Correlation-based channel selection and regularized feature optimization for MI-based BCI
Multi-channel EEG data are usually necessary for spatial pattern identification in motor
imagery (MI)-based brain computer interfaces (BCIs). To some extent, signals from some …
imagery (MI)-based brain computer interfaces (BCIs). To some extent, signals from some …
Novel hybrid brain–computer interface system based on motor imagery and P300
Motor imagery (MI) is a mental representation of motor behavior and has been widely used
in electroencephalogram based brain–computer interfaces (BCIs). Several studies have …
in electroencephalogram based brain–computer interfaces (BCIs). Several studies have …
Learning common time-frequency-spatial patterns for motor imagery classification
The common spatial patterns (CSP) algorithm is the most popular spatial filtering method
applied to extract electroencephalogram (EEG) features for motor imagery (MI) based brain-…
applied to extract electroencephalogram (EEG) features for motor imagery (MI) based brain-…
A cloud model based fruit fly optimization algorithm
L Wu, C Zuo, H Zhang - Knowledge-Based Systems, 2015 - Elsevier
Fruit Fly Optimization Algorithm (FOA) is a new global optimization algorithm inspired by the
foraging behavior of fruit fly swarm. However, similar to other swarm intelligence based …
foraging behavior of fruit fly swarm. However, similar to other swarm intelligence based …
Cluster decomposing and multi-objective optimization based-ensemble learning framework for motor imagery-based brain–computer interfaces
Objective. Motor imagery (MI) is a mental representation of motor behavior and a widely
used pattern in electroencephalogram (EEG) based brain–computer interface (BCI) systems. …
used pattern in electroencephalogram (EEG) based brain–computer interface (BCI) systems. …
Efficient representations of EEG signals for SSVEP frequency recognition based on deep multiset CCA
Canonical correlation analysis (CCA) has been widely used for frequency recognition in
steady-state visual evoked potential (SSVEP) based brain–computer interfaces (BCIs). However…
steady-state visual evoked potential (SSVEP) based brain–computer interfaces (BCIs). However…
[HTML][HTML] An Improved Adaptive Monte Carlo Localization Algorithm Integrated with a Virtual Motion Model
C Zuo, D Xie, L Wu, X Tang, H Zhang - Sensors, 2025 - mdpi.com
Regarding the issue of high dependency on odometry in the adaptive Monte Carlo localization
(AMCL) algorithm, an improved AMCL algorithm based on the normal distributions …
(AMCL) algorithm, an improved AMCL algorithm based on the normal distributions …
Temporal frequency joint sparse optimization and fuzzy fusion for motor imagery-based brain-computer interfaces
Background Motor imagery (MI) related features are typically extracted from a fixed
frequency band and time window of EEG signal. Meanwhile, the time when the brain activity …
frequency band and time window of EEG signal. Meanwhile, the time when the brain activity …
White blood cell classification network using MobileNetv2 with multiscale feature extraction module and attention mechanism
Y Zou, L Wu, C Zuo, L Chen, B Zhou… - … Signal Processing and …, 2025 - Elsevier
White blood cells play a crucial role in the human immune system. The accurate classification
of white blood cells can help doctors diagnose various diseases for patients. To enhance …
of white blood cells can help doctors diagnose various diseases for patients. To enhance …
An improved fruit fly optimization algorithm with Q-learning for solving distributed permutation flow shop scheduling problems
C Zhao, L Wu, C Zuo, H Zhang - Complex & Intelligent Systems, 2024 - Springer
The distributed permutation flow shop scheduling problem (DPFSP) is one of the hottest
issues in the context of economic globalization. In this paper, a Q-learning enhanced fruit fly …
issues in the context of economic globalization. In this paper, a Q-learning enhanced fruit fly …