Cargando…
Effective Connectivity for Decoding Electroencephalographic Motor Imagery Using a Probabilistic Neural Network
Motor imagery (MI)-based brain–computer interfaces have gained much attention in the last few years. They provide the ability to control external devices, such as prosthetic arms and wheelchairs, by using brain activities. Several researchers have reported the inter-communication of multiple brain r...
Autores principales: | Awais, Muhammad Ahsan, Yusoff, Mohd Zuki, Khan, Danish M., Yahya, Norashikin, Kamel, Nidal, Ebrahim, Mansoor |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8512774/ https://www.ncbi.nlm.nih.gov/pubmed/34640888 http://dx.doi.org/10.3390/s21196570 |
Ejemplares similares
-
A large electroencephalographic motor imagery dataset for electroencephalographic brain computer interfaces
por: Kaya, Murat, et al.
Publicado: (2018) -
Dimensionality Reduction and Channel Selection of Motor Imagery Electroencephalographic Data
por: Naeem, Muhammad, et al.
Publicado: (2009) -
Electroencephalographic Functional Connectivity With the Tacit Learning System Prosthetic Hand: A Case Series Using Motor Imagery
por: Iwatsuki, Katsuyuki, et al.
Publicado: (2020) -
Quaternion-Based Signal Analysis for Motor Imagery Classification from Electroencephalographic Signals
por: Batres-Mendoza, Patricia, et al.
Publicado: (2016) -
A Densely Connected Multi-Branch 3D Convolutional Neural Network for Motor Imagery EEG Decoding
por: Liu, Tianjun, et al.
Publicado: (2021)