A decomposition-based hybrid ensemble CNN framework for driver fatigue recognition
Electroencephalogram (EEG) has become increasingly popular in driver fatigue monitoring systems. Several decomposition methods have been attempted to analyze the EEG signals that are complex, nonlinear and non-stationary and improve the EEG decoding performance in different applications. However,...
Saved in:
Main Authors: | Li, Ruilin, Gao, Ruobin, Suganthan, Ponnuthurai Nagaratnam |
---|---|
Other Authors: | School of Electrical and Electronic Engineering |
Format: | Article |
Language: | English |
Published: |
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/170827 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
An enhanced ensemble deep random vector functional link network for driver fatigue recognition
by: Li, Ruilin, et al.
Published: (2024) -
A spectral-ensemble deep random vector functional link network for passive brain–computer interface
by: Li, Ruilin, et al.
Published: (2024) -
Analysis of schizophrenic EEG synchrony using empirical mode decomposition
by: Ziqiang, Z., et al.
Published: (2014) -
Significant wave height forecasting using hybrid ensemble deep randomized networks with neurons pruning
by: Gao, Ruobin, et al.
Published: (2023) -
Ensemble incremental learning Random Vector Functional Link network for short-term electric load forecasting
by: Qiu, Xueheng, et al.
Published: (2020)