From research to clinic: A sensor reduction method for high-density EEG neurofeedback systems.
Clin Neurophysiol
; 130(3): 352-358, 2019 03.
Article
en En
| MEDLINE
| ID: mdl-30669011
ABSTRACT
OBJECTIVE:
To accurately deliver a source-estimated neurofeedback (NF) signal developed on a 128-sensors EEG system on a reduced 32-sensors EEG system.METHODS:
A linearly constrained minimum variance beamformer algorithm was used to select the 64 sensors which contributed most highly to the source signal. Monte Carlo-based sampling was then used to randomly generate a large set of reduced 32-sensors montages from the 64 beamformer-selected sensors. The reduced montages were then tested for their ability to reproduce the 128-sensors NF. The high-performing montages were then pooled and analyzed by a k-means clustering machine learning algorithm to produce an optimized reduced 32-sensors montage.RESULTS:
Nearly 4500 high-performing montages were discovered from the Monte Carlo sampling. After statistically analyzing this pool of high performing montages, a set of refined 32-sensors montages was generated that could reproduce the 128-sensors NF with greater than 80% accuracy for 72% of the test population.CONCLUSION:
Our Monte Carlo reduction method was used to create reliable reduced-sensors montages which could be used to deliver accurate NF in clinical settings.SIGNIFICANCE:
A translational pathway is now available by which high-density EEG-based NF measures can be delivered using clinically accessible low-density EEG systems.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Contexto en salud:
1_ASSA2030
Problema de salud:
1_financiamento_saude
Asunto principal:
Encéfalo
/
Electroencefalografía
/
Neurorretroalimentación
Tipo de estudio:
Health_economic_evaluation
Límite:
Humans
Idioma:
En
Revista:
Clin Neurophysiol
Asunto de la revista:
NEUROLOGIA
/
PSICOFISIOLOGIA
Año:
2019
Tipo del documento:
Article