Graz Brain-Computer Interface (BCI) II
ICCHP '94 Proceedings of the 4th international conference on Computers for handicapped persons
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
Neural network design
Digital Signal Processing: A Computer-Based Approach
Digital Signal Processing: A Computer-Based Approach
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Hybrid Taguchi-genetic algorithm for global numerical optimization
IEEE Transactions on Evolutionary Computation
A local neural classifier for the recognition of EEG patterns associated to mental tasks
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Review article: Human scalp EEG processing: Various soft computing approaches
Applied Soft Computing
Clustering technique-based least square support vector machine for EEG signal classification
Computer Methods and Programs in Biomedicine
Grey particle swarm optimization
Applied Soft Computing
Grey-Based particle swarm optimization algorithm
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
Particle swarm optimization with grey evolutionary analysis
Applied Soft Computing
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The brain-computer interface (BCI) is a system that transforms the brain activity of different mental tasks into a control signal. The system provides an augmentative communication method for patients with severe motor disabilities. In this paper, a neural classifier based on improved particle swarm optimization (IPSO) is proposed to classify an electroencephalogram (EEG) of mental tasks for left-hand movement imagination, right-hand movement imagination, and word generation. First, the EEG patterns utilize principle component analysis (PCA) in order to reduce the feature dimensions. Then a three-layer neural network trained using particle swarm optimization is used to realize a classifier. The proposed IPSO method consists of the modified evolutionary direction operator (MEDO) and the traditional particle swarm optimization algorithm (PSO). The proposed MEDO combines the evolutionary direction operator (EDO) and the migration. The MEDO can strengthen the searching global solution. The IPSO algorithm can prevent premature convergence and outperform the other existing methods. Experimental results have shown that our method performs well for the classification of mental tasks from EEG data.