Unravelling Causal Relationships Between Cortex and Muscle with Errors-in-variables Models.
Annu Int Conf IEEE Eng Med Biol Soc
; 2021: 967-970, 2021 11.
Article
en En
| MEDLINE
| ID: mdl-34890320
ABSTRACT
Corticomuscular communications are commonly estimated by Granger causality (GC) or directed coherence, with the aim of assessing the linear causal relationship between electroencephalogram (EEG) and electromyogram (EMG) signals. However, conventional GC based on standard linear regression (LR) models may be substantially underestimated in the presence of noise in both EEG and EMG signals some healthy subjects with good motor skills show no significant GC. In this study, errors-in-variables (EIV) models are investigated for the purpose of estimating underlying linear time-invariant systems in the context of GC. The performance of the proposed method is evaluated using both simulated data and neurophysiological recordings, and compared with conventional GC. It is demonstrated that the inferred EIV-based causality offers an advantage over typical LR-based GC when detecting communication between the cortex and periphery using noisy EMG and EEG signals.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Corteza Cerebral
/
Electroencefalografía
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Annu Int Conf IEEE Eng Med Biol Soc
Año:
2021
Tipo del documento:
Article
Pais de publicación:
EEUU
/
ESTADOS UNIDOS
/
ESTADOS UNIDOS DA AMERICA
/
EUA
/
UNITED STATES
/
UNITED STATES OF AMERICA
/
US
/
USA