TY - JOUR
T1 - Decoding Upper Limb Movement Attempt from EEG Measurements of the Contralesional Motor Cortex in Chronic Stroke Patients
AU - Antelis, Javier M.
AU - Montesano, Luis
AU - Ramos-Murguialday, Ander
AU - Birbaumer, Niels
AU - Minguez, Javier
N1 - Publisher Copyright:
© 1964-2012 IEEE.
PY - 2017/1
Y1 - 2017/1
N2 - Goal: Stroke survivors usually require motor rehabilitation therapy as, due to the lesion, they completely or partially loss mobility in the limbs. Brain-computer interface technology offers the possibility of decoding the attempt to move paretic limbs in real time to improve existing motor rehabilitation. However, a major difficulty for the practical application of the BCI to stroke survivors is that the brain rhythms that encode the motor states might be diminished due to the lesion. This study investigates the continuous decoding of natural attempt to move the paralyzed upper limb in stroke survivors from electroencephalographic signals of the unaffected contralesional motor cortex. Results: Experiments were carried out with the aid of six severely affected chronic stroke patients performing/attempting self-selected reaching movements of the unaffected/affected upper limb. The electroencephalographic (EEG) analysis showed significant cortical activation on the uninjured motor cortex when moving the contralateral unaffected arm and in the attempt to move the ipsilateral affected arm. Using this activity, significant continuous decoding of movement was obtained in six out of six participants in movements of the unaffected limb, and in four out of six participants in the attempt to move the affected limb. Conclusion: This study showed that it is possible to construct a decoder of the attempt to move the paretic arm for chronic stroke patients using the EEG activity of the healthy contralesional motor cortex. Significance: This decoding model could provide to stroke survivors with a natural, easy, and intuitive way to achieve control of BCIs or robot-assisted rehabilitation devices.
AB - Goal: Stroke survivors usually require motor rehabilitation therapy as, due to the lesion, they completely or partially loss mobility in the limbs. Brain-computer interface technology offers the possibility of decoding the attempt to move paretic limbs in real time to improve existing motor rehabilitation. However, a major difficulty for the practical application of the BCI to stroke survivors is that the brain rhythms that encode the motor states might be diminished due to the lesion. This study investigates the continuous decoding of natural attempt to move the paralyzed upper limb in stroke survivors from electroencephalographic signals of the unaffected contralesional motor cortex. Results: Experiments were carried out with the aid of six severely affected chronic stroke patients performing/attempting self-selected reaching movements of the unaffected/affected upper limb. The electroencephalographic (EEG) analysis showed significant cortical activation on the uninjured motor cortex when moving the contralateral unaffected arm and in the attempt to move the ipsilateral affected arm. Using this activity, significant continuous decoding of movement was obtained in six out of six participants in movements of the unaffected limb, and in four out of six participants in the attempt to move the affected limb. Conclusion: This study showed that it is possible to construct a decoder of the attempt to move the paretic arm for chronic stroke patients using the EEG activity of the healthy contralesional motor cortex. Significance: This decoding model could provide to stroke survivors with a natural, easy, and intuitive way to achieve control of BCIs or robot-assisted rehabilitation devices.
KW - Brain-computer interface (BCI)
KW - movement decoding
KW - stroke
KW - uninjured motor cortex
UR - https://www.scopus.com/pages/publications/85008426326
U2 - 10.1109/TBME.2016.2541084
DO - 10.1109/TBME.2016.2541084
M3 - Article
C2 - 27046866
AN - SCOPUS:85008426326
SN - 0018-9294
VL - 64
SP - 99
EP - 111
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
IS - 1
M1 - 7440830
ER -