association (AMA)
Kamavuako, Ernest Nlandu& Jochumsen, Mads& Niazi, Imran Khan& Dremstrup, Kim. Comparison of Features for Movement Prediction from Single-Trial Movement-Related Cortical Potentials in Healthy Subjects and Stroke Patients. Computational Intelligence and Neuroscience. 2015. Vol. 2015, no. 2015, pp.1-8.
https://search.emarefa.net/detail/BIM-1057770
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1057770