Cargando…
CNN-Based Personal Identification System Using Resting State Electroencephalography
As a biometric characteristic, electroencephalography (EEG) signals have the advantages of being hard to steal and easy to detect liveness, which attract researchers to study EEG-based personal identification technique. Among different EEG protocols, resting state signals are the most practical opti...
Autores principales: | Fan, Yongdong, Shi, Xiaoyu, Li, Qiong |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8687816/ https://www.ncbi.nlm.nih.gov/pubmed/34938327 http://dx.doi.org/10.1155/2021/1160454 |
Ejemplares similares
-
A Review of Resting-State Electroencephalography Analysis in Disorders of Consciousness
por: Bai, Yang, et al.
Publicado: (2017) -
Electroencephalography resting‐state networks in people with Stroke
por: Snyder, Dylan B., et al.
Publicado: (2021) -
Reliability of Resting-State Microstate Features in Electroencephalography
por: Khanna, Arjun, et al.
Publicado: (2014) -
Arrangements of Resting State Electroencephalography as the Input to Convolutional Neural Network for Biometric Identification
por: Lai, Chi Qin, et al.
Publicado: (2019) -
DE-CNN: An Improved Identity Recognition Algorithm Based on the Emotional Electroencephalography
por: Wang, Yingdong, et al.
Publicado: (2020)