Detection of Intention Level in Response to Task Difficulty from EEG Signals. Koyas, E., Hocaoglu, E., Patoglu, V., & Cetin, M. In IEEE International Workshop on Machine Learning for Signal Processing (MLSP2013), 2013. abstract bibtex We present an approach that enables detecting intention levels of patients in response to task difficulty utilizing an electroencephalogram (EEG) based Brain-Computer Interface (BCI). In particular, we use Linear Discriminant Analysis (LDA) to classify event-related synchronization (ERS) and desynchronization (ERD) patterns associated with right elbow flexion and extension movements, while lifting different weights of loads in the hand. Additionally, we also present a correlation analysis between intention levels detected from EEG and surface electromyogram (sEMG) signals. With a view towards detecting patients� intention levels during rehabilitation therapies, the proposed approach has the potential to ensure active involvement of patients throughout exercise routines and increase the efficacy of robot assisted therapies.
@InProceedings{Koyasb,
booktitle = {IEEE International Workshop on Machine Learning for Signal Processing (MLSP2013)},
author = {Ela Koyas and Elif Hocaoglu and Volkan Patoglu and Mujdat Cetin},
title = {Detection of Intention Level in Response to Task Difficulty from {EEG} Signals},
year = {2013},
abstract = {We present an approach that enables detecting intention levels of patients in response to task difficulty utilizing an electroencephalogram (EEG) based Brain-Computer Interface (BCI). In particular, we use Linear Discriminant Analysis (LDA) to classify event-related synchronization (ERS) and desynchronization (ERD) patterns associated with right elbow flexion and extension movements, while lifting different weights of loads in the hand. Additionally, we also present a correlation analysis between intention levels detected from EEG and surface electromyogram (sEMG) signals. With a view towards detecting patients� intention levels during rehabilitation therapies, the proposed approach has the potential to ensure active involvement of patients throughout exercise routines and increase the efficacy of robot assisted therapies.}
}
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