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

Kamavuako EN, Jochumsen M, Niazi IK, Dremstrup K - Comput Intell Neurosci (2015)

Bottom Line: 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 proposed approach also (error = 3.4 ± 0.8%) outperformed template matching (error = 26.9 ± 2.3%) significantly (P > 0.001).

View Article: PubMed Central - PubMed

Affiliation: Department of Health Science and Technology, Aalborg University, 9220 Aalborg, Denmark.

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 (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.

No MeSH data available.


Related in: MedlinePlus

Feature space representation using 2 discriminative features after Fisher projection. Features are normalized between −1 and 1 for the sake of clarity for the worst subject at each task. Square represents noise, black circles represent s20, and gray circles represent f60.
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fig5: Feature space representation using 2 discriminative features after Fisher projection. Features are normalized between −1 and 1 for the sake of clarity for the worst subject at each task. Square represents noise, black circles represent s20, and gray circles represent f60.

Mentions: Figures 4 and 5 show the two discriminative features when each feature type is projected using Fisher discrimination projection considering three classes (f60, s20, and noise) for the best subject and worst subject, respectively.


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

Kamavuako EN, Jochumsen M, Niazi IK, Dremstrup K - Comput Intell Neurosci (2015)

Feature space representation using 2 discriminative features after Fisher projection. Features are normalized between −1 and 1 for the sake of clarity for the worst subject at each task. Square represents noise, black circles represent s20, and gray circles represent f60.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4487719&req=5

fig5: Feature space representation using 2 discriminative features after Fisher projection. Features are normalized between −1 and 1 for the sake of clarity for the worst subject at each task. Square represents noise, black circles represent s20, and gray circles represent f60.
Mentions: Figures 4 and 5 show the two discriminative features when each feature type is projected using Fisher discrimination projection considering three classes (f60, s20, and noise) for the best subject and worst subject, respectively.

Bottom Line: 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 proposed approach also (error = 3.4 ± 0.8%) outperformed template matching (error = 26.9 ± 2.3%) significantly (P > 0.001).

View Article: PubMed Central - PubMed

Affiliation: Department of Health Science and Technology, Aalborg University, 9220 Aalborg, Denmark.

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 (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.

No MeSH data available.


Related in: MedlinePlus