Identification of Neuromotor Patterns Associated with Hand Rehabilitation Movements from EMG and EEG signals.

Michelle  Soto-Florido; Elkin Garcia-Cifuentes; Angela Maria Iragorri Cucalon; Iván Fernando Mondragón Bernal; Diego Mendez Chaves; Julian  Colorado; Catalina Alvarado Rojas

Identification of Neuromotor Patterns Associated with Hand Rehabilitation Movements from EMG and EEG signals.

Pontificia Universidad Javeriana
School of Medicine

Producción: Contribución a una conferencia › Póster › revisión exhaustiva

Resumen

Stroke is the leading cause of disability worldwide, with approximately 85% of patients experiencing impairment in the upper limb (from shoulder to finger functionality). Because of the high complexity of its movements, and the importance for the activities of daily life, the rehabilitation of the hand, remains a challenge. We propose a method to identify individual hand movements from electroencephalographic (EEG) and electromyographic (EMG) signals, offering a possibility to monitor brain networks associated to motor control that could be used for neuroprosthetics purposes and post-stroke rehabilitation.

Idioma original	Inglés
Estado	Publicada - 16 may. 2025
Evento	Rebah week 2025 - Chicago, Estados Unidos Duración: 12 may. 2025 → 16 may. 2025 https://rehabweek.org/

Conferencia

Conferencia	Rebah week 2025
País/Territorio	Estados Unidos
Ciudad	Chicago
Período	12/05/25 → 16/05/25
Dirección de internet	https://rehabweek.org/

Otros archivos y enlaces

https://rehabweek.org/wp-content/uploads/PP-412.pdf

Identification of Neuromotor Patterns Associated with Hand Rehabilitation Movements from EMG and EEG Signals
Soto-Florido, M., Garcia-Cifuentes, E., Irragorri, A. M., Mondragon, I. F., Mendez, D., Colorado, J. D. & Alvarado-Rojas, C., 2025, p. 1307-1312. 6 p.
Producción: Contribución a una conferencia › Paper › revisión exhaustiva
2 Citas (Scopus)

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title = "Identification of Neuromotor Patterns Associated with Hand Rehabilitation Movements from EMG and EEG signals.",

abstract = "Stroke is the leading cause of disability worldwide, with approximately 85\% of patients experiencing impairment in the upper limb (from shoulder to finger functionality). Because of the high complexity of its movements, and the importance for the activities of daily life, the rehabilitation of the hand, remains a challenge. We propose a method to identify individual hand movements from electroencephalographic (EEG) and electromyographic (EMG) signals, offering a possibility to monitor brain networks associated to motor control that could be used for neuroprosthetics purposes and post-stroke rehabilitation.",

author = "Michelle Soto-Florido and Elkin Garcia-Cifuentes and \{Iragorri Cucalon\}, \{Angela Maria\} and \{Mondrag{\'o}n Bernal\}, \{Iv{\'a}n Fernando\} and \{Mendez Chaves\}, Diego and Julian Colorado and \{Alvarado Rojas\}, Catalina",

year = "2025",

month = may,

day = "16",

language = "English",

note = "Rebah week 2025 ; Conference date: 12-05-2025 Through 16-05-2025",

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Identification of Neuromotor Patterns Associated with Hand Rehabilitation Movements from EMG and EEG signals. / Soto-Florido, Michelle ; Garcia-Cifuentes, Elkin; Iragorri Cucalon, Angela Maria et al.
2025. Sesión de cárteles presentada en Rebah week 2025, Chicago, Illinois, Estados Unidos.

Producción: Contribución a una conferencia › Póster › revisión exhaustiva

TY - CONF

T1 - Identification of Neuromotor Patterns Associated with Hand Rehabilitation Movements from EMG and EEG signals.

AU - Soto-Florido, Michelle

AU - Garcia-Cifuentes, Elkin

AU - Iragorri Cucalon, Angela Maria

AU - Mondragón Bernal, Iván Fernando

AU - Mendez Chaves, Diego

AU - Colorado, Julian

AU - Alvarado Rojas, Catalina

PY - 2025/5/16

Y1 - 2025/5/16

N2 - Stroke is the leading cause of disability worldwide, with approximately 85% of patients experiencing impairment in the upper limb (from shoulder to finger functionality). Because of the high complexity of its movements, and the importance for the activities of daily life, the rehabilitation of the hand, remains a challenge. We propose a method to identify individual hand movements from electroencephalographic (EEG) and electromyographic (EMG) signals, offering a possibility to monitor brain networks associated to motor control that could be used for neuroprosthetics purposes and post-stroke rehabilitation.

AB - Stroke is the leading cause of disability worldwide, with approximately 85% of patients experiencing impairment in the upper limb (from shoulder to finger functionality). Because of the high complexity of its movements, and the importance for the activities of daily life, the rehabilitation of the hand, remains a challenge. We propose a method to identify individual hand movements from electroencephalographic (EEG) and electromyographic (EMG) signals, offering a possibility to monitor brain networks associated to motor control that could be used for neuroprosthetics purposes and post-stroke rehabilitation.

UR - https://rehabweek.org/wp-content/uploads/PP-412.pdf

M3 - Poster

T2 - Rebah week 2025

Y2 - 12 May 2025 through 16 May 2025

ER -

Identification of Neuromotor Patterns Associated with Hand Rehabilitation Movements from EMG and EEG signals.

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Identification of Neuromotor Patterns Associated with Hand Rehabilitation Movements from EMG and EEG Signals

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