Data stream classification based on extreme learning machine: a review
X Zheng, P Li, X Wu - Big Data Research, 2022 - Elsevier
Many daily applications are generating massive amount of data in the form of stream at an
ever higher speed, such as medical data, clicking stream, internet record and banking …
ever higher speed, such as medical data, clicking stream, internet record and banking …
Deep learning in EEG: Advance of the last ten-year critical period
Deep learning has achieved excellent performance in a wide range of domains, especially
in speech recognition and computer vision. Relatively less work has been done for …
in speech recognition and computer vision. Relatively less work has been done for …
Functional brain network classification for Alzheimer's disease detection with deep features and extreme learning machine
The human brain can be inherently modeled as a brain network, where nodes denote
billions of neurons and edges denote massive connections between neurons. Analysis on …
billions of neurons and edges denote massive connections between neurons. Analysis on …
A review of improved extreme learning machine methods for data stream classification
L Li, R Sun, S Cai, K Zhao, Q Zhang - Multimedia Tools and Applications, 2019 - Springer
Classification is a hotspot in data stream mining and has gained increasing interest from
various research fields. Compared with traditional data stream classification methods …
various research fields. Compared with traditional data stream classification methods …
Radar emitter recognition based on SIFT position and scale features
S Liu, X Yan, P Li, X Hao… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
As the complexity of the battlefield electromagnetic environment has increased, it has
become challenging to identify radiation sources using traditional radar emitter identification …
become challenging to identify radiation sources using traditional radar emitter identification …
Early classification of motor tasks using dynamic functional connectivity graphs from EEG
Objective. Classification of electroencephalography (EEG) signals with high accuracy using
short recording intervals has been a challenging problem in developing brain computer …
short recording intervals has been a challenging problem in developing brain computer …
A deep neural network with subdomain adaptation for motor imagery brain-computer interface
M Zheng, B Yang - Medical Engineering & Physics, 2021 - Elsevier
Background The nonstationarity problem of EEG is very serious, especially for spontaneous
signals, which leads to the poor effect of machine learning related to spontaneous signals …
signals, which leads to the poor effect of machine learning related to spontaneous signals …
Deep weighted extreme learning machine
The imbalanced data classification attracts increasing attention in the past years due to the
continuous expansion of data available in many areas, such as biomedical engineering …
continuous expansion of data available in many areas, such as biomedical engineering …
Brain-computer interface with corrupted EEG data: a tensor completion approach
One of the current issues in brain-computer interface (BCI) is how to deal with noisy
electroencephalography (EEG) measurements organized as multidimensional datasets …
electroencephalography (EEG) measurements organized as multidimensional datasets …
Improving classification of slow cortical potential signals for BCI systems with polynomial fitting and voting support vector machine
HR Hou, QH Meng, M Zeng… - IEEE Signal Processing …, 2017 - ieeexplore.ieee.org
Classification of slow cortical potential (SCP) signals is crucial for brain-computer interface
(BCI) systems. This letter presents a new scheme to improve the classification performance …
(BCI) systems. This letter presents a new scheme to improve the classification performance …