Decoding Upper Limb Movement Attempt From EEG Measurements of the Contralesional Motor Cortex in Chronic Stroke Patients

Autores

Antelis, J; Montesano, L; Ramos-Murguialday, A; Birbaumer, N; Minguez, J

Revista

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING

Año: 2017 Volumen: 64 (1) Páginas: 99-111

Editor:

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

DOI:

10.1109/TBME.2016.2541084

Resumen

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 tomove 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. Con-clusion: 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.

Palabras clave

Brain-computer ; interface ; (BCI) ; movement ; decoding ; stroke ; uninjured ; motor ; cortex

Afiliación

Antelis, JM (Reprint Author), Tecnol Monterrey, Campus Guadalajara, Zapopan 45201, Mexico.
Antelis, Javier M., Tecnol Monterrey, Campus Guadalajara, Zapopan 45201, Mexico.
Montesano, Luis, Inst Invest Ingn Aragon I3A, Zaragoza, Spain.
Ramos-Murguialday, Ander; Birbaumer, Niels, Eberhard Karls Univ Tubingen, Inst Med Psychol & Behav Neurobiol, Tubingen, Germany.
Ramos-Murguialday, Ander, TECNALIA, Hlth Technol, Derio, Spain.
Birbaumer, Niels, Ist Ricovero & Cura Carattere Sci, Rome, Italy.
Minguez, Javier, Inst Invest Ingn Aragon, Zaragoza, Spain.
Minguez, Javier, Bit&Brain Technol SL, Zaragoza, Spain.